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29-1 If 100 random samples of size n were drawn

79-1 find in the table

79-2 percentile of students' distribution

80-1 "students' distribution, 90 percent of the area"

83-1 T-distributions are spread out

84-2 A t-distribution with 30 d.f. is most similar

86-1 a true statement regarding the comparison of t-distributions

87-2 A t-distribution is used in estimating MU when

88-1 Suppose that we repeatedly draw a random sample

88-2 Obtain the values of the t-distribution

89-1 the value of Student's t that subdivides

92-1 standard normal distribution and a student's distribution are alike

96-1 Since percentiles of the t-distribution approximate those of

148-1 When (for what level of confidence) do we use Z =

148-2 (Z-score) corresponds to the sample mean

154-2 the 95% confidence interval for the appropriate z-score is

155-1 on which test was his performance better

156-1 then P(70.5 < XBAR < 71.3) is approximately

161-1 what is the probability that the mean competency

161-2 The middle 99% points for the distribution of the sample mean

162-1 P(WBAR > 195)

163-1 what are the middle 90%

175-1 Find (approximately) P[XBAR>2]

193-1 the students' personal libraries at this

201-1 whether or not the manufacturing process is operating satisfactorily

215-2 average (mean) height of 9 randomly selected setters

732-1 "Which of the following describes a ""statistical inference""?"

765-2 Standard error of a mean

777-1 MU

1342-1 A pocket calculator enthusiast claims that 80% of all incoming freshmen

1394-1 Establish a 90% confidence interval for the mean speed of cars

1398-1 is 10. The standard error of the mean is

1399-2 "As the sample size (n) increases, the mean of a random sample"

1399-3 In general the sampling distribution of the mean and the distribution

1400-2 Suppose that all possible distinct samples of size n > 30

1401-1 Suppose that all possible distinct samples of size n > 1

1418-1 90% confidence limits corresponding to a

1418-2 the lower confidence limit for a two sided

1419-1 t distribution is usually used instead of the standard normal

1420-2 90% confidence interval for the true percentage of children

1421-1 "MU, which of the following is most precise:"

1421-2 An approximate 95% confidence interval

1425-3 "Other things being equal, the larger the confidence coefficient"

1426-1 An investigation reveals that a confidence interval with

1428-1 percentages of fat in 1 pound packages

1429-1 Construct a 99% confidence interval

1429-2 "1, 2, 3, 4, 5. Construct a 95% symmetric confidence"

1430-2 What kind of table whould you use

1431-2 Determine 95% confidence limits corresponding to a

1433-1 The value you would find in the table to complete the information

1433-2 A 95% confidence interval for the mean

1434-1 interval for the population proportion favoring Senator Claghorn is:

1434-2 "t-statistic, student A uses a confidence coefficient of"

1435-1 What will be the effects of changes

1435-2 "To three decimals, what is the upper limit of"

1438-1 The size of a confidence interval for a mean is affected by

1439-1 The 93% confidence interval for MU is closest to:

1440-1 Consider the following table which gives the deodorants preferred

1441-2 then a 99% confidence interval for p is given by

1443-1 What is the probability that XBAR differs from MU by more than 1?

1443-2 A 95% confidence interval for MU is:

1445-1 The mean weight of a random sample of 16 dogs on the quad

1446-1 "XBAR = 31 and SUM(i = 1, 400)((X(i) - 31)**2) = 1596"

1447-1 "XBAR = 20 and SUM(i = 1, 16)((X(i) - 20)**2) = 960"

1448-2 Eighty percent of a sample of 400 people support candidate B.

1449-1 "8, 11, 9, 17, 12, 15 are a sample of size 6"

1454-1 found that the mean cost (i.e. XBAR) of hospital care for

1455-1 by adding to and subtracting from the sample mean a certain multiple

1455-2 A 90% confidence interval

1456-1 "Given that 99% confidence limits for MU are 42 and 58, "

1457-1 Suppose a psychologist wishes to know the mean IQ of students

1459-1 an approximate 95% confidence interval for MU?

1460-2 Find the 0.95 confidence interval estimate

1461-2 "A parameter is fixed (non-fluctuating), a confidence interval is"

1463-1 contained within approximately how many of these intervals?

1463-2 The sample mean of 225 scores on a math test is 75.

1464-1 the probability of MU falling within a confidence interval

1469-2 Is 113 an acceptable value for MU at a 95% confidence level?

1470-1 The Chamber of Commerce in Miami Beach wishes to estimate

1471-1 estimate the proportion of people in a population who

1471-2 Set up a 90% confidence interval for the area of the plot.

1472-1 John has done an experiment on gallons of water per second

1472-2 500 accounts receivable is selected

1473-1 A survey on consumer finances reports that 33 per cent

1473-3 Use a confidence level of 90%.

1474-2 confidence interval for the proportion of males

1478-1 nine patients on this new diet had observed cholesterol

1478-2 interval for the actual percentage of television viewers

1480-1 voters in Portsmouth indicate that 60%

1481-1 confidence interval for the true mean arm reach of tournament fighters.

1481-2 "Estimate the proportion of ""poverty"" families"

1482-1 Interpret the statistical meaning of this confidence interval.

1483-1 Would your interval have been narrower if SIGMA would have been

1484-1 respond to him based on part a above

1485-1 "their bills for the evening were [$12.50, $10.75, $14.28]"

1486-1 from 0 (no parenting skills deemed successful) to 100

1487-1 A sociologist conducted a study of assertion by

1488-1 "A sample of six male delinquents, aged 16, indicates"

1491-2 Explain why the confidence interval in (b) is approximate.

1495-1 Make a confidence interval statement concerning the proportion of

1499-3 Would you forecast a win for candidate A? Why?

1500-1 Which of the following confidence limits would appear most favorable

1501-1 determine if these limits are consistent with

1509-1 "If (5,8) is a 95% confidence interval for a MU, then the probability"

1509-2 A confidence interval for MU will generally be smaller if

1510-1 As the size of a confidence coefficient (one minus ALPHA)

1510-2 The sample mean lies at the center of the confidence interval for MU.

1511-2 A 95% confidence interval is twice as long as a 90% confidence

1513-3 With any sample size a high level of confidence in an interval

1522-2 What would be the midpoint of an interval estimate of p?

1523-1 the width of the interval can be narrowed by

1547-1 The confidence coefficient is the probability that an unknown parameter

1547-3 If confidence intervals are computed from repeated samples of the same

1548-2 MARGIN OF ERROR

1551-2 The purpose of using a sample and calculating a mean is to

1566-1 will approximate the normal curve if

1566-3 "If we think of how all possible XBAR's are distributed, the"

1568-1 The Central Limit Theorem tells us that:

1568-2 "As the size of the sample, n, increases towards the size of the popu"

1569-1 The Central Limit Theorem

1570-1 The sampling distribution of means of random samples of size n drawn

1572-3 10 hours using the Central Limit Theorem.

1576-1 "According to the Central Limit Theorem, how does a sampling distribu"

1577-1 Define what is meant by the sampling distribution of XBAR for size

1578-1 What is the approximate distribution of Z?

1580-2 The Central Limit Theorem is of most value when we sample from

1581-1 "As the sample size increases, the distribution of the sample mean"

1581-2 The Central Limit Theorem applies to the case of sampling from

1582-2 "the same size, drawn from a severely skewed population, will equal"

1583-2 "According to the Central Limit Theorem, the shape of "

1584-2 "The central limit theorem tells us that, if we take a large sample,"

1585-2 The sampling distribution of XBAR is approximately normal if and only

1691-1 "Other things being equal, a low level of confidence is desirable."

1730-1 ________________ is the process of drawing conclusions about population

1791-3 A proportion is a special case of a mean when you have

1815-1 "variance 36. For samples of size 9, the sampling distribution"

1820-2 "If the population distribution of scores (X) is normally distributed,"

1857-4 Suppose we have sampled (with replacement) from a finite population.

1858-3 A larger mean implies a larger standard deviation.

1859-1 non-normal population with SIGMA**2 = 1 and MU = 0 would have mean

1865-1 "The variance of the sample mean, SIGMA(XBAR)**2, is computed"

1869-1 "Does this imply that one is ""above average"" and the other is ""below"

1869-2 Give an example illustrating that the standard error of a sample

1891-2 Explain how SIGMA differs from SIGMA(XBAR).

1898-1 "For a large sample size, the estimated standard error of the mean is"

1898-2 The standard deviation of the original observations is generally

1901-1 Distributions of population statistics have standard deviations

1905-2 estimator of the variance of the population from which

1927-1 VAR(XBAR) = __________. (sometimes denoted (SIGMA(XBAR))**2).

1928-1 "If the variance of the mean S(XBAR)**2 = 5 and n = 5, what is S**2?"

1951-3 The distribution of XBAR will have a variance equal to the

1959-1 If the random variable X has a normal distribution with mean MU and

1960-2 "In a population with MU = 10 and SIGMA**2 = 64, the standard error"

1962-1 "MU = 5 and SIGMA = 20, the probability that the mean"

1963-2 If independent samples of size 6 are drawn over and over again and

1964-1 The standard error of the mean is another name for the standard

1964-3 "SIGMA(XBAR) changes in what way when n,"

1966-1 S(XBAR) is:

1966-3 "SIGMA(XBAR), is customarily called the:"

1967-1 Find the standard error of the mean for

1968-1 How could you estimate the value of the standard error

1968-2 "as before, but using samples of size 9n"

1969-1 The standard error of the mean

1969-2 Which of the following best describes the standard error of YBAR?

1971-1 Which investigator is apt to obtain a better estimate of the corres

1977-4 "As the sample size increases, the standard error of the mean remains"

1978-1 The standard deviation of the random sampling distribution of the mean

1978-3 "If the sample size is greater than one, the sampling distribution"

1979-3 The standard error of the sample mean increases with the sample

1981-1 The formula SIGMA(XBAR) = SIGMA/SQRT(n) requires the population

2074-2 The standard error of the median is an index of

2074-4 it must be a _________________ distribution.

2103-1 Let MU(X) and SIGMA(X)**2 be the mean and the variance of a

2857-1 A sampling distribution:

2859-1 Which is NOT a characteristic of a (random) sampling distribution

2860-3 We __________ have a complete sampling distribution displayed for us.

2862-4 How does a sampling distribution differ from the distribution of

2866-1 if the sample size is increased nine-fold

2867-3 A sampling distribution could be considered a population.

2889-1 If a population is very large an especially large sample is usually

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Q: Which one of the following statements is correct? a. If L and U are the lower and upper limits of a 99% confidence interval for MU, then MU varies between L and U with a probability of .99. b. If for a sample of 100 students from the registrar's office it was found that 95% of these students had dean's list averages, then random sampling from the entire student body could not possibly have been performed. c. Assuming the population variance is known, then if the sample size is doubled, the variance of the distribution of the sample mean of a variable would be halved. d. If 100 random samples of size n were drawn and, if, for each, a 99% confidence interval for MU was computed, then exactly 99 of these confidence intervals would contain within their limits the true population mean. e. For continuous variables the probabilities P(a < x < b) and P(a <= x <= b) are always the same, whereas for discrete variables these probabilities are always different.

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Q: A sample of size 16 is taken from a normal population. Then a 99% confidence interval is set up with XBAR = 30 and s = 20. The value you would find in the table to complete the information necessary to obtain the interval would be: (a) 2.602 (d) 2.947 (b) 2.326 (e) 2.921 (c) 2.576 (f) none of these.

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Q: The first percentile of students' distribution with 24 degrees of freedom is: (1) -2.80 (4) 2.49 (2) -2.50 (5) 2.50 (3) -2.49

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Q: For students' distribution, 90 percent of the area lies between t = -1.89 and t = 1.89 if the degrees of freedom are: (1) 2 (4) 7 (2) 3 (5) 8 (3) 6

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Q: A large normally distributed population has mean 16. Consider all samples of size 7. The numbers (XBAR - 16)/(S/SQRT(7)) are: (a) normally distributed (b) t distributed with 7 degrees of freedom (c) t distributed with 6 degrees of freedom (d) neither t nor normal

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Q: T-distributions are spread out __________(more or less) than a normal distribution with MU = 0, SIGMA = 1.

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Q: A t-distribution with 30 d.f. is most similar to a _____ distribution. a. normal distribution with mean = 1 and variance = 1 b. normal distribution with mean = 0 and variance = SIGMA**2 c. normal distribution with mean = 0 and variance = 29 d. normal distribution with mean = 0 and variance = 1

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Q: Which of the following is a true statement regarding the comparison of t-distributions to the standard normal distribution? a. The normal distribution is symmetrical whereas the t-distributions are slightly skewed. b. The proportion of area beyond a specific value of t is less than the proportion of area beyond the corresponding value of z. c. The greater the df, the more the t-distributions resemble the standard normal distribution. d. All of the above. e. None of the above.

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Q: A t-distribution is used in estimating MU when __________ is unknown but its use assumes that the sample data ____________________________.

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Q: Suppose that we repeatedly draw a random sample from a normally distributed population with a known mean and calculate a value for student's t for each sample. a. We will calculate t = (sample mean - _____) / _____ b. If each sample consists of 7 elements, the t distribution generation will have _____ degrees of freedom. c. Over a large number of trials _____% of the values generated will be greater than 1.440.

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Q: Obtain the values of the t-distribution with 8 degrees of freedom that subdivide the area under the curve so that 5% is to the left of the smaller value and 5% is to the right of the larger value. Sketch the curve of this t-distribution and indicate the areas involved.

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Q: What is the value of Student's t that subdivides the area under any Student's t curve so that 50% of the area lies to the right of that value? What is the corresponding value for a standard normal distribution?

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Q: 1) State two ways in which a standard normal distribution and a student's distribution are alike. 2) State at least one way in which they differ.

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Q: True or False? If False, correct it. Since percentiles of the t-distribution approximate those of the normal distribution if n is greater than 30, this is usually considered a good sample size to use.

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Q: When (for what level of confidence) do we use Z = 1.645, for a two-sided test or confidence interval? a. 90% b. 95% c. 80% d. 100% e. 99%

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Q: A random sample of size 25 is taken from a population with mean 7 and variance 4. The sample mean is calculated to be 8. What value of the standard normal random variable (Z-score) corresponds to the sample mean? a. 25 b. 1.25 c. -1.25 d. +2.5 e. none of the above

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Q: Consider the following data: 1, 7, 3, 3, 6, 4 Assuming this data is drawn from a normal population with mean = MU and variance = 6, the 95% confidence interval for the appropriate z-score is _____ standard units long. a) 5.14 b) 4.90 c) 4.58 d) 4.04 e) 3.92

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Q: The class average on the first midterm was 81 with a standard deviation of 9. The class average on the second midterm was 78 with a standard deviation of 12. If a student got a 93 on each test, on which test was his performance better relative to the class? A. First midterm B. Second midterm

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Q: The height of male college freshmen has a normal distribution with mean 71 inches and standard deviation 3 inches. If 100 male college freshmen are selected at random, and XBAR is the average of their heights, then P(70.5 < XBAR < 71.3) is approximately: a) .4525 b) .6732 c) .7938 d) .8413 e) none of these

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Q: Suppose that for a sample of 36 Family Nurse Practitioners (FNP's) from several similar type hospital clinics, a competency score ranging from 0 to 100 was derived based on performance at the clinic. Suppose further that the population mean competency score for all FNP's was 80 and the population variance was 100. For the sample of 36 FNP's, what is the probability that the mean competency score will be between 75 and 80? a. .4987 b. .1915 c. .5013 d. .2287 e. .5115

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Q: Suppose that for a sample of 36 Family Nurse Practitioners (FNP's) from several similar type hospital clinics, a competency score ranging from 0 to 100 was derived based on performance at the clinic. Suppose further that the population mean competency score for all FNP's was 80 and the population variance was 100. The middle 99% points for the distribution of the sample mean competency score described above is (rounded to two decimal places): a. (54.24, 105.76) b. (76.12, 83.88) c. (56.74, 103.26) d. (75.71, 84.29) e. None of these

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Q: Suppose that the weight (W) of male patients registered at a diet clinic has the normal distribution with mean 190 and variance 100. For a sample of size 25 from the clinic, which of the following statements is equivalent to the statement: P(WBAR > 195) where WBAR denotes the mean weight of the sample? a. P(Z < -2.5) d. P(Z > 2.5) b. P(Z < 1) e. P(Z < 2.5) c. P(Z > -1) (Note: Z is a standard normal random variable.)

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Q: Suppose that the weight (W) of male patients registered at a diet clinic has the normal distribution with mean 190 and variance 100. For a sample of size 25 from the clinic, what are the middle 90% points of the distribution of WBAR where WBAR denotes the mean weight of the sample? a. (186.08, 193.92) d. (186.71, 193.29) b. (173.55, 206.45) e. None of these c. (170.40, 209.6)

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Q: A random sample of size 100 is selected from a population with MU=0 and SIGMA=20. Find (approximately) P[XBAR>2]. a. .0228 b. .4207 c. .3085 d. .1587 e. None of the above.

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Q: Suppose the distribution of the students' personal libraries at this university can be approximated by a normal distribution with mean equal to 18.7 and variance equal to 1.08. If a random sample of 27 students is polled, what is the probability that the average size of their lib- raries will be at least 19.3 books?

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Q: A company manufactures cylinders that have a mean 2 inches in diameter. The standard deviation of the diameters of the cylinders is .10 inches. The diameters of a sample of 4 cylinders are measured every hour. The sample mean is used to decide whether or not the manufacturing process is operating satisfactorily. The following decision rule is applied: If the mean diameter for the sample of 4 cylinders is equal to 2.15 inches or more, or equal to 1.85 inches or less, stop the process. If the mean diameter is more than 1.85 inches and less then 2.15 inches, leave the process alone. a. What is the probability of stopping the process if the process average MU, remains at 2.00 inches? b. What is the probability of stopping the process if the process mean were to shift to MU = 2.10 inches? c. What is the probability of leaving the process alone if the process mean were to shift to MU = 2.15 inches? To MU = 2.30 inches?

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Q: Suppose that height of English setter dogs is normally distributed with a mean of 30 inches and a known variance of 9. What is the probability that the average (mean) height of 9 randomly selected setters will be greater than 31 inches?

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Q: Which of the following describes a "statistical inference"? a. A true statement about a population made by measuring some sample of that population. b. A conjecture about a population made by measuring some sample of that population. c. A true statement about a sample made by measuring some population. d. A conjecture about a sample made by measuring some population. e. A true statement about a sample made by measuring the entire population.

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Q: Define the following term and give an example of its use. Your example should not be one given in class or in a handout. Standard error of a mean

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Q: Define the following term and give an example of its use. Your example should not be one given in class or in a handout. MU

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Q: A pocket calculator enthusiast claims that 80% of all incoming freshmen own a pocket calculator. To investigate this claim a random sample of 200 incoming freshmen have been interviewed. Of these 120 own calculators. a. Set 90% confidence limits for P1, the true proportion having calculators. (Use ALPHA = .10.) b. Test the enthusiast's claim.

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Q: A random sample of 64 cars passing a check point on a certain highway showed a mean speed of 60.0 mph. The standard deviation of speeds is known to be 15.0 mph. a. Give a point estimate of the population mean speed on this highway. b. Establish a 90% confidence interval for the mean speed of cars on this highway. Interpret the meaning of this interval.

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Q: A sample of 3600 cases is drawn at random from an infinitely large population. The standard deviation of the population is 10. The standard error of the mean is a. 2/15. b. 1/6. c. 4/5. d. 10. e. none of the above.

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Q: True or False? If false, correct it. As the sample size (n) increases, the mean of a random sample is less likely to be near the mean of the population.

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Q: True or False? If false, correct it. In general the sampling distribution of the mean and the distribution of the parent population have exactly the same shape.

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Q: Suppose we have sampled (with replacement) from a finite population. Suppose that all possible distinct samples of size n > 30 have been selected and that the mean and variance have been computed for each sample. Suppose here, that Variance = SUM((X - XBAR)**2/(n - 1)). True or False? If False, correct it. The distribution of XBAR will be symmetric.

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Q: Suppose we have sampled with replacement from a finite population. Suppose that all possible distinct samples of size n (where n > 1) have been selected and that the mean has been computed for each sample. True or false? If false, correct it. The shape of the distribution of XBAR will be the same as the shape of the population.

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Q: A sample of twenty-five observations is taken from a normal popu- lation with variance 9. 90% confidence limits corresponding to a sample mean of 30 are best represented by: (a) 30 +/- 9.00 (b) 30 +/- .79 (c) 30 +/- 1.03 (d) 30 +/- .47 (e) 30 +/- .99

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Q: Suppose that a sample of size 16 from a normal distribution with mean MU yielded a sample mean of 3.2 and a standard deviation of 4. For a 98% confidence level, the lower confidence limit for a two sided in- terval for MU is: (1) 0.6 (4) 2.555 (2) 0.62 (5) 5.8 (3) 2.5

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Q: To find confidence intervals for the mean of a normal distribution, the t distribution is usually used instead of the standard normal distribu- tion because: (1) the mean of the population is not known (2) the t distribution is more efficient (3) the variance of the population is usually not known (4) the standard error of the estimate is S/SQRT(n) (5) the sample mean is known.

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Q: Out of 50 children drawn at random from a large population of school children, all but 10 showed need of dental treatment. Based on normal approximation, a 90% confidence interval for the true percentage of children in that population who need dental treatment is: (a) (10.7%, 29.3%) (d) (65.4%, 94.6%) (b) (77.1%, 82.9%) (e) None of these. (c) (70.7%, 89.3%)

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Q: In repeated constructions of 95% confidence intervals for a population mean, MU, which of the following is most precise: a. MU falls in the interval approximately 95 times out of a 100. b. the interval brackets the unknown MU approximately 95 times out of a 100. c. 95 out of a 100 populations will have their means in the interval. d. XBAR falls in the interval approximately 95 times out of a 100. e. The interval brackets XBAR approximately 95 times out of a 100.

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Q: Suppose in a random sample of 9 men, the mean height is found to be 70 inches, and suppose it is known that the population standard deviation is 3 inches. An approximate 95% confidence interval for the population mean height is: a. 64 to 76 inches d. 69 to 71 inches b. 67 to 73 inches e. none of these c. 68 to 72 inches

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Q: True or False? If False, correct it. Other things being equal, the larger the confidence coefficient (i.e. 1 - ALPHA), the smaller the confidence interval.

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Q: True or False? If False, correct it. An investigation reveals that a confidence interval with confidence coefficient .95 for the mean extends from 11.2 to 17.5. This means that in about 95 percent of all samples drawn by the same method, the sample means will fall between 11.2 and 17.5.

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Q: Suppose we have a population of percentages of fat in 1 pound packages of hamburger. Suppose these percentages are normally distributed with mean 28 and standard deviation 4. The probability that the mean per- centage of a sample of 16 packages is between 26.5 and 29.5 ounces is closest to: a. .433 b. .134 c. .866 d. .933 e. .067

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Q: The mean of a sample of 100 items, drawn from an infinite population with variance 400, is XBAR = 5. Construct a 99% confidence interval for MU, the mean of the population. a. -.05 to 9.25 b. 2 to 7 c. -0.30 to 10.20 d. -0.5 to 11.2 e. -0.15 to 10.15

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Q: A sample of size 5 from a normal population consists of the numbers 1, 2, 3, 4, 5. Construct a 95% symmetric confidence interval for the mean of the population. a. 2.00 - 4.00 b. 1.04 - 4.96 c. 0.00 - 6.00 d. 1.52 - 4.48 e. 1.85 - 4.15 f. 1.90 - 4.10 g. 0.50 - 5.50 h. 0.95 - 5.05

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Q: In a survey study on the incidence of depression in a population of psychiatric hospital administrators, scores on a depression measure were obtained from 86 respondents. The observed mean score was 62, with a standard deviation of 16. What kind of table whould you use in constructing a 99% confidence interval for the mean depression score? a. Chi square b. Kendall's tau c. binomial probabilities d. student's t e. other (specify):

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Q: A sample of twenty-five observations is taken from a normal population with variance 4. Determine 95% confidence limits corresponding to a sample mean of 20. (a) 20 +/- .4 (c) 20 +/- .32 (b) 20 +/- 8 (d) 20 +/- .8

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Q: A sample of size 26 is taken from a normal population. Then a 95% confidence interval is set up with XBAR = 30 and s = 20. The value you would find in the table to complete the information necessary to obtain the interval would be: a. 2.064 b. 2.060 c. 1.711 d. 1.708 e. 1.960

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Q: Nine men with a genetic condition that causes obesity entered a weight reduction program. After four months the statistics of weight loss were XBAR = 11.2, S = 9.0. A 95% confidence interval for the mean of the population of which this is a sample (assuming normality and randomness) is: a. 11.2 +/- 1.96(3) b. 11.2 +/- 1.86(3) c. 11.2 +/- 2.262(3) d. 11.2 +/- 2.306(3) e. 11.2 +/- 2.306(9)/SQRT(8)

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Q: A political pollster is hired to estimate the proportion of voters in favor of Senator Claghorn. He takes a sample of 400 and finds 56% of the voters favor the Senator. A 95% confidence interval for the true population proportion favoring Senator Claghorn is: (a) .56 +/- 1.96SQRT((.56)(.44)) (b) .44 +/- 1.96SQRT((.56)(.44)/400) (c) .56 +/- 1.96SQRT((.44)(.56)/400) (d) .56 +/- 1.64SQRT((.56)(.44)/400) (e) none of the above.

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Q: In finding confidence intervals for the mean of a normal population by using a t-statistic, student A uses a confidence coefficient of 0.95 while student B uses 0.99. Which one of the following statements is true about the length of the confidence intervals found by A and B? (The length of the confidence interval is the upper limit minus the lower limit). (1) B's interval will always be smaller than A's interval (2) B's interval will usually be smaller than A's interval (3) B's interval will always be larger than A's interval (4) B's interval will usually be larger than A's interval (5) There is no way of knowing which of the intervals will be larger.

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Q: For a certain normally distributed population, the value of the standard deviation is known, but the value of the mean is unknown. What will be the effects of changes in sample size and in the confidence coefficient on the length of the confidence interval estimate of the population mean? a. Increasing sample size increases the length, given a fixed coefficient. b. Increasing the confidence coefficient decreases the length given a fixed sample size. c. Increasing sample size decreases the length, given a fixed coefficient. d. None of the above

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Q: A random sample of 625 boxes taken from the output of a box making machine was inspected for flaws. It was found that 500 of the boxes were free from flaws. To three decimals, what is the upper limit of the 0.99 confidence interval estimate of the proportion of acceptable boxes being produced? a. .8 + 1.96*SQRT(.16/625) b. .8 + 2.576*(.16/625) c. .8 + 1.96*(.16/625) d. .8 + 2.576*SQRT(.16/625)

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Q: The size of a confidence interval for a mean is affected by changes in which of the following? a. The size of the sample b. The confidence coefficient c. The variance of the sample d. b and c only e. a, b, and c

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Q: The strength of elevator cables are to be measured. Let X = strength of a cable, and assume X is normal with mean MU and variance SIGMA**2, both unknown. A sample of 89 straps is taken, with results XBAR = 31 and S**2 = 89. The 93% confidence interval for MU is closest to: (a) 31 +/- (2.11)*SQRT(89) (b) 31 +/- (1.60)*(1.0) (c) 31 +/- (1/SQRT(89)) (d) 31 +/- 1.81 (e) 31 +/- (1.80)*(89/SQRT(89))

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Q: Consider the following table which gives the deodorants preferred by samples of lacrosse and soccer players. Brand X Brand Y ------------------ lacrosse players | 20 | 30 -------------------------------------- soccer players | 10 | 40 -------------------------------------- 1. Now let PI(1) be the proportion of all lacrosse players who prefer Brand X. An 80% confidence interval for PI(1) would use a table value closest to: A. 1.28 B. 1.10 C. 1.50 D. 1.70 E. 1.90 2. An 80% confidence interval for PI(1) would then be closest to: A. (1/5) +/- (1.28)(SQRT((4/25)(1/100))) B. (2/5) +/- (1.28)(SQRT((6/25)(1/50))) C. (2/3) +/- (1.1)(SQRT((2/9)(1/30))) D. (1/5) +/- (1.5)(SQRT((4/25)(1/50))) E. (2/5) +/- (1.7)(SQRT((6/25)(1/100)))

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Q: A poll of 100 students revealed that 50 were in favor of returning to the semester system. If p is the proportion of all U.C.D. stu- dents in favor of semesters, then a 99% confidence interval for p is given by (a) .5 +/- .098 (d) .5 +/- .135 (b) .5 +/- .112 (e) .5 +/- .141 (c) .5 +/- .129

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Q: A sample of size 36 is taken from a population with unknown mean MU and standard deviation SIGMA = 3. What is the probability that XBAR differs from MU by more than 1? (a) .3413 (b) .9544 (c) .0228 (d) .4772 (e) .0456

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Q: A sample of size 36 is taken from a population with unknown mean MU and standard deviation SIGMA = 3. A 95% confidence interval for MU is: (a) XBAR +/- 1.96(1/2) (d) MU +/- 1.64(XBAR) (b) XBAR +/- 1.96 (e) MU +/- 1.96(SIGMA) (c) XBAR +/- 1.64(1/4)

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Q: The mean weight of a random sample of 16 dogs on the quad is 58 lbs. with a sample standard deviation of 8 lbs. A 95% confidence interval for the mean weight of dogs on the quad is: a) 58 +/- (1.96*2) b) 58 +/- (2.13*2) c) 58 +/- (1.96*4) d) 58 +/- (1.75*2) e) 58 +/- (2.33*4)

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Q: A sample of size 400 is taken from a large population, yielding XBAR = 31 and SUM(i = 1, 400)((X(i) - 31)**2) = 1596 The 95% confidence interval for the mean is: (a) [30.88, 31.12] (d) [30.84, 31.16] (b) [30.80, 31.19] (e) [30.76, 31.24] (c) [30.75, 31.25]

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Q: A sample of size 16 is taken from a normally distributed population, yielding: XBAR = 20 and SUM(i = 1, 16)((X(i) - 20)**2) = 960 The 95% confidence interval for the mean of this population is: (a) [15.8, 24.3] (d) [17.1, 22.9] (b) [15.6, 24.4] (e) [16.4, 23.6] (c) [16.2, 23.8]

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Q: Eighty percent of a sample of 400 people support candidate B. The 95% confidence interval for the proportion of people who support B is nearest: A. [.768, .832] B. [.761, .839] C. [.775, .825] D. [.790, .810]

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Q: If the observations: 8, 11, 9, 17, 12, 15 are a sample of size 6 from a normal population with a mean = MU and a variance = 24, then a 95% confidence interval for MU has confidence limits a. 12 +/- 3.92 b. 11.5 +/- 3.92 c. 12 +/- 5.142 d. 11.5 +/- 5.142 e. none of these

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Q: Suppose for a random sample of 16 hospitals in North Carolina, it was found that the mean cost (i.e. XBAR) of hospital care for recovery from acute myocardial infarction (MI) was $15000 with a sample standard deviation of $1200. Then a 99% confidence interval for the mean cost of recovery for all hospitals in North Carolina would have the following limits (rounded to nearest whole numbers): a. (14116, 15884) b. (14123,15876) c. (14225, 15775) d. (14227, 15773) e. none of these

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Q: The limits of a 95% confidence interval for the mean MU of some population, whose variance SIGMA**2 is assumed known, are found by adding to and subtracting from the sample mean a certain multiple of the standard error. For a sample size of 36, the multiplier described in the previous sentence is: a. 1.645 b. 1.6896 c. 2.0301 d. 1.96 e. None of these

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Q: Suppose that a sample of sixteen heights yielded a sample mean of 75.1 inches and a sample variance of 9.0. A 90% confidence interval (rounded to two decimal places) for the true mean height of the population has which of the following limits: a. (73.79, 76.41) d. (71.16, 79.04) b. (73.87, 76.33) e. None of these c. (74.09, 76.11)

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Q: Given that 99% confidence limits for MU are 42 and 58, which of of the following could be 95% confidence limits? a. 43 and 57 b. 41 and 59 c. 41 and 57

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Q: Suppose a psychologist wishes to know the mean IQ of students in a given high school. 25 students were sampled and the sample mean was 103. The sample standard deviation was 12. Which of the following is a 90% confidence interval for the mean IQ for all students? a. 98.9 - 107.1 b. 97.6 - 108.3 c. 98.0 - 108.0 d. 94.5 - 111.5 e. none of these

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Q: In a sample of size 400 we found XBAR=10 and S**2=100. Which of the following is an approximate 95% confidence interval for MU? a. (.2, 19.8) b. (-9.8, 9.8) c. (8.04, 11.96) d. (9.02, 10.98) e. (-.98, .98)

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Q: A sample of size 25 was taken from a normal population whose standard deviation SIGMA is believed to be 20. The sample data produced a mean XBAR of 57.5 Find the 0.95 confidence interval estimate for the popu- lation mean MU. a) 18.3 to 96.7 b) 49.66 to 65.34 c) 50.9 to 64.1 d) 53.5 to 61.5 e) 55.93 to 59.07

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Q: A parameter is fixed (non-fluctuating), a confidence interval is a. also fixed. b. equal to + or - one standard deviation. c. variable from sample to sample. d. either fixed or variable depending on sample size. e. None of the above.

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Q: We know the mean MU of a population. Suppose 1,000 samples of size n are drawn from this population. For each sample we compute a 90% confidence interval for MU. We would expect the mean of the population would NOT be contained within approximately how many of these intervals? a. 0 b. 10 c. 100 d. 900 e. Impossible to tell.

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Q: The sample mean of 225 scores on a math test is 75. Find the 95 percent confidence interval for the mean of the population, assuming that SIGMA(X) = 7. a. 72.5 - 77.5 b. 74.1 - 75.9 c. 73.2 - 76.8 d. 73.8 - 76.2 e. None of the above.

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Q: In a 95% confidence interval for the mean, a. if many samples are drawn, YBAR will fall within the confidence interval 95% of the time. b. the probability of YBAR falling within a confidence interval computed from one sample is .95. c. the probability of MU falling within a confidence interval compu- ted on one sample is .95. d. if many samples are drawn, the computed confidence intervals will contain MU 95% of the time.

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Q: The following values of "Y" represent a random sample from some population -- 115, 110, 112, 113, 111, 107, 110, 106, 112. (a) Construct a 95% confidence interval for the population mean. (b) Is 113 an acceptable value for MU at a 95% confidence level? (c) Construct a 99% confidence interval for the population mean.

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Q: The Chamber of Commerce in Miami Beach wishes to estimate the mean expenditures per tourist per visit in that city. A random sample of one-hundred tourists has been selected for investigation, and it has been found that the mean expenditures of the sample was $800.00 per tourist per visit. It is known that the standard deviation of expenditures for all tourists is $120.00. Construct a 95 percent confidence interval for the true mean expendi- ture per tourist. Interpret your result.

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Q: It is wanted to estimate the proportion of people in a population who have English ancestors. Make a 95% interval estimate assuming that a random sample of 100 people has 21 of English descent in it.

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Q: A plot of land is surveyed by 25 student surveyors with the following results: YBAR = 7.25 acres [SUM(i=1,25)(Y(i)-YBAR)**2]/[25] = .01660 Set up a 90% confidence interval for the area of the plot.

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Q: John has done an experiment on gallons of water per second that flow in a sewer main in the city. He makes 16 measurements of this flow and finds that their average is 100 and their variance is 9. Find a 98% confidence interval for the mean flow.

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Q: A random sample of 500 accounts receivable is selected from the 4,032 accounts that a firm has, and the sample mean is found to be $242.30. The sample standard deviation is computed to be $3.20. Set up a .99 confidence interval estimate of the population mean. How do you interpret the meaning of this interval?

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Q: A survey on consumer finances reports that 33 per cent of a sample of 2,600 spending units expected good times during the next 12 months. Assume that a simple random sample was used in the study. Set up a .95 confidence interval estimate of the population proportion of spending units expecting good times.

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Q: Compute a confidence interval for the mean, given XBAR = 24, n = 25, and SIGMA(XBAR) = 2. (Use a confidence level of 90%.)

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Q: Out of the random sample of 360 full time students on the River Campus, 225 were male and 135 were females. Obtain a 99% con- fidence interval for the proportion of males among the full time student body.

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Q: A new diet for the reduction of cholesterol is introduced. In order to test this procedure, nine patients on this new diet had observed choles- terol levels of: patient cholesterol patient cholesterol 1 240 6 220 2 290 7 190 3 220 8 230 4 250 9 200 5 260 XBAR = 210 S**2 = SUM(([X(i) - 210]**2)/8) = 950 S = 30.9 Assume cholesterol levels are normally distributed. Construct a 99% confidence interval for MU with XBAR = 210.

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Q: In a random sample of 200 television viewers in a certain area, 95 had seen a certain controversial program. Construct a 0.99 confi- dence interval for the actual percentage of television viewers in that area who saw the program.

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Q: The results of an imaginary random sample of 30 registered voters in Portsmouth indicate that 60% of the voters favor Henry Kissinger for president. Set 95% confidence limits for this sample result.

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Q: A sample of size 9 is used to estimate the mean arm reach of fighters in a certain tournament. Arm reach (inches) for all fighters in the tournament is known to be normally distributed with a variance 4 inches squared. If the sample mean is 35 inches, establish a 95% confidence interval for the true mean arm reach of tournament fighters.

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Q: A random sample of 100 families in a large city showed 20 with annual earnings that placed them in a "poverty" category. Estimate the pro- portion of "poverty" families in the city using a 90% confidence interval.

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Q: A light bulb manufacturer desires to determine the mean life time of the light bulbs in the most recent batch produced. Available are 25 light bulbs to be tested. For these bulbs, the average lifetime is 72 hours (i.e. XBAR = 72). From previous experience it is known that each batch produced bulbs according to a normal distribution with standard deviation of 15 hours. a. Construct a 99% confidence interval for the mean MU of the batch. b. Interpret the statistical meaning of this confidence interval.

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Q: a) Find a 95% confidence interval for MU from a sample of 9 where XBAR = 12 and s = 1, and interpret the interval. b) Would your interval have been narrower if SIGMA would have been known to be 1?

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Q: Taking a random sample from its very extensive files, a water company finds that the amount owed in 16 delinquent accounts have a mean of $16.35 and a standard deviation of $4.56. a. Use these values to construct a .98 confidence interval for the average amount owed on all delinquent accounts. b. If Mr. Blackwater, the company president, claims the delinquent accounts have a population mean of $19.01, how could you quickly respond to him based on part a above (also after explaining that you were using a 2% ALPHA level)?

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Q: You are interested in the amount of discretionary funds available to male patrons of a disco. A random sample of three patrons indicates that their bills for the evening were [$12.50, $10.75, $14.28]. Esti- mate with 90% confidence the mean amount spent by patrons at this disco.

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Q: A researcher assigns each of his interviewers a list of 7 families, drawn randomly from a region, to be interviewed. Each interviewer is instructed to administer a successful parenting scale SPS to each parent in his sample. The SPS scores, X(i), are defined as ranging from 0 (no parenting skills deemed successful) to 100 (successful parenting skills consistently and skillfully applied). The first interviewer returned with the following scores for his seven female respondents (i.e., mothers). Based on this sample, estimate the mean SPS for females in Region I with 90% confidence: [X(i)] = [75, 62, 48, 50, 55, 62, 69] S = 9.856

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Q: A sociologist conducted a study of assertion by having one of her top students, after appropriate training, note the number of assertive acts performed in a day by each of 10 randomly selected coeds, producing the following sample of data, in acts per day: [5,3,10,6,4,9,5,5,7,5]. Estimate with 90% confidence the mean number of assertive acts per day performed by the coeds. SUM(X(i)) = 59 XBAR = 5.9 S = 2.18

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Q: A sample of six male delinquents, aged 16, indicates that they have the following number of delinquent acts recorded on their police record: [5, 3, 3, 4, 3, 5]. Estimate, with 90% confidence, the mean number of such acts recorded on the records of the universe of subjects (i.e., 16 year-old delinquent males in Gotham City). SUM(X(i)) = 23 S = 0.983

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Q: In a random sample of 100 flashlight batteries, the average useful life was 22 hours and the sample standard deviation was 5 hours. (a) Estimate MU, the average life of all batteries of the type sampled. (b) Determine an approximate 99% confidence interval for MU. (c) Explain why the confidence interval in (b) is approximate.

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Q: In a recent survey of Democrats in the Congress, 30% favored Senator Kennedy of Massachusetts as the party's top presidential candidate at this time, while the remaining 70% was divided up among numerous other potential candidates. The results were reported accurate to within +/- 10% with 95% confidence. Make a confidence interval statement concerning the proportion of Democrats in the Congress who preferred Senator Kennedy at the time of the survey.

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Q: A TV network reports that their survey based on 200 randomly selected voters indicates: .60 favor candidate A .40 favor candidate B Margin of Error (95%) for the difference = .14. Would you forecast a win for candidate A? Why?

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Q: Suppose that you are interested in an election and the latest poll indicates: Your candidate: 36% of the vote Leading opponent: 40% of the vote Suppose also that the poll indicates no uncommitted voters. Which of the following confidence limits would appear most favorable to your candidate? Why? a. difference = 4% +/- 1%, 1 - ALPHA = .99 b. difference = 4% +/- 10%, 1 - ALPHA = .99 c. difference = 4% +/- 10%, 1 - ALPHA = .80

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Q: A brewery producing beer has a number of specifications for quality. Among these standards is the requirement that the degree of hop like flavor should be a value of 8.0. The production of the brewery consists of a large number of batches. It's possible for differences to arise between batches, so we will regard each batch as a different population. We will consider the hoppiness of each batch as a normally distributed variable with mean and variance unknown. From each batch you can remove 6 samples for hoppiness. For each batch you are to: a. set confidence limits for the batch (population) mean, MU; b. determine if these limits are consistent with the require- ment that hoppiness is a value of 8.0. 1. Outline the procedure to be followed in setting confidence limits where the probability of the interval calculated including MU is: a. 90% b. 99% 2. Apply the procedure outlined to this set of sample values: 13, 11, 9, 14, 8, 11. Is this sample data consistent with the speci- fication of hoppiness = 8.0 when the probability level used is: a. 90% b. 99% 3. Do these results suggest any weakness in the procedure used? If so, what?

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Q: True or false? If false, explain why. If (5,8) is a 95% confidence interval for a MU, then the probability that MU lies in the interval is .95.

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Q: True or false? If false, correct it. A confidence interval for MU will generally be smaller if a confidence coefficient equals .90 than if this coefficient equals .95.

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Q: True or False? Explain. As the size of a confidence coefficient (one minus ALPHA) is increased, the width of the corresponding confidence interval will tend to increase.

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Q: True or False? If False, correct it. The sample mean lies at the center of the confidence interval for MU.

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Q: True or False? If False, correct it. A 95% confidence interval is twice as long as a 90% confidence interval.

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Q: True or false? If false, explain why. With any sample size a high level of confidence in an interval estimate may be obtained.

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Q: We are interested in the proportion (p) of voters in a community that would favor a school issue to be settled by a referendum. A random sample of size 225 from the community indicated that 65% were in favor. What would be the midpoint of an interval estimate of p? a. .03 b. 1.96 c. p d. Insufficient information to obtain correct result. e. Sufficient information but correct result is not given.

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Q: In interval estimation of a population mean, the width of the interval can be narrowed by a. increasing n. b. lessening the confidence level (e.g., .99 to .90). c. reducing the magnitude of SIGMA(X). d. all of the above. e. none of the above.

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Q: True or False? If false, correct it. The confidence coefficient is the probability that an unknown parameter has a certain value.

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Q: True or False? If False, correct it. If confidence intervals are computed from repeated samples of the same size, in the long run they will cover the unknown parameter in the same percentage of cases as the confidence coefficient.

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Q: Define the following term and give an example of its use. Your example should not be one given in class or in a handout. MARGIN OF ERROR

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Q: The purpose of using a sample and calculating a mean is to a. find the average for the sample b. determine the dispersion of the sample c. estimate the mean of the population d. estimate sample size

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Q: The distribution of means of all possible samples of the same size (n) drawn from a population will approximate the normal curve if a. the n is large enough b. the population is large c. the population is symmetrical d. the mean of each sample equals the mean of the population e. none of the above is correct.

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Q: If we think of how all possible XBAR's are distributed, the mean and variance of those observations are a. MU(X) and SIGMA(X) b. zero and SIGMA(X) c. MU(X) and ((SIGMA(X))**2)/SQRT(n) d. MU(X) and ((SIGMA(X))**2)/n e. none of these.

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Q: The Central Limit Theorem tells us that: a) the shape of all sampling distributions of sample means are nor- mally distributed. b) the mean of the distribution of sample means is less than the mean of the parent population. c) the standard deviation of the distribution of sample means is the same as the standard deviation of the population. d) all of the above are true. e) none of the above are true.

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Q: As the size of the sample, n, increases towards the size of the popu- lation, the value of the standard error of the mean, SIGMA(XBAR), approaches: a) zero b) one c) SIGMA d) SIGMA/SQRT(n) e) None of the above are correct.

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Q: The Central Limit Theorem a. says that YBAR approaches MU(Y) as sample size increases. b. says that s(Y) approaches SIGMA(Y) as sample size increases. c. says that both a and b will occur. d. refers to a matter other than those stated above.

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Q: The sampling distribution of means of random samples of size n drawn from some population will approach normality a. only if the parent population is normally distributed and if n is large. b. only if the parent population is normally distributed regardless of the value of n. c. if n is large regardless of the shape of the parent population distribution. d. regardless of the value of n and regardless of the shape of the parent population distribution.

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Q: The mean of a random sample of size n = 36 is used to estimate the sample mean of a very large population of means which has standard deviation SIGMA = 25 hours. Find the probability that the sample mean will be "off" either way by less than 10 hours using the Central Limit Theorem.

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Q: According to the Central Limit Theorem, how does a sampling distribu- tion of means change as sample size increases? Of what significance is this change (are these changes) in telling you how accurately a sample mean will estimate a population mean?

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Q: The scientific rationale for statistical inferences about population means relies on a set of three propositions which we have called the Central Limit Theorem. These propositions give us information about, respectively, the mean, standard deviation, and shape of a sampling distribution of XBAR. A. Define what is meant by the sampling distribution of XBAR for size 25 samples. B. According to the Central Limit Theorem, what relationship will necessarily exist between the mean of this sampling distribution and the mean of the population? C. According to the Central Limit Theorem, what relationship will necessarily exist between the standard error of the mean (SIGMA(XBAR)) and the standard deviation of the population? D. Based on your answer in C, explain how a confidence interval estimate of MU will be affected by: 1) an increase in the size of the sample selected 2) an increase in the variability of the population from which the sample is selected.

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Q: If X(1), X(2),...X(n) are n(n > 30) independent and identically distributed random variables and Z = (XBAR - MU(XBAR))/SQRT(VAR(XBAR)) a.) What is the approximate distribution of Z? b.) MU(Z) = _________________________. c.) VAR(Z) = _______________________. d.) Why is the central limit theorem of fundamental importance to statistics?

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Q: True or false? If false, explain why. The Central Limit Theorem is of most value when we sample from a normal distribution.

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Q: True or False? If False, correct it. As the sample size increases, the distribution of the sample mean approaches a normal distribution.

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Q: True or False? If False, correct it. The Central Limit Theorem applies to the case of sampling from a normal distribution as well as other cases.

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Q: True or False? If False, correct it. The mean of the distribution of means of all possible random samples of the same size, drawn from a severely skewed population, will equal the population mean.

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Q: Determine if the following is true or false and explain why. According to the Central Limit Theorem, the shape of the sampling distribution of XBAR (given that n = 30) will be normal, whether or not the shape of the population is normal.

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Q: True or False? If false, explain why. The central limit theorem tells us that, if we take a large sample, the sample values will follow an approximate normal distribution.

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Q: True or False? If False, correct it. The sampling distribution of XBAR is approximately normal if and only if the population is normally distributed.

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Q: True or False? If False, correct it. Other things being equal, a low level of confidence is desirable.

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Q: ________________ is the process of drawing conclusions about population characteristics from the facts given by a sample. It is generalization from the specific to the general.

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Q: True or false? A proportion is a special case of a mean when you have a dichotomous population.

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Q: Consider simple random sampling from a normal distribution with mean 18 and variance 36. For samples of size 9, the sampling distribution for the sample mean has mean and variance equal to: a. 2 and 12 respectively b. 18 and 4 respectively c. 18 and 12 respectively d. 2 and 4 respectively e. none of these

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Q: If the population distribution of scores (X) is normally distributed, then the sampling distribution of XBAR will be distributed a. normally only if n is large. b. normally for any given sample size if the sample is randomly selected. c. normally if XBAR is large. d. Not enough information is given to determine the characteristics of the distribution. e. There is sufficient information but the correct answer is not given.

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Q: Suppose we have sampled (with replacement) from a finite population. Suppose that all possible distinct samples of size n (where n > 1) have been selected and that the mean and variance have been computed for each sample. (Suppose here, that the variance = (SUM((X-XBAR)**2))/(n-1).) True or false? If false, correct it. The distribution of XBAR will have a mean equal to MU.

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Q: True or False? If False, correct it. A larger mean implies a larger standard deviation.

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Q: True or False? If False, correct it. The means of two hundred random samples each of size 4 from a non-normal population with SIGMA**2 = 1 and MU = 0 would have mean approximately 0 and standard deviation approximately .5.

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Q: A population is such that MU = 10, and SIGMA(X)**2 = 9. A simple random sample of size 10 is taken and XBAR is computed. The variance of the sample mean, SIGMA(XBAR)**2, is computed to be: (a) 9 (b) .9 (c) .6 (d) .36

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Q: You are the parent of two children that have been given an IQ test that purports to be a sampling of the intellect. One child receives a score of 99 and the other receives 101. You know, of course, that the IQ is based on 100 as average or 'normal'. Does this imply that one is "above average" and the other is "below average"? Explain.

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Q: Give an example illustrating that the standard error of a sample difference ought to be reported along with the sample difference.

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Q: Explain how SIGMA differs from SIGMA(XBAR).

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Q: True or False? If False correct it. For a large sample size, the estimated standard error of the mean is generally larger than the sample standard deviation obtained from a random sample of the same size.

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Q: True or False? If False, correct it. The standard deviation of the original observations is generally larger than the standard deviation of all possible sample means.

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Q: True or false? If false, explain why. Distributions of population statistics have standard deviations while distributions of sample statistics have standard errors.

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Q: What is the value of the usual (unbiased) estimator of the variance of the population from which the random sample composed of the values 5, 10, and 3 came? a. 85.33 b. 13.00 c. 8.67 d. 6.00 e. none of the above

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Q: Let X be a random variable with the following probability distribution: P(X = 0) = .4 P(X = 1) = .3 P(X = 2) = .2 P(X = 3) = .1 A sample of 5 values is taken from a population with the above probability distribution. The sample is: X(1) = 0 X(2) = 1 X(3) = 0 X(4) = 3 X(5) = 2 VAR(XBAR) = __________. (sometimes denoted (SIGMA(XBAR))**2). Estimated variance of XBAR, ((S(XBAR))**2) = __________.

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Q: A. If the sample standard deviation S = 3 and n = 3, what is S(XBAR)**2? B. If the variance of the mean S(XBAR)**2 = 5 and n = 5, what is S**2?

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Q: Suppose we have sampled (with replacement) from a finite population. Suppose that all possible distinct samples of size n (where n > 1) have been selected and that the mean has been computed for each sample. True or false? If false, correct it. The distribution of XBAR will have a variance equal to the population variance.

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Q: If the random variable X has a normal distribution with mean MU and variance SIGMA**2, which one of the following statements gives the most accurate information about the sampling distribution of the sample mean XBAR? (1) XBAR is often close to SIGMA regardless of the sample size. (2) As the sample size decreases, XBAR is closer to MU. (3) As the sample size decreases, the variability of the distri- bution of XBAR about MU increases. (4) The errors (XBAR - MU) are both positive and negative regard- less of the sample size. (5) XBAR becomes less than SIGMA**2 as the sample size increases.

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Q: In a population with MU = 10 and SIGMA**2 = 64, the standard error of XBAR for a sample size of 16 is: a. -1.25 b. 0.5 c. 2 d. 4 e. 16

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Q: If random samples of size 25 are drawn from a normal population for which MU = 5 and SIGMA = 20, the probability that the mean of a ran- dom sample will be less than zero is: (1) .1056 (4) .3944 (2) .2119 (5) .8944 (3) .2881

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Q: Suppose Y is a continuous random variable (now assumed to be normally distributed) with MU = 138 and SIGMA**2 = 126. If independent samples of size 6 are drawn over and over again and YBAR is calculated for each sample, then: a. the mean of YBAR is 138 and the standard deviation of YBAR is 21. b. the mean of YBAR is 138 and the standard deviation of YBAR is square root of 21. c. the mean of YBAR is 21. d. the variance of YBAR is 21. e. b and d.

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Q: The standard error of the mean is another name for the standard deviation of: a. a sample b. a population c. the sampling distribution of any statistic d. the sampling distribution of the mean e. none of the above

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Q: SIGMA(XBAR) changes in what way when n, the sample size, increases? a. it increases b. it stays the same c. it decreases d. it is 0 e. it is 1

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Q: S(XBAR) is: a. the standard error of the mean b. the standard deviation of the sampling distribution of XBAR c. an estimate of the standard error of the mean d. the standard deviation of the sampling distribution of X e. none of these

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Q: The standard deviation for the sampling distribution of sample means, SIGMA(XBAR), is customarily called the: a) coefficient of variation for sample means. b) sampling error for means. c) standard deviation for the mean. d) standard error of the mean. e) all of the above are correct.

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Q: Find the standard error of the mean for a distribution where samples of size 36 are taken from a population with a mean MU = 100 and a standard deviation SIGMA = 18. a) 0.5 b) 2.0 c) 3.0 d) 18.0 e) None of the above are correct.

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Q: A sample of 100 cases is drawn, and values of YBAR and s(Y) are computed on the sample. The population mean and standard deviation are unknown. How could you estimate the value of the standard error of the mean, SIGMA(YBAR), in this situation? a. Use [SUM(Y(i)**2)]/[n-1] b. Use [SIGMA(Y)**2]/[SQRT(n-1)] c. Use [s(Y)]/[SQRT(n)] d. None of the above. e. Impossible to obtain an estimate from such data.

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Q: A "large number" of random samples, of n cases each, is drawn from a certain population of scores. The mean of each sample was computed and the "large number" of means thus obtained was organized into a frequency distribution. The standard deviation of this distribution was determined. The whole process was then repeated with the same population as before, but using samples of size 9n, i.e., 9 times as large as before. How will the standard deviation of this second distribution of means compare with that of the first distribution? a. It will be one-ninth as large. b. It will be one-third as large. c. Since "large numbers" are involved, it will be the same. d. It will be three times as large. e. It will be nine times as large.

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Q: The standard error of the mean a. is theoretically determined. b. behaves just like a standard deviation. c. is affected by sample size. d. is characterized by all of the above.

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Q: Which of the following best describes the standard error of YBAR? a. [SIGMA(Y)**2]/[n] b. [SIGMA(Y)]/[n] c. SQRT([SIGMA(Y)]/[n]) d. [SIGMA(Y)]/[SQRT(n)]

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Q: Investigator X conducted a survey in which he randomly selected and weighed 1000 persons in a population of 1 million to estimate mean weight. Investigator B conducted a similar survey except that he obtained data on 500 persons in a population of 10,000. Which investigator is apt to obtain a better estimate of the corres- ponding population mean? Why?

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Q: True or False? If False, correct it. As the sample size increases, the standard error of the mean remains unchanged.

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Q: True or False? If false, correct it. The standard deviation of the random sampling distribution of the mean is equal to the population standard deviation.

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Q: True or False? If False, correct it. If the sample size is greater than one, the sampling distribution of the mean will always have a variance which is larger than the variance of the associated parent population.

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Q: True or False? If False, correct it. The standard error of the sample mean increases with the sample size.

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Q: True or False? If False, correct it. The formula SIGMA(XBAR) = SIGMA/SQRT(n) requires the population to be normally distributed.

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Q: The standard error of the median is an index of a. the variability of a sampling distribution of S(y)'s. b. the variability of a sampling distribution of medians. c. the central tendency of a sampling distribution of S(y)'s. d. the central tendency of a sampling distribution of medians. e. none of the above.

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Q: If the appropriate measure of variability for a distribution is the standard error it must be a _________________ distribution.

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Q: Let MU(X) and SIGMA(X)**2 be the mean and the variance of a population. If MU(XBAR) and SIGMA(XBAR)**2 are the mean and variance of the sampling distribution of XBAR, then: a. MU(X) > MU(XBAR) and SIGMA(X)**2 >= SIGMA(XBAR)**2 b. MU(X) = MU(XBAR) and SIGMA(X)**2 >= SIGMA(XBAR)**2 c. SIGMA(X)**2 >= SIGMA(XBAR)**2; one cannot predict the relationship between MU(X) and MU(XBAR) d. MU(X) < MU(XBAR) and SIGMA(X)**2 <= SIGMA(XBAR)**2 e. none of these

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Q: A sampling distribution: a) is a distribution of all the various sample statistics that can be found for one sample. b) of the mean is a distribution of the means taken from all possible samples of a given size n that could be taken from the population. c) of any statistic has an approximately normal distribution. d) is a histogram showing the distribution of the sample. e) all of the above are correct.

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Q: Which is NOT a characteristic of a (random) sampling distribution of means? a. Its mean is the same as the mean of the population of scores. b. Its standard deviation is greater than that of the population of scores. c. It tends to resemble the normal distribution irrespective of the shape of the population of scores with sufficient n. d. Its standard deviation changes with variation in sample size.

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Q: We __________ have a complete sampling distribution displayed for us. a. always b. frequently c. seldom d. never

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Q: How does a sampling distribution differ from the distribution of a sample?

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Q: True or False? If False, correct it. Assuming random sampling, if the sample size is increased nine- fold, then the standard deviation of the sample mean is reduced by one third.

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Q: True or false? A sampling distribution could be considered a population.

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Q: True or False? Explain your answer. If a population is very large an especially large sample is usually taken or needed.

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A: c. Assuming the population variance is known, then if the sample size is doubled, the variance of the distribution of the sample mean of a variable would be halved.

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A: (d) 2.947 df = n - 1 = 16 - 1 = 15 t(ALPHA = .005, df = 15) = 2.947

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A: (3) -2.49.

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A: (4) 7 t value from t table at 7 df = 1.90.

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A: (c) (t distributed with 6 degrees of freedom)

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A: T-distributions are spread out MORE than a normal distribution with MU = 0, SIGMA = 1.

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A: d. normal distribution with mean = 0 and variance = 1.

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A: c. The greater the df, the more the t-distributions resemble the standard normal distribution.

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A: A t-distribution is used in estimating MU when SIGMA is unknown but its use assumes that the sample data COMES FROM A NORMAL DISTRIBUTION.

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A: a. MU s/SQRT(n) b. 6 c. 10

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A: t = -1.86 and +1.86 If available, consult file of graphs and diagrams that could not be computerized for appropriate sketch.

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A: Zero Zero

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A: 1) Both center at zero and are symmetric. 2) The student t has more area in the tails. The variance of the standard normal is 1 while the variance of the student t depends on the degrees of freedom and is in general greater than 1.

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A: True

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A: a. 90%

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A: d. +2.5 Sample Mean = XBAR E(XBAR) = 7 = MU VAR(XBAR) = SIGMA**2/n = 4/25 SIGMA(XBAR) = 2/5 Then Z = (XBAR - MU)/SIGMA(XBAR) = (8 - 7)/(2/5) = 2.5

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A: e) 3.92 Z(ALPHA = .05/2) = 1.96 2 * 1.96 = 3.92

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A: A. First midterm Z = (X - MU)/SIGMA Z(1) = (93 - 81)/9 Z(2) = (93 - 78)/12 = 12/9 = 15/12 = 1.33 = 1.25 Since Z(1) > Z(2), the student did better on the first midterm relative to the class.

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A: c) .7938 P(70.5 < XBAR < 71.3) = P([(70.5-71)/.3] < Z < [(71.3-71)/.3]) = P((-.5/.3) < Z < (.3/.3)) = P(-1.67 < Z < 1) = .4525 + .3413 = .7938

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A: a. .4987 stderr = 10/6 = SS below P(75 < XBAR < 80) = P[(75-80)/SS < Z < (80-80)/SS] = P(-3 < Z < 0) = .4987

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A: d. (75.71, 84.29) 99% C.I. = MU +/- (Z*S(XBAR)) = 80 +/- (2.575*(10/6)) = from 75.71 to 84.29

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A: d. P(Z > 2.5) Z = (195 - 190)/[SQRT((100)/25)] = 2.5

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A: d. (186.71, 193.29) Z = 1.645 SIGMA(XBAR) = SQRT(100/25) = 2 190 +/- (2)(1.645)

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A: d. .1587 P[XBAR>2] = P[Z > [[2-0]/[20/SQRT(100)]]] = P[Z > [[2]/[2]]] = P[Z > 1] = .1587

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A: MU = 18.7 SIGMA**2 = 1.08 SIGMA = 1.04 SIGMA(XBAR) = SIGMA/SQRT(n) = 1.04/SQRT(27) = .200 P(X >= 19.3) = P(Z >= (19.3 - 18.7)/.200) = P(Z >= 3) = .0013

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A: a. Z = (1.85 - 2.00)/(.10/SQRT(4)) or (2.15 - 2.00)/(.10/SQRT(4)) = -.15/.05 or = +.15/.05 = -3 = +3 P(Z<-3 or Z>+3) = .0013 + .0013 = .0026 b. Z = (1.85 - 2.10)/.05 or (2.15 - 2.10)/.05 = -2.5/.05 .05/.05 = -5 1 P(Z<-5 or Z>1) = .00000 + .1587 = .1587 c. Using MU = 2.15: Z = (1.85 - 2.15)/.05 or (2.15 - 2.15)/.05 = -.30/.05 0/.05 = -6 0 P(-6Back to review this question

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A: Z = (XBAR - MU)/(SIGMA/SQRT(n)) = (31 - 30)/(3/SQRT(9)) = 1/(3/3) = 1 Area beyond Z = .1587. Therefore, the probability that the average of a random sample of 9 setters will exceed 31 inches is 0.1587 or 15.87%.Back to review this question

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A: b. A conjecture about a population made by measuring some sample of that population.Back to review this question

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A: Definition: A measure of variation among values for sample means for a particular sample size. The usual method used to calculate an estimate of the standard error of a mean for sample size n is to obtain an estimate of the population variance from the sample elements (S**2), convert it to an estimated variance of a mean for the desired sample size (Divide S**2 by n), and take the square root. Notice that the same estimated popula- tion variance can be used to calculate standard errors of a mean for several different sample sizes. If the population variance is known, the process is the same but you are no longer estimating. Example: Suppose that a random sample of size 10 has provided an estimated population variance of 25 with 9 df. That esti- mated variance provides the basis for calculating A: Estimated standard error of a mean for a sample size of 4 = 2.5 B: Estimated standard error of a mean for a sample size of 16 = 1.25 C: Estimated standard error of a mean for a sample size of 25 = 1.00 Symbols: S(YBAR), S(XBAR), SIGMA(YBAR), SIGMA(XBAR),...Back to review this question

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A: Definition: A Greek letter usually used to indicate the population mean. Example: Suppose that we are concerned with the population of coins carried by each student in this class on a certain day. MU, the population mean = .78, would be the arithmatic average of the amounts carried by all students in the class that day. Symbol: MUBack to review this question

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A: Using ALPHA = .10, Z = 1.645 S = SQRT(pq/n) = SQRT((.6)(.4)/200) = .0346 a. C.I. = .6 +/- 1.645*.0346 C.I. = .6 +/- .057 C.I. = .543 to .657 at 90% confidence b. Since .8 is not included in our confidence interval we must reject his claim at the 10% significance level.Back to review this question

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A: a. 60.0 miles per hour b. C.I. = XBAR +/- [Z * SIGMA(XBAR)] = 60.0 +/- [1.645 * (15/SQRT(64))] = 60.0 +/- [3.08] = from 56.92 to 63.08 This means that the method used for arriving at this interval will produce intervals containing the mean 90% of the time. This parti- cular interval could be one of those, or it could be one of the 5% that fail to include the mean.Back to review this question

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A: b. 1/6. Standard Error of Mean = Standard Deviation/SQRT(n) = 10/SQRT(3600) = 1/6Back to review this question

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A: False - change "less" to "more".Back to review this question

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A: False - in general, the sampling distribution of the mean and the distribution of the parent population have different shapes.Back to review this question

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A: True, the central limit theorem implies that the sampling distribution of the mean will approach normality for large samples, even if the original population distribution is not normal.Back to review this question

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A: False. The Central Limit Theorem states that the sampling distribution of the mean will be normal in shape for large samples, even if the original population distribution is not.Back to review this question

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A: (e) 30 +/- .99 Since SIGMA = 3, SQRT(n) = 5, and Z(critical) for 5% in each tail = 1.645: SIGMA/SQRT(n) = .6 and (.6)*(1.645) = .987Back to review this question

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A: (1) 0.6 Lower confidence limit = XBAR - [t * (S/SQRT(n))] = 3.2 - [2.6 * (4/4)] = 0.6Back to review this question

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A: (3) The variance of the population is usually not known.Back to review this question

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A: (c) (70.7%, 89.3%) Confidence interval is: 80.0 +/- (100*1.645*SQRT(.80*(1 - .80)/50))Back to review this question

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A: b. the interval brackets the unknown MU approximately 95 times out of a 100.Back to review this question

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A: c. 68 to 72 inches C.I. = XBAR +/- Z * Standard Error = 70 +/- 1.96 * SIGMA/SQRT(n) = 70 +/- 1.96 * 3/SQRT(9) = 68.04 to 71.96 inches or approximately 68 to 72 inches.Back to review this question

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A: False, the confidence interval increases as the confidence coefficient increases, other things being equal.Back to review this question

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A: False, it means that in about 95 percent of all samples drawn by the same method, the true mean will be included in the confidence interval.Back to review this question

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A: c. .866 Z = (X - MU)/(SIGMA/SQRT(n)) Z = (2.95-28)/(4/SQRT(16)) = 1.50 Area between 26.5 and 29.5 is .866.Back to review this question

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A: e. -0.15 to 10.15 SIGMA(XBAR) = 20/10 = 2 C.I. = XBAR +/- Z(.005)SIGMA(XBAR) = 5 +/- 2.576(2) = 5 +/- 5.15Back to review this question

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A: b. 1.04 - 4.96 Noting n = 5, we find: XBAR = 3, S**2 = 2.5, S = SQRT(2.5) = 1.581 Using t(ALPHA = .05, df = 4) = 2.776: C.I. = XBAR +/- (t)*(S/SQRT(n)) = 3 +/- (2.776)*(1.581/SQRT(5)) = 3 +/- 1.96 = 1.04 to 4.96Back to review this question

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A: d. student's t--this assumes depression scores are normally distributed and student's t is the most common way of making statements about 1 or 2 means.Back to review this question

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A: (d) 20 +/- .8 SIGMA(XBAR) = SIGMA/SQRT(n) = 2/5 = .40 (SIGMA(XBAR))*Z(crit) = (.40)( 1.96) = .784 == .8 C.I. = 20 +/- .8Back to review this question

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A: b. 2.060 Since all statistics involved are sample statistics, use the t distribution with df = 25, and it is found that 2.5% of the area lies in each tail in a two-tailed test when t = 2.060.Back to review this question

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A: d. 11.2 +/- 2.306(3) C.I. = XBAR +/- t(C)(S/SQRT(n)) = 11.2 +/- 2.306(9/SQRT(9)) = 11.2 +/- 2.306(3)Back to review this question

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A: (c) .56 +/- 1.96SQRT((.44)(.56)/400) SIGMA = SQRT((P)(Q)/n) = SQRT((.56)(.49)/400). Population Proportion = P +/- Z(C)(S) = .56 +/- 1.96((.44)(.56)/ 400)Back to review this question

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A: (3) B's interval will always be larger than A's interval.Back to review this question

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A: c. Increasing sample size decreases the length, given a fixed coefficient.Back to review this question

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A: d. .8 + 2.576*SQRT(.16/625) C.I. = p +/- Z(ALPHA/2)*SQRT(pq/n) p = 500/625 = .8, q = 125/625 = .2 C.I. = .8 +/- Z(.005)*SQRT(.8*.2/625) Upper limit = .8 + 2.576*SQRT(.16/625)Back to review this question

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A: e. a, b, and c Confidence Interval = XBAR +/- (Z)*(Standard Deviation/SQRT(n)) Z value is dependent on the confidence coefficient, and the standard error is SQRT(Variance). Therefore, a, b, and c affect the size of the confidence in- terval.Back to review this question

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A: (d) 31 +/- 1.81 Since the df are so high, we can use the normal table even though SIGMA is unknown. 93% confidence interval = XBAR +/- Z*S/SQRT(n) Area beyond Z = .035 Therefore, Z = 1.81 C.I. = 31 +/- (1.81)(SQRT(89)/SQRT(89)) = 31 +/- 1.81Back to review this question

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A: 1. A. 1.28 2. B. (2/5) +/- (1.28)(SQRT((6/25)(1/50))) PI(1) = 20/50 = 2/5 S.D. = SQRT(pq/n) = SQRT((2/5)(3/5)/50)Back to review this question

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A: (c) .5 +/- .129 S = SQRT((.5)(.5)/100) = .05 C.I. = .5 +/- (2.576)(.05) = .5 +/- (.1288)Back to review this question

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A: (e) .0456 Half confidence interval = (SIGMA/SQRT(n))*Z = (3/6)*2 Z = 2 Area beyond Z = .0228 Total Probability = 2(.0228) = .0456, because it is the proba- bility of getting the difference of -1 as well as +1.Back to review this question

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A: (a) XBAR +/- 1.96(1/2) C.I. = XBAR +/- Z*SIGMA/SQRT(n) = XBAR +/- 1.96*(3/6)Back to review this question

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A: b) 58 +/- (2.13*2) C.I. = MU +/- (t*S/SQRT(N)) = 58 +/- (2.13 * 8/4) = 58 +/- 4.26 Note: t(df=15, ALPHA=.05, two-tailed) = 2.13Back to review this question

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A: (b) [30.80, 31.19] C.I. = XBAR +/- Z*S/SQRT(n) = 31 +/- 1.96*2/SQRT(400) = 30.80, 31.19 note: VARIANCE = 1596/(n - 1) = 1596/399 = 4 Therefore, S will be = SQRT(4) = 2Back to review this question

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A: (a) [15.8, 24.3] C.I. = XBAR +/- t*S/SQRT(n) = 20 +/- 2.13*8/4 = [15.8, 24.3] Variance = 960/15 = 64 Standard Deviation = SQRT(64) = 8 Standard Error Of Mean = 8/SQRT(16) = 2Back to review this question

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A: B. [.761, .839] C.I. = PHAT +/- Z(ALPHA/2) * SQRT((PHAT*(1-PHAT))/n) = .8 +/- 1.96 * SQRT((.8*.2)/400) = .8 +/- .0392Back to review this question

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A: a. 12 +/- 3.92 XBAR = (8 + 11 + 9 + 17 + 12 + 15)/6 = 12 SIGMA(XBAR) = SQRT(24/6) = 2 C.I. = XBAR +/- Z(ALPHA/2) * (SIGMA(XBAR)) = 12 +/- (1.96)(2) = 12 +/- (3.92)Back to review this question

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A: a. (14116, 15884) C.I. = 15,000 +/- (2.947*1200)/SQRT(16) = 15,000 +/- 884.1 = from 14116 to 15884Back to review this question

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A: d. 1.96 (Using normal table)Back to review this question

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A: a. (73.79, 76.41) 75.1 +/- (1.75)[SQRT(9)/SQRT(16)] 75.1 +/- 1.31Back to review this question

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A: a. 43 and 57.Back to review this question

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A: a. 98.9 - 107.1 S(XBAR) = S/SQRT(n) = 12/5 = 2.4 t(critical, df=24, two-tailed, ALPHA=.1) = 1.711 C.I. = XBAR +/- [t * S(XBAR)] = 103 +/- [1.711 * (12/5)] = 103 +/- [4.1064] = from 98.89 to 107.1064Back to review this question

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A: d. (9.02, 10.98) Since SIGMA is unknown, use the t distribution. However because n=400, t is approximated by the Z distribution. 95% C.I. = MU +/- Z(.025)*[S/SQRT(n)] = 10 +/- (1.96)(10/SQRT(400)) = 10 +/- (1.96)(.5) = 10 +/- 0.98 = from 9.02 to 10.98Back to review this question

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A: 2) 49.66 to 65.34 CI = XBAR +/- [Z*SIGMA(XBAR)] = 57.5 +/- [1.96*(20/SQRT(25))] = 57.5 +/- [7.84] = from 49.66 to 65.34Back to review this question

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A: c. variable from sample to sample.Back to review this question

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A: c. 100 .10 * 1000 = 100Back to review this question

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A: b. 74.1 - 75.9 C.I. = 75 +/- (1.96) * (7/SQRT(225)) = 75 +/- .91467 = (74.1, 75.9)Back to review this question

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A: d. if many samples are drawn, the computed confidence intervals will contain MU 95% of the time.Back to review this question

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A: XBAR = 110.67 S(X)**2 = 7.1 S(X) = 2.667 df = 8 (a) 110.67 +/- 2.306 * 2.667/SQRT(9) 108.61 <= MU <= 112.72 at 95% confidence (b) No, it is outside the interval in part a. (c) 110.67 +/- 3.355 (2.667/3) 107.69 <= MU <= 113.65 at 99% confidenceBack to review this question

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A: CI = XBAR +/- ((Z(.025))*SIGMA)/SQRT(n) = 800 +/- ((1.96)(120))/SQRT(100) = 800 +/- 23.52 = from 776.48 to 823.52Back to review this question

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A: S = SQRT((.21)(.79)/100) = .0407 Z(ALPHA/2 = .025) = 1.96 C.I. = .21 +/- (1.96)(.0407) = .21 +/- (.0798) = from .13 to .29Back to review this question

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A: S(YBAR) = SQRT([n/(n-1)][.01660/n]) = .0263 YBAR - t(crit)*S(YBAR) < MU < YBAR + t(crit)*S(YBAR) 7.25 - 1.711 * .0263 < MU < 7.25 + 1.711 * .0263 7.205 < MU < 7.295Back to review this question

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A: C.I. = XBAR +/- [t(df=15,ALPHA=.01)*S/SQRT(n)] C.I. = 100 +/- [2.602*(3/SQRT(16))] C.I. = 100 +/- 1.95 = from 98.05 to 101.95Back to review this question

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A: Using t with ALPHA = .01 and df = 499, C.I. = XBAR +/- (t) (S/SQRT(n)) = 242.30 +/- (2.576) (3.20/SQRT(500)) = 241.93 to 242.67. 99% of the time that this procedure is used to calculate an interval, the resulting interval will contain MU. This interval may or may not include MU.Back to review this question

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A: PHAT = .33 n = 2600 Z(ALPHA=.025) = 1.96 Stand. error of proportion = SQRT((PHAT(1-PHAT))/n) = SQRT((.33*.67)/2600) = .009 C.I. = .33 +/- (1.96 * .009) = from .312 to .348Back to review this question

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A: C.I. = XBAR +/- (Z)*(SIGMA(XBAR)) = 24 +/- (1.645) (2) = 24 +/- 3.29Back to review this question

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A: PHAT = 225/360 = .625 or 62.5% Standard error of proportion = SQRT((PHAT(1-PHAT))/n) S(PHAT) = SQRT(.625*.375/360) = .026 C.I. = PHAT +/- Z(ALPHA = .01) * S(PHAT) = 62.5% +/- (2.58)*(02.6)% = 62.5% +/- 6.7% = from 55.8 to 69.2%Back to review this question

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A: S(XBAR) = S/SQRT(n) = 30.9/3 = 10.3 C.I. = XBAR +/- ([t(ALPHA = .005, df = 8)]*S(XBAR)) = 210 +/- (3.355*10.3) = from 175.4 to 244.6Back to review this question

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A: .475 +/- 2.58 SQRT((.475*.525)/200) = .475 +/- .091Back to review this question

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A: SIGMA = SQRT(pq/n) and t with ALPHA = .05 and df = 29 equals 2.042 C.I. = .6 +/- 2.042 * SQRT ((.6*(1-.6))/30) C.I. = .6 +/- 2.042 * SQRT (.008) C.I. = .6 +/- .1826 C.I. = .417 - .783 at 95% confidenceBack to review this question

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A: C.I. = XBAR +/- (Z) (SIGMA/SQRT(n)) = 35 +/- (1.96) (SQRT(4/9)) = 35 +/- 1.31 = 33.69 to 36.31Back to review this question

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A: 90% confidence interval is from .1342 to .2658. C.I. = PHAT +/- [Z(ALPHA(2))*SQRT((PHAT*QHAT)/n)] = .2 +/- [1.645 * (.2*.8/100)] = .1342 to .2658Back to review this question

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A: a. Confidence interval = XBAR +/- [Z * S(XBAR)] = 72 +/- (2.576 * [15/SQRT(25)]) = 72 +/- (2.576 * 3) = from 64.272 to 79.728 b. Based on the sample statistic, we are 99% confident that the in- terval from 64.27 to 79.73 covers the mean. This implies that if all possible samples of size n (= 25) were taken from the population, and a 99% confidence interval were calculated from each sample, 99% of all these intervals would contain the true population mean.Back to review this question

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A: a) C.I. = XBAR +/- (t(ALPHA=.05,df=8) * [s/SQRT(n)]) = 12 +/- (2.306 * [1/SQRT(9)]) = 12 +/- .769 = from 11.231 to 12.769 This procedure for producing confidence limits will yield a confi- dence interval that contains MU 95% of the time. A probability statement cannot be made about the calculated interval (11.231, 12.769) since it either contains MU or does not contain MU. b) Yes, because a Z value would then be used. Z(.025) = 1.96 < 2.306Back to review this question

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A: a. C.I. = XBAR +/- [t*S(XBAR)]; t(ALPHA=.01, one-tail, df=15) = 2.602 = 16.35 +/- [2.602*(4.56/SQRT(16))] = from 13.384 to 19.316 b. According to the confidence interval found in part a, Mr. Blackwater's estimate of $19.01 is a possible estimate for the population mean. It should be pointed out that we are 98% confident that such a confidence interval would contain the population mean.Back to review this question

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A: n = 3 SUM(X(i)) = 37.53 XBAR = [37.53]/[3] = 12.51 S**2 = [[(12.50-12.51)**2]+[(10.75-12.51)**2]+[(14.28-12.51)**2]]/[2] = [[0.0001] +[3.0976] + [3.1329]]/[2] = 3.1153 S = 1.7650 S(XBAR) = [1.7650]/[SQRT(3)] = 1.0190 90% C.I. = XBAR +/- [t(ALPHA=0.05, df=2) * S(XBAR)] = 12.51 +/- [2.92 * 1.019] = 12.51 +/- 2.98 = from $9.53 to $15.49Back to review this question

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A: n = 7 SUM(X(i)) = 421 XBAR = 421/7 = 60.143 S(XBAR) = [9.856]/[SQRT(7)] = 3.725 90% C.I. = XBAR +/- [t(ALPHA=.05, df=6) * S(XBAR)] = 60.143 +/- [(1.943) * (3.725)] = 60.143 +/- [7.238] = from 52.905 to 67.381Back to review this question

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A: n = 10 90% C.I. = 5.9 +/- [t(df=9,ALPHA=.05,one-tail)*(2.18/SQRT(10)] = 5.9 +/- [1.833 * 0.69] = 5.9 +/- [1.266] = from 4.63 to 7.166Back to review this question

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A: n = 6 XBAR = 3.83 90% C.I. = 3.83 +/- [t(df=5,ALPHA=.05,one-tail)*(0.983/SQRT(6)] = 3.83 +/- [2.015 * 0.401] = 3.83 +/- [0.809] = from 3.02 to 4.64Back to review this question

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A: (a) MU(HAT) = 22 (b) With t(ALPHA = .01, df = 99) = 2.63 22 +/- 2.63 * 5/SQRT(100) 22 +/- 1.315 20.685 to 23.315 (c) There are several reasons why this confidence interval is approxi- mate, among them is that the term average is not clearly defined and that S approximates SIGMA. Also, the population distribution may not be normal.Back to review this question

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A: P = 3/10 C.I. for this proportion = 3/10 +/- 10% = 3/10 +/- 1/10 = 2/10 to 4/10 Therefore, between 20% and 40% of the Democrats favored Senator Kennedy.Back to review this question

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A: Confidence Interval for the difference: = .20 +/- .14 = .06 <= the true difference <= .34 at 95% confidence. Since this interval does not include zero, I would forecast a win for candidate A.Back to review this question

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A: The most favorable confidence limit for my candidate would be c., 4% +/- 10%, 1-ALPHA=.80. This limit indicates a larger standard deviation than the other two, since t(ALPHA=.20) is smaller than t(ALPHA=.05). Since my candidate is behind the most favorable situation is that which indicates the greatest uncertainty that the difference is anything more than random sampling variation.Back to review this question

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A: 1. a. To set 90% confidence limits including MU, we need XBAR, the sample standard deviation, s, the sample size, n, and a t value for ALPHA = .1. Use the formula: XBAR +/- (t, ALPHA/2)(s/SQRT(n)) with t(ALPHA/2) = 2.015. b. Use the same procedure as in 1a, but use t(ALPHA/2) = 4.032. 2. XBAR = 11 Standard deviation = 2.28 n = 6 a. 11 +/- (2.015)(2.28/SQRT(6)) = 11 +/- (2.015)(.93) = 11 +/- 1.876 = 9.124 to 12.876 This is inconsistent with the specification of hoppiness = 8.0. b. 11 +/- (4.032)(2.28/SQRT(6)) = 11 +/- (4.032)(.93) = 11 +/- 3.753 = 7.247 to 14.753 This is consistent with the specification of hoppiness = 8.0. 3. Ths basic weakness seems to be in using a procedure that produces a confidence interval consistent with a wide range of values, which makes it difficult to detect departures from MU = 8. This situation is exaggerated when the 99% level is used. In addition, the sample size used seems small relative to the variability measured.Back to review this question

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A: False. If (5,8) is a 95% confidence interval for MU then we are 95% "confident" that the interval contains the value of MU. However, since MU is a constant it either does (P=1.00) or does not (P=0.00) lie in the interval, so it does not make sense to say that P=.95.Back to review this question

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A: True - the more confidence one needs to place in the interval containing the true mean, the more leeway one must provide.Back to review this question

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A: True CONFIDENCE INTERVAL = Mean +/- Z*Standard Error As you increase (1 - ALPHA) the Z value increases, so the width of the confidence interval will also increase.Back to review this question

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A: TrueBack to review this question

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A: False. If confidence intervals are based on the same sample observations, and are for the same unknown parameter, then, for example, under normality assumption, a 90% CI for MU is = XBAR +/- 1.645 S(XBAR) A 95% CI for MU is: XBAR +/- 1.96 S(XBAR) Hence the statement is not true.Back to review this question

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A: True.Back to review this question

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A: e. Sufficient information but correct result is not given. midpoint of interval = PHAT = .65Back to review this question

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A: d. all of the above.Back to review this question

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A: False - the confidence coefficient is the probability that an unknown parameter will be within the given interval.Back to review this question

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A: True.Back to review this question

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A: Definition: The product of a probability dependent multiplier times the standard error of a random variable. If the standard error is known the multiplier usually comes from the standard normal table. If the standard error is estimated the multiplier usually comes from the student's t. Example: Suppose that the significance level is .05 and the population variance is known to be 16. Suppose further that a sample of size 4 has been drawn. Then the 95% margin of error for the population mean is 1.96 times 2 or 3.92. (The population standard error of a mean is the square root of the variance divided by the sample size, here: SQRT(16/4). Symbol: Such as MOE = Z(.05) * S(XBAR)Back to review this question

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A: c. estimate the mean of the populationBack to review this question

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A: a. the n is large enoughBack to review this question

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A: d. MU(X) and ((SIGMA(X))**2)/nBack to review this question

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A: e) none of the above are true.Back to review this question

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A: a) zeroBack to review this question

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A: d. refers to a matter other than those stated above.Back to review this question

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A: c. if n is large regardless of the shape of the parent population distribution.Back to review this question

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A: Confidence Interval = XBAR +/- Z*SIGMA(XBAR) = XBAR +/- 10 10 = Z * SIGMA(XBAR) = Z * (25/6) Z = 2.4 Probability = 2 * (.4918) = .9836Back to review this question

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A: The variance of the sampling distribution of means decreases as sample size increases. Therefore, one's estimate becomes more accurate as sample size increases.Back to review this question

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A: A. The sampling distribution of XBAR for a sample size of 25 is the frequency distribution of XBARs that results from finding the mean of every distinct sample of 25 from the population. The Central Limit Theorem states, among other things, that the shape of this sampling distribution of the means will approach that of a normal distribution as larger samples are used, regardless of the shape of the population distribution. The figure below is an example of a sampling distribution of XBAR where MU is the population mean and SIGMA is the population standard deviation. (If available, consult file of graphs and diagrams that could not be computerized for appropriate figure.) B. They will be the same. C. SIGMA(XBAR) = SIGMA/SQRT(n) D. 1) Confidence interval size will decrease. 2) Confidence interval size will increase.Back to review this question

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A: a.) Z is approximately normally distributed with MEAN = 0 and SIGMA = 1. b.) MU(Z) = 0 c.) VAR(Z) = 1 d.) It is of fundamental importance because it specifies that the sampling distribution of the mean will be normal in shape for large samples, even if the original population distribution is not.Back to review this question

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A: False. The Central Limit Theorem holds equally whether we sample from a normal distribution or not.Back to review this question

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A: True, the Central Limit Theorem justifies approximating the distribution of XBAR with a normal distribution when n is sufficiently large.Back to review this question

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A: True, the Central Limit Theorem applies to sampling from any distribution.Back to review this question

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A: True, the mean of the sampling distribution of means is exactly equal to the population mean regardless of sample size and type of distribution.Back to review this question

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A: True, the normal approximation to the probability distribution of XBAR is reasonably good in most cases for sample sizes of 30 or more, pro- vided we have a random sample from X(i).Back to review this question

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A: False. The central limit theorem says that, under the given condition, the sample means, obtained upon repeated sampling from a population, will be normally distributed.Back to review this question

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A: False. The sampling distribution of XBAR will be normal in shape for large samples, (n>30), even if the original population distri- bution is not.Back to review this question

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A: False. Other things being equal, a HIGH level of confidence is desirable.Back to review this question

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A: STATISTICAL INFERENCE is the process of drawing conclusions about popula- tion characteristics from the facts given by a sample. It is generali- from the specific to the general. Other possible answers might be: INFERENCE, INDUCTIVE INFERENCE, INFERENTIAL STATISTICSBack to review this question

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A: True. Let X = 1 if it has the desired attribute and X = 0 if it does not. Then the proportion, p, is: p = (SUM(X))/n.Back to review this question

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A: b. 18 and 4 respectively Sampling distribution for the sample mean has a mean equal to the population mean and variance equal to SIGMA**2/n.Back to review this question

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A: b. normally for any given sample size if the sample is randomly selected.Back to review this question

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A: TrueBack to review this question

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A: False, size of mean is independent of size of standard deviation. Standard deviation depends on the variability among individual ob- servations.Back to review this question

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A: True. Mean of sampling distribution = population mean. Standard deviation of sampling distribution = SIGMA/SQRT(n) = 1/SQRT(4) = .5Back to review this question

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A: (b) .9 Since SIGMA(XBAR)**2 = SIGMA(X)**2/n.Back to review this question

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A: This question cannot be answered accurately without knowing the standard deviation for IQ scores. Then these differences from the mean or average can be evaluated relative to the standard deviation as to whether there is a significant difference between them.Back to review this question

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A: The difference in mean income between two towns of 10,000 people, when based on samples of 5 people, will be much more variable from sampling situation to situation than a difference based on samples of 100 people. The standard error allows us to take this into account.Back to review this question

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A: SIGMA is the standard deviation of the population, while SIGMA(XBAR) is the standard error of the mean, which is the standard deviation of the sampling distribution of means. The relationship between the two is: SIGMA(XBAR) = SIGMA/SQRT(n); where n is the size of the sample.Back to review this question

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A: False. For large n, S(XBAR) should be smaller than the standard deviation because we know S(XBAR) = S/SQRT(n). Therefore, S(XBAR) will always be smaller than S, since n is larger than 1.Back to review this question

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A: True. The standard deviation of all possible sample means should equal SIGMA/SQRT(n), where SIGMA is the standard deviation of the original observations, and n is the sample size.Back to review this question

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A: True.Back to review this question

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A: b. 13.00 XBAR = (5 + 10 + 3)/3 = 6 S(X)**2 = [(5 - 6)**2 + (10 - 6)**2 + (3 - 6)**2]/(3 - 1) = (1 + 16 + 9)/2 = 26/2 = 13Back to review this question

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A: MU = E(X) = (0*.4) + (1*.3) + (2*.2) + (3*.1) = 1.0 E(X**2) = (0*.4) + (1*.3) + (4*.2) + (9*.1) = 2.0 VAR(X) = E(X**2) - (E(X))**2 = 2.0 - 1.0 = 1.0 VAR(XBAR) = (VAR(X))/n = 1.0/5 = 0.2 SUM(X) = 6 SUM(X**2) = 14 S(X)**2 = [SUM(X**2) - ((SUM(X))**2)/n]/(n - 1) = [14 - (36/5)]/4 = 1.7 S(XBAR)**2 = (S(X)**2)/n = 1.7/5 = 0.34Back to review this question

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A: A. 3 B. 25Back to review this question

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A: False. The distribution of XBAR will have a variance equal to the population variance divided by n.Back to review this question

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A: (3) As the sample size decreases, the variability of the distribution of XBAR about MU increases. Variability = (XBAR - MU)/(SIGMA/SQRT(n)) Therefore, variability increases as n decreases because n is in the denominator.Back to review this question

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A: c. 2 SIGMA(XBAR) = SIGMA/SQRT(n) = 8/4 = 2Back to review this question

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A: (1) .1056 Z = (XBAR - MU)/(SIGMA/SQRT(n)) Z = (0 - 5)/(20/5) = -1.25 AREA = .5 - .3944 = .1056Back to review this question

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A: (e.) b and d According to the Central Limit Theorem: MU(XBAR) = MU = 138 SIGMA(XBAR)**2 = SIGMA**2/n = 126/6 = 21Back to review this question

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A: (d.) the sampling distribution of the mean.Back to review this question

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A: c. it decreases SIGMA(XBAR) = SIGMA/SQRT(n)Back to review this question

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A: c. an estimate of the standard error of the meanBack to review this question

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A: d) standard error of the mean.Back to review this question

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A: c) 3.0 SIGMA(XBAR) = SIGMA/SQRT(n) = 18/SQRT(36) = 3Back to review this question

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A: c. Use [s(Y)]/[SQRT(n)]Back to review this question

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A: b. It will be one-third as large.Back to review this question

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A: d. is characterized by all of the above.Back to review this question

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A: d. [SIGMA(Y)]/[SQRT(n)]Back to review this question

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A: Investigator X. It is the size of the sample rather than the population that matters, when dealing with such large populations and samples. SIGMA(sample) = SIGMA(population)/SQRT(n) If we regard SIGMA(population) as a known constant, then 1/SQRT(500) = .044 and 1/SQRT(1000) = .031 These "multiplication factors" will be introduced, leading to a larger standard error with a smaller sample. I.E., the bigger the sample, the larger our denominator when we take the square root, and the larger the denominator, the smaller the value of the standard error of the mean.Back to review this question

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A: False, as the sample size increases, the standard error of the mean decreases. [SIGMA(XBAR) = SIGMA/SQRT(n)]Back to review this question

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A: False - the standard deviation of the random sampling distribution of the mean is equal to the population standard deviation divided by SQRT(n).Back to review this question

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A: False, the sampling distribution of the mean will always have a variance which is less than the variance of the associated parent population. Variance of sampling distribution of mean = S**2/n Variance of parent population = S**2Back to review this question

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A: False, the standard error of the sample mean decreases as the sample size increases.Back to review this question

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A: False. The only requirement for this formula is that sample members are independently and identically distributed.Back to review this question

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A: b. the variability of a sampling distribution of medians.Back to review this question

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A: If the appropriate measure of variability for a distribution is the standard error it must be a sampling distribution.Back to review this question

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A: b. MU(X) = MU(XBAR) and SIGMA(X)**2 >= SIGMA(XBAR)**2 Mean of a sampling distribution is always equal to mean of parent distribution and variance of sampling distribution is equal to variance of parent distrbution divided by n.Back to review this question

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A: b) of the mean is a distribution of the means taken from all possible samples of a given size n that could be taken from the population.Back to review this question

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A: b. Its standard deviation is greater than that of the population of scores.Back to review this question

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A: c. We SELDOM have a complete sampling distribution displayed for us.Back to review this question

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A: The sampling distribution is the distribution of estimates for some particular statistic found from taking many samples. The process is to take a sample, calculate the estimate, draw another sample, calculate its estimate, and repeat this a large number of times. The sampling distribution is formed by getting a frequency diagram of these estimates. In contrast, the distribution of the sample is a frequency diagram of the observations occurring within one sample.Back to review this question

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A: True. Standard deviation of the sample mean = (SIGMA)/SQRT(n) and: (SIGMA)/SQRT(9*n) = (1/3)*[(SIGMA)/SQRT(n)]Back to review this question

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A: True.Back to review this question

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A: False, sample size depends on economic considerations and the varia- bility in the population. A more variable population needs a bigger sample, while a less variable population requires a smaller sample.Back to review this question

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## Identification:

Item is still being reviewed Multiple Choice BASICTERMS/PROB SIMPLE/CI SAMPLESIZE PROBABILITY CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS SAMPLING T= 5 Comprehension D= 4 GeneralBack to this chapter's Contents

Based upon item submitted by H. B. Christensen - BYU Multiple Choice TDISTRIBUTION SIMPLE/CI PROBDISTRIBUTION PROBABILITY CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS T= 2 Computation D= 1 GeneralBack to this chapter's Contents

Item is still being reviewed Multiple Choice TDISTRIBUTION PROBDISTRIBUTION PROBABILITY T= 2 Application D= 1 General ***Statistical Table Necessary***Back to this chapter's Contents

Based upon item submitted by B. Weir - N. C. State & Massey Univ. Multiple Choice TDISTRIBUTION PROBDISTRIBUTION PROBABILITY T= 2 Comprehension Application D= 2 General ***Statistical Table Necessary***Back to this chapter's Contents

Based upon item submitted by F. J. Samaniego - UC Davis Multiple Choice TDISTRIBUTION PROBDISTRIBUTION PROBABILITY T= 5 Comprehension D= 2 GeneralBack to this chapter's Contents

Based upon item submitted by J. L. Mickey -UCLA Multiple Choice TDISTRIBUTION OTHER/N PROBDISTRIBUTION PROBABILITY NORMAL T= 2 Comprehension D= 2 GeneralBack to this chapter's Contents

Item is still being reviewed Multiple Choice STANDUNITS/NORMA TDISTRIBUTION PROBDISTRIBUTION PROBABILITY T= 2 Comprehension D= 2 GeneralBack to this chapter's Contents

Item is still being reviewed Multiple Choice TDISTRIBUTION OTHER/N PROBDISTRIBUTION PROBABILITY NORMAL T= 2 Comprehension D= 4 General EducationBack to this chapter's Contents

Based upon item submitted by J. L. Mickey -UCLA Fill-in TDISTRIBUTION ESTIMATION/OTHER PROBDISTRIBUTION PROBABILITY ESTIMATION CONCEPT STATISTICS T= 2 Comprehension D= 4 General ***Multiple Parts***Back to this chapter's Contents

Based upon item submitted by J. Warren - UNH Fill-in TDISTRIBUTION STANDUNITS/NORMA I650I PROBDISTRIBUTION PROBABILITY T= 5 Comprehension D= 3 General ***Multiple Parts*** ***Statistical Table Necessary***Back to this chapter's Contents

Based upon item submitted by J. Warren - UNH Numerical Answer TDISTRIBUTION I650I PROBDISTRIBUTION PROBABILITY T= 5 Comprehension Computation D= 2 General ***Statistical Table Necessary***Back to this chapter's Contents

Based upon item submitted by J. Warren - UNH Numerical Answer TDISTRIBUTION STANDUNITS/NORMA I650I PROBDISTRIBUTION PROBABILITY T= 2 Comprehension D= 1 GeneralBack to this chapter's Contents

Item is still being reviewed Short Answer NORMAL TDISTRIBUTION PROBDISTRIBUTION PROBABILITY T= 5 Comprehension D= 2 General ***Multiple Parts***Back to this chapter's Contents

Item is still being reviewed True/False TDISTRIBUTION OTHER/T PROBDISTRIBUTION PROBABILITY TTEST PARAMETRIC STATISTICS T= 2 Comprehension D= 3 GeneralBack to this chapter's Contents

Based upon item submitted by W. J. Hall - Univ. of Rochester Multiple Choice ZSCORE SIMPLE/CI TWOTAIL/Z STANDUNITS/NORMA PROBDISTRIBUTION PROBABILITY CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS ZTEST PARAMETRIC T= 2 Computation Comprehension D= 1 General ***Statistical Table Necessary***Back to this chapter's Contents

Based upon item submitted by W. J. Hall - Univ. of Rochester Multiple Choice ZSCORE STANDERROROFMEAN OTHER/CI STANDUNITS/NORMA PROBDISTRIBUTION PROBABILITY DESCRSTAT/P PARAMETRIC STATISTICS CONFIDENCEINTERV ESTIMATION CONCEPT T= 2 Computation D= 2 GeneralBack to this chapter's Contents

Item is still being reviewed Multiple Choice SIMPLE/CI ZSCORE CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS STANDUNITS/NORMA PROBDISTRIBUTION PROBABILITY T= 2 Computation D= 2 General ***Statistical Table Necessary***Back to this chapter's Contents

Based upon item submitted by F. J. Samaniego - UC Davis Multiple Choice ZSCORE STANDUNITS/NORMA PROBDISTRIBUTION PROBABILITY T= 5 Computation D= 2 Education GeneralBack to this chapter's Contents

Based upon item submitted by F. J. Samaniego - UC Davis Multiple Choice ZSCORE MEAN STANDERROROFMEAN STANDUNITS/NORMA PROBDISTRIBUTION PROBABILITY DESCRSTAT/P PARAMETRIC STATISTICS T=10 Computation D= 4 GeneralBack to this chapter's Contents

Item is still being reviewed Multiple Choice ZSCORE STANDUNITS/NORMA PROBDISTRIBUTION PROBABILITY T= 5 Application D= 4 Biological Sciences Education ***Statistical Table Necessary***Back to this chapter's Contents

Item is still being reviewed Multiple Choice ZSCORE SIMPLE/CI STANDUNITS/NORMA PROBDISTRIBUTION PROBABILITY CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS T= 5 Application Comprehension D= 3 Biological Sciences Education ***Statistical Table Necessary***Back to this chapter's Contents

Item is still being reviewed Multiple Choice ZSCORE STANDERROROFMEAN STANDUNITS/NORMA PROBDISTRIBUTION PROBABILITY DESCRSTAT/P PARAMETRIC STATISTICS T= 2 Comprehension D= 2 Biological Sciences GeneralBack to this chapter's Contents

Based upon item submitted by D. Kleinbaum - Univ of North Carolina Multiple Choice SIMPLE/CI ZSCORE MEAN CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS STANDUNITS/NORMA PROBDISTRIBUTION PROBABILITY DESCRSTAT/P PARAMETRIC T= 5 Application Comprehension D= 6 Biological Sciences General ***Statistical Table Necessary***Back to this chapter's Contents

Item is still being reviewed Multiple Choice ZSCORE STANDERROROFMEAN STANDUNITS/NORMA PROBDISTRIBUTION PROBABILITY DESCRSTAT/P PARAMETRIC STATISTICS T= 2 Computation D= 2 General ***Statistical Table Necessary***Back to this chapter's Contents

Item is still being reviewed Numerical Answer ZSCORE STANDUNITS/NORMA PROBDISTRIBUTION PROBABILITY T=10 Computation D= 2 General Education ***Calculator Necessary*** ***Statistical Table Necessary***Back to this chapter's Contents

Based upon item submitted by G. Miaoulis - Wright State Univ. Numerical Answer ZSCORE STANDERROROFMEAN TESTING I650I DESCRSTAT/P PARAMETRIC STATISTICS CONCEPT STANDUNITS/NORMA PROBDISTRIBUTION PROBABILITY T=10 Application D= 6 General Natural Sciences ***Multiple Parts*** ***Statistical Table Necessary***Back to this chapter's Contents

Based upon item submitted by J. Warren - UNH Numerical Answer STANDERROROFMEAN ZSCORE SAMPLINGDISTRIB I650I DESCRSTAT/P PARAMETRIC STATISTICS STANDUNITS/NORMA PROBDISTRIBUTION PROBABILITY SAMPLING T= 5 Comprehension D= 3 General ***Statistical Table Necessary***Back to this chapter's Contents

Item is still being reviewed Multiple Choice SCOPEOFINFERENCE BASICTERMS/STATS CONCEPT STATISTICS T= 2 Comprehension D= 1 General EducationBack to this chapter's Contents

Based upon item submitted by J. Warren - UNH Definition STANDERROROFMEAN BASICTERMS/STATS I650I DESCRSTAT/P PARAMETRIC STATISTICS T= 5 Comprehension D= 3 GeneralBack to this chapter's Contents

Based upon item submitted by J. Warren - UNH Definition MEAN BASICTERMS/STATS I650I DESCRSTAT/P PARAMETRIC STATISTICS T= 5 Comprehension D= 3 GeneralBack to this chapter's Contents

Item is still being reviewed Short Answer PROPORTION SIMPLE/CI TESTING STANDERROR/OTHER I650I DESCRSTAT/P PARAMETRIC STATISTICS CONFIDENCEINTERV ESTIMATION CONCEPT T= 5 Application D= 6 General ***Calculator Necessary*** ***Multiple Parts*** ***Statistical Table Necessary***Back to this chapter's Contents

Item is still being reviewed Numerical Answer ESTIMATION CONFIDENCEINTERV CONCEPT STATISTICS T= 5 Computation Comprehension D= 3 General ***Statistical Table Necessary***Back to this chapter's Contents

Based upon item submitted by R. Pruzek - SUNY at Albany Multiple Choice STANDERROROFMEAN SAMPDIST/C DESCRSTAT/P PARAMETRIC STATISTICS ESTIMATION CONCEPT T= 5 Computation D= 2 General ***Calculator Necessary***Back to this chapter's Contents

Based upon item submitted by R. Pruzek - SUNY at Albany True/False SAMPDIST/C SAMPLESIZE ESTIMATION CONCEPT STATISTICS SAMPLING T= 5 Comprehension D= 3 GeneralBack to this chapter's Contents

Based upon item submitted by R. Pruzek - SUNY at Albany True/False SAMPDIST/C MEAN ESTIMATION CONCEPT STATISTICS DESCRSTAT/P PARAMETRIC T= 5 Comprehension D= 3 GeneralBack to this chapter's Contents

Based upon item submitted by R. Pruzek - SUNY at Albany True/False SAMPDIST/C CENTRALLIMITTHM ASSUMPTCUSTOMARY ESTIMATION CONCEPT STATISTICS MISCELLANEOUS T= 5 Comprehension D= 4 GeneralBack to this chapter's Contents

Based upon item submitted by R. Pruzek - SUNY at Albany True/False CENTRALLIMITTHM SAMPDIST/C CONCEPT STATISTICS ESTIMATION T= 5 Comprehension D= 3 GeneralBack to this chapter's Contents

Item is still being reviewed Multiple Choice SIMPLE/CI CONFIDENCEINTERV MEAN ESTIMATION CONCEPT STATISTICS DESCRSTAT/P PARAMETRIC T= 2 Computation Comprehension D= 5 General ***Calculator Necessary*** ***Statistical Table Necessary***Back to this chapter's Contents

Based upon item submitted by B. Weir - N. C. State & Massey Univ. Multiple Choice CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS T= 2 Computation Comprehension Application D= 2 General ***Calculator Necessary***Back to this chapter's Contents

Item is still being reviewed Multiple Choice CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS T= 2 Comprehension D= 1 GeneralBack to this chapter's Contents

Item is still being reviewed Multiple Choice CONFIDENCEINTERV PROPORTION ZSCORE ESTIMATION CONCEPT STATISTICS DESCRSTAT/P PARAMETRIC STANDUNITS/NORMA PROBDISTRIBUTION PROBABILITY T= 5 Computation D= 4 General Biological Sciences ***Calculator Necessary*** ***Statistical Table Necessary***Back to this chapter's Contents

Based upon item submitted by J. Inglis Multiple Choice CONFIDENCEINTERV TYPE1ERROR ESTIMATION CONCEPT STATISTICS T= 5 Comprehension D= 4 GeneralBack to this chapter's Contents

Based upon item submitted by J. Inglis Multiple Choice CONFIDENCEINTERV STANDERROROFMEAN TYPE1ERROR ESTIMATION CONCEPT STATISTICS DESCRSTAT/P PARAMETRIC T= 5 Computation D= 4 General ***Statistical Table Necessary***Back to this chapter's Contents

Based upon item submitted by D. Kleinbaum - Univ of North Carolina True/False CONFIDENCEINTERV SIMPLE/CI ESTIMATION CONCEPT STATISTICS T= 2 Comprehension D= 5 GeneralBack to this chapter's Contents

Based upon item submitted by D. Kleinbaum - Univ of North Carolina True/False CONFIDENCEINTERV SIMPLE/CI ESTIMATION CONCEPT STATISTICS T= 2 Comprehension D= 5 GeneralBack to this chapter's Contents

Based upon item submitted by F. J. Samaniego - UC Davis Multiple Choice ***Calculus Necessary*** SIMPLE/CI CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS T= 5 Computation D= 3 General ***Statistical Table Necessary***Back to this chapter's Contents

Item is still being reviewed Multiple Choice SIMPLE/CI CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS T= 5 Application Computation D= 3 General ***Statistical Table Necessary***Back to this chapter's Contents

Item is still being reviewed Multiple Choice SIMPLE/CI STANDARDDEVIATIO SIMPLEDATASET I650I CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS DESCRSTAT/P PARAMETRIC T= 5 Application D= 5 General ***Statistical Table Necessary***Back to this chapter's Contents

Based upon item submitted by R. L. Stout & R. M. Paolino - Brown Multiple Choice SIMPLE/CI OTHER/T TDISTRIBUTION CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS TTEST PARAMETRIC PROBDISTRIBUTION PROBABILITY T= 5 Comprehension D= 5 PsychologyBack to this chapter's Contents

Item is still being reviewed Multiple Choice SIMPLE/CI CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS T= 5 Computation D= 1 General ***Statistical Table Necessary***Back to this chapter's Contents

Item is still being reviewed Multiple Choice SIMPLE/CI MEAN CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS DESCRSTAT/P PARAMETRIC T= 2 Analysis D= 2 General ***Statistical Table Necessary***Back to this chapter's Contents

Based upon item submitted by F. J. Samaniego - UC Davis Multiple Choice SIMPLE/CI CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS T= 5 Comprehension D= 3 General ***Statistical Table Necessary***Back to this chapter's Contents

Item is still being reviewed Multiple Choice SIMPLE/CI CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS T= 5 Comprehension D= 3 General ***Statistical Table Necessary***Back to this chapter's Contents

Based upon item submitted by B. Weir - N. C. State & Massey Univ. Multiple Choice SIMPLE/CI CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS T= 2 Comprehension D= 2 GeneralBack to this chapter's Contents

Based upon item submitted by A. Bugbee - UNH Multiple Choice SAMPLESIZE SIMPLE/CI STANDERROROFMEAN SAMPLING STATISTICS CONFIDENCEINTERV ESTIMATION CONCEPT DESCRSTAT/P PARAMETRIC T= 2 Comprehension D= 2 GeneralBack to this chapter's Contents

Item is still being reviewed Multiple Choice PROPORTION SIMPLE/CI DESCRSTAT/P PARAMETRIC STATISTICS CONFIDENCEINTERV ESTIMATION CONCEPT T= 5 Computation D= 4 Business General ***Statistical Table Necessary***Back to this chapter's Contents

Item is still being reviewed Multiple Choice VARIANCE SIMPLE/CI TYPE1ERROR SAMPLESIZE DESCRSTAT/P PARAMETRIC STATISTICS CONFIDENCEINTERV ESTIMATION CONCEPT SAMPLING T= 2 Comprehension D= 3 GeneralBack to this chapter's Contents

Based upon item submitted by F. J. Samaniego - UC Davis Multiple Choice SIMPLE/CI CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS T= 5 Computation D= 3 General ***Statistical Table Necessary***Back to this chapter's Contents

Item is still being reviewed Multiple Choice SIMPLE/CI PROPORTION CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS DESCRSTAT/P PARAMETRIC T= 5 Comprehension D= 3 General ***Multiple Parts*** ***Statistical Table Necessary***Back to this chapter's Contents

Based upon item submitted by F. J. Samaniego - UC Davis Multiple Choice PROPORTION SIMPLE/CI DESCRSTAT/P PARAMETRIC STATISTICS CONFIDENCEINTERV ESTIMATION CONCEPT T= 5 Computation D= 3 General ***Statistical Table Necessary***Back to this chapter's Contents

Based upon item submitted by F. J. Samaniego - UC Davis Multiple Choice SIMPLE/CI CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS T=10 Application D= 4 General ***Statistical Table Necessary***Back to this chapter's Contents

Based upon item submitted by F. J. Samaniego - UC Davis Multiple Choice SIMPLE/CI CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS T= 5 Computation D= 2 General ***Statistical Table Necessary***Back to this chapter's Contents

Based upon item submitted by F. J. Samaniego - UC Davis Multiple Choice SIMPLE/CI TDISTRIBUTION CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS PROBDISTRIBUTION PROBABILITY T= 5 Computation D= 3 General ***Statistical Table Necessary***Back to this chapter's Contents

Based upon item submitted by F. J. Samaniego - UC Davis Multiple Choice SIMPLE/CI VARIANCE STANDERROROFMEAN ZSCORE CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS DESCRSTAT/P PARAMETRIC STANDUNITS/NORMA PROBDISTRIBUTION PROBABILITY T=10 Computation D= 3 General ***Calculator Necessary*** ***Statistical Table Necessary***Back to this chapter's Contents

Based upon item submitted by F. J. Samaniego - UC Davis Multiple Choice SIMPLE/CI TSCORE VARIANCE STANDERROROFMEAN CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS STANDUNITS/NORMA PROBDISTRIBUTION PROBABILITY DESCRSTAT/P PARAMETRIC T=10 Computation D= 3 General ***Calculator Necessary*** ***Statistical Table Necessary***Back to this chapter's Contents

Based upon item submitted by F. J. Samaniego - UC Davis Multiple Choice SIMPLE/CI PROPORTION CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS DESCRSTAT/P PARAMETRIC T= 5 Computation D= 3 General ***Statistical Table Necessary***Back to this chapter's Contents

Based upon item submitted by F. J. Samaniego - UC Davis Multiple Choice SIMPLE/CI STANDERROROFMEAN CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS DESCRSTAT/P PARAMETRIC T= 5 Computation D= 3 General ***Statistical Table Necessary***Back to this chapter's Contents

Item is still being reviewed Multiple Choice SIMPLE/CI CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS T= 5 Application D= 5 Biological Sciences Economics ***Statistical Table Necessary***Back to this chapter's Contents

Based upon item submitted by D. Kleinbaum - Univ of North Carolina Multiple Choice SIMPLE/CI CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS T= 2 Comprehension D= 3 General ***Statistical Table Necessary***Back to this chapter's Contents

Based upon item submitted by D. Kleinbaum - Univ of North Carolina Multiple Choice SIMPLE/CI CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS T= 5 Application D= 5 General ***Statistical Table Necessary***Back to this chapter's Contents

Item is still being reviewed Multiple Choice SIMPLE/CI MEAN CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS DESCRSTAT/P PARAMETRIC T= 2 Comprehension D= 3 GeneralBack to this chapter's Contents

Item is still being reviewed Multiple Choice SIMPLE/CI STANDERROROFMEAN MEAN CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS DESCRSTAT/P PARAMETRIC T=10 Computation D= 3 General Psychology ***Statistical Table Necessary***Back to this chapter's Contents

Item is still being reviewed Multiple Choice SIMPLE/CI MEAN STANDERROROFMEAN CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS DESCRSTAT/P PARAMETRIC T= 2 Computation D= 2 General ***Statistical Table Necessary***Back to this chapter's Contents

Item is still being reviewed Multiple Choice SIMPLE/CI CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS T= 2 Computation D= 3 General ***Statistical Table Necessary***Back to this chapter's Contents

Item is still being reviewed Multiple Choice SIMPLE/CI CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS T= 2 Comprehension D= 3 General EducationBack to this chapter's Contents

Item is still being reviewed Multiple Choice SIMPLE/CI CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS T= 2 Comprehension D= 3 General EducationBack to this chapter's Contents

Item is still being reviewed Multiple Choice SIMPLE/CI CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS T= 5 Application D= 4 General Education ***Calculator Necessary***Back to this chapter's Contents

Item is still being reviewed Multiple Choice SIMPLE/CI CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS T= 2 Comprehension D= 3 GeneralBack to this chapter's Contents

Based upon item submitted by CIID - UNH Numerical Answer SIMPLE/CI MEAN CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS DESCRSTAT/P PARAMETRIC T= 5 Application Comprehension D= 3 General ***Calculator Necessary*** ***Multiple Parts*** ***Statistical Table Necessary*** 1 Page 1470Back to this chapter's Contents

Item is still being reviewed Numerical Answer SIMPLE/CI CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS T= 5 Application D= 4 Economics General ***Statistical Table Necessary***Back to this chapter's Contents

Item is still being reviewed Numerical Answer PROPORTION SIMPLE/CI DESCRSTAT/P PARAMETRIC STATISTICS CONFIDENCEINTERV ESTIMATION CONCEPT T= 5 Computation D= 2 General ***Calculator Necessary*** ***Statistical Table Necessary***Back to this chapter's Contents

Item is still being reviewed Numerical Answer SIMPLE/CI CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS T= 5 Computation D= 1 General ***Calculator Necessary*** ***Statistical Table Necessary***Back to this chapter's Contents

Based upon item submitted by C. R. Buncher - Cincinnati Numerical Answer SIMPLE/CI TDISTRIBUTION CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS PROBDISTRIBUTION PROBABILITY T= 5 Comprehension Computation D= 3 General Biological Sciences Natural Sciences ***Statistical Table Necessary***Back to this chapter's Contents

Based upon item submitted by G. Miaoulis - Wright State Univ. Numerical Answer SIMPLE/CI STANDERROROFMEAN MEAN I650I CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS DESCRSTAT/P PARAMETRIC T= 5 Application Comprehension D= 4 General Business ***Calculator Necessary*** ***Statistical Table Necessary***Back to this chapter's Contents

Item is still being reviewed Numerical Answer PROPORTION SIMPLE/CI STANDERROR/OTHER DESCRSTAT/P PARAMETRIC STATISTICS CONFIDENCEINTERV ESTIMATION CONCEPT T= 5 Computation D= 3 Business General ***Calculator Necessary*** ***Statistical Table Necessary***Back to this chapter's Contents

Based upon item submitted by W. J. Hall - Univ. of Rochester Numerical Answer SIMPLE/CI STANDERROROFMEAN I650I CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS DESCRSTAT/P PARAMETRIC T= 5 Computation Comprehension D= 4 General ***Statistical Table Necessary***Back to this chapter's Contents

Based upon item submitted by W. J. Hall - Univ. of Rochester Numerical Answer PROPORTION SIMPLE/CI DESCRSTAT/P PARAMETRIC STATISTICS CONFIDENCEINTERV ESTIMATION CONCEPT T=10 Comprehension Computation D= 4 General ***Calculator Necessary*** ***Statistical Table Necessary***Back to this chapter's Contents

Based upon item submitted by S. Selvin - UC Berkeley Numerical Answer SIMPLE/CI CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS T= 5 Computation D= 3 Biological Sciences General ***Statistical Table Necessary***Back to this chapter's Contents

Based upon item submitted by K. Gorowara - Wright State U. Numerical Answer SIMPLE/CI CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS T= 5 Application D= 6 General Business ***Calculator Necessary***Back to this chapter's Contents

Based upon item submitted by J. Warren - UNH Numerical Answer SIMPLE/CI PROPORTION I650I CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS DESCRSTAT/P PARAMETRIC T= 5 Comprehension D= 4 General ***Calculator Necessary*** ***Statistical Table Necessary***Back to this chapter's Contents

Based upon item submitted by J. Warren - UNH Numerical Answer SIMPLE/CI STANDERROROFMEAN MEAN I650I CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS DESCRSTAT/P PARAMETRIC T= 5 Computation D= 4 General ***Statistical Table Necessary***Back to this chapter's Contents

Item is still being reviewed Numerical Answer PROPORTION SIMPLE/CI DESCRSTAT/P PARAMETRIC STATISTICS CONFIDENCEINTERV ESTIMATION CONCEPT T= 5 Computation D= 4 General ***Calculator Necessary*** ***Statistical Table Necessary***Back to this chapter's Contents

Item is still being reviewed Numerical Answer SIMPLE/CI MEAN CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS DESCRSTAT/P PARAMETRIC T=10 Computation Comprehension D= 4 General ***Multiple Parts*** ***Statistical Table Necessary***Back to this chapter's Contents

Item is still being reviewed Numerical Answer SIMPLE/CI CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS T= 5 Comprehension Computation D= 2 General ***Multiple Parts*** ***Statistical Table Necessary***Back to this chapter's Contents

Item is still being reviewed Numerical Answer SIMPLE/CI CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS T= 5 Computation Comprehension D= 4 General Business Economics ***Calculator Necessary*** ***Multiple Parts*** ***Statistical Table Necessary***Back to this chapter's Contents

Item is still being reviewed Numerical Answer SIMPLE/CI MEAN CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS DESCRSTAT/P PARAMETRIC T= 5 Computation D= 3 General Social Sciences Sociology ***Calculator Necessary*** ***Statistical Table Necessary***Back to this chapter's Contents

Item is still being reviewed Numerical Answer SIMPLE/CI MEAN CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS DESCRSTAT/P PARAMETRIC T= 5 Computation D= 3 General Psychology Social Sciences ***Calculator Necessary*** ***Statistical Table Necessary***Back to this chapter's Contents

Item is still being reviewed Numerical Answer SIMPLE/CI CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS T= 5 Computation D= 2 Sociology General ***Calculator Necessary*** ***Statistical Table Necessary***Back to this chapter's Contents

Item is still being reviewed Numerical Answer SIMPLE/CI MEAN CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS DESCRSTAT/P PARAMETRIC T= 5 Computation D= 3 General Sociology Social Sciences ***Calculator Necessary*** ***Statistical Table Necessary***Back to this chapter's Contents

Based upon item submitted by R. F. Tate - U. of Oregon Short Answer SIMPLE/CI STANDERROROFMEAN MEAN I650I CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS DESCRSTAT/P PARAMETRIC T= 5 Comprehension Computation D= 5 General ***Multiple Parts*** ***Statistical Table Necessary***Back to this chapter's Contents

Based upon item submitted by L. J. Tashman - U. of Vermont Short Answer SIMPLE/CI PROPORTION CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS DESCRSTAT/P PARAMETRIC T=10 Computation D= 4 GeneralBack to this chapter's Contents

Based upon item submitted by J. Warren - UNH Short Answer PROPORTION SIMPLE/CI I650I DESCRSTAT/P PARAMETRIC STATISTICS CONFIDENCEINTERV ESTIMATION CONCEPT T= 5 Comprehension Computation D= 4 General Social SciencesBack to this chapter's Contents

Based upon item submitted by J. Warren - UNH Short Answer SIMPLE/CI PROPORTION TYPE1ERROR I650I CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS DESCRSTAT/P PARAMETRIC T= 5 Comprehension D= 4 General Social SciencesBack to this chapter's Contents

Based upon item submitted by J. Warren - UNH Short Answer SIMPLE/CI STANDERROROFMEAN TDISTRIBUTION MEAN SIMPLEDATASET I650I CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS DESCRSTAT/P PARAMETRIC PROBDISTRIBUTION PROBABILITY MISCELLANEOUS I650/TEMPORARY T=10 Application D= 6 General Business ***Calculator Necessary*** ***Multiple Parts*** ***Statistical Table Necessary***Back to this chapter's Contents

Item is still being reviewed True/False SIMPLE/CI CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS T= 2 Computation D= 6 GeneralBack to this chapter's Contents

Based upon item submitted by R. Pruzek - SUNY at Albany True/False SIMPLE/CI TYPE1ERROR I650I CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS T= 2 Comprehension D= 2 GeneralBack to this chapter's Contents

Based upon item submitted by R. Pruzek - SUNY at Albany True/False SIMPLE/CI TYPE1ERROR TYPE2ERROR CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS T= 5 Comprehension D= 3 GeneralBack to this chapter's Contents

Based upon item submitted by R. Pruzek - SUNY at Albany True/False SIMPLE/CI CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS T= 2 Comprehension D= 2 GeneralBack to this chapter's Contents

Based upon item submitted by W. J. Hall - Univ. of Rochester True/False SIMPLE/CI ESTIMATION/OTHER CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS T= 2 Comprehension D= 2 GeneralBack to this chapter's Contents

Item is still being reviewed True/False SIMPLE/CI SAMPLESIZE TYPE1ERROR CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS SAMPLING T= 2 Comprehension D= 2 GeneralBack to this chapter's Contents

Item is still being reviewed Multiple Choice PROPORTION OTHER/CI ESTIMATION/OTHER OTHER/Z DESCRSTAT/P PARAMETRIC STATISTICS CONFIDENCEINTERV ESTIMATION CONCEPT ZTEST T= 5 Computation D= 5 General Education Social Sciences ***Statistical Table Necessary***Back to this chapter's Contents

Item is still being reviewed Multiple Choice OTHER/CI MEAN CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS DESCRSTAT/P PARAMETRIC T= 2 Comprehension D= 4 General EducationBack to this chapter's Contents

Based upon item submitted by R. Pruzek - SUNY at Albany True/False OTHER/CI CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS T= 5 Comprehension D= 3 GeneralBack to this chapter's Contents

Based upon item submitted by W. J. Hall - Univ. of Rochester True/False OTHER/CI CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS T= 2 Comprehension D= 4 GeneralBack to this chapter's Contents

Based upon item submitted by J. Warren - UNH Definition OTHER/CI I650I CONFIDENCEINTERV ESTIMATION CONCEPT STATISTICS T= 5 Comprehension D= 3 GeneralBack to this chapter's Contents

Item is still being reviewed Multiple Choice ESTIMATION/OTHER SAMPLE ESTIMATION CONCEPT STATISTICS SAMPLING T= 2 Comprehension D= 2 GeneralBack to this chapter's Contents

Item is still being reviewed Multiple Choice CENTRALLIMITTHM SAMPLESIZE SAMPLINGDISTRIB CONCEPT STATISTICS SAMPLING T= 2 Comprehension D= 3 GeneralBack to this chapter's Contents

Based upon item submitted by W. J. Hall - Univ. of Rochester Multiple Choice CENTRALLIMITTHM VARIANCE/OTHER CONCEPT STATISTICS DESCRSTAT/P PARAMETRIC T= 2 Comprehension D= 2 GeneralBack to this chapter's Contents

Item is still being reviewed Multiple Choice CENTRALLIMITTHM CONCEPT STATISTICS T= 2 Comprehension D= 3 GeneralBack to this chapter's Contents

Item is still being reviewed Multiple Choice CENTRALLIMITTHM STANDERROROFMEAN CONCEPT STATISTICS DESCRSTAT/P PARAMETRIC T= 2 Comprehension D= 3 GeneralBack to this chapter's Contents

Item is still being reviewed Multiple Choice CENTRALLIMITTHM CONCEPT STATISTICS T= 2 Comprehension D= 6 General EducationBack to this chapter's Contents

Item is still being reviewed Multiple Choice CENTRALLIMITTHM SAMPLINGDISTRIB MEAN CONCEPT STATISTICS SAMPLING DESCRSTAT/P PARAMETRIC T= 2 Comprehension D= 3 GeneralBack to this chapter's Contents

Item is still being reviewed Numerical Answer CENTRALLIMITTHM CONCEPT STATISTICS T= 5 Application D= 7 General ***Statistical Table Necessary***Back to this chapter's Contents

Item is still being reviewed Short Answer SAMPLINGDISTRIB SAMPLESIZE CENTRALLIMITTHM SAMPLING STATISTICS CONCEPT T= 5 Comprehension D= 4 General ***Multiple Parts***Back to this chapter's Contents

Based upon item submitted by L. J. Tashman - U. of Vermont Short Answer CENTRALLIMITTHM SAMPLINGDISTRIB STANDERROROFMEAN I650I CONCEPT STATISTICS SAMPLING DESCRSTAT/P PARAMETRIC T=10 Comprehension D= 4 General ***Multiple Parts***Back to this chapter's Contents

Based upon item submitted by S. Selvin - UC Berkeley Short Answer CENTRALLIMITTHM CONCEPT STATISTICS T= 5 Comprehension D= 3 General ***Multiple Parts***Back to this chapter's Contents

Based upon item submitted by H. B. Christensen - BYU True/False CENTRALLIMITTHM CONCEPT STATISTICS T= 2 Computation D= 1 GeneralBack to this chapter's Contents

Based upon item submitted by H. B. Christensen - BYU True/False CENTRALLIMITTHM CONCEPT STATISTICS T= 2 Comprehension D= 2 GeneralBack to this chapter's Contents

Item is still being reviewed True/False CENTRALLIMITTHM CONCEPT STATISTICS T= 2 Comprehension D= 3 GeneralBack to this chapter's Contents

Based upon item submitted by R. Pruzek - SUNY at Albany True/False MEAN CENTRALLIMITTHM STANDERROROFMEAN SAMPDIST/C SAMPLESIZE DESCRSTAT/P PARAMETRIC STATISTICS CONCEPT ESTIMATION SAMPLING T= 5 Comprehension D= 4 GeneralBack to this chapter's Contents

Item is still being reviewed True/False CENTRALLIMITTHM CONCEPT STATISTICS T= 5 Comprehension D= 3 GeneralBack to this chapter's Contents

Item is still being reviewed True/False CENTRALLIMITTHM CONCEPT STATISTICS T= 2 Computation D= 1 GeneralBack to this chapter's Contents

Item is still being reviewed True/False SAMPLINGDISTRIB CENTRALLIMITTHM SAMPLING STATISTICS CONCEPT T= 2 Comprehension D= 3 GeneralBack to this chapter's Contents

Item is still being reviewed True/False TYPE1ERROR CONCEPT STATISTICS T= 2 Comprehension D= 2 GeneralBack to this chapter's Contents

Based upon item submitted by W. Federer - Cornell Fill-in SCOPEOFINFERENCE SAMPLE CONCEPT STATISTICS SAMPLING T= 2 Comprehension D= 3 GeneralBack to this chapter's Contents

Item is still being reviewed True/False PROPORTION MEAN DESCRSTAT/P PARAMETRIC STATISTICS T= 2 Comprehension D= 4 GeneralBack to this chapter's Contents

Item is still being reviewed Multiple Choice SAMPLINGDISTRIB MEAN VARIANCE SAMPLING STATISTICS DESCRSTAT/P PARAMETRIC T= 2 Computation Comprehension D= 2 GeneralBack to this chapter's Contents

Item is still being reviewed Multiple Choice SAMPLINGDISTRIB MEAN SAMPLING STATISTICS DESCRSTAT/P PARAMETRIC T= 2 Comprehension D= 3 General EducationBack to this chapter's Contents

Based upon item submitted by R. Pruzek - SUNY at Albany True/False MEAN SAMPLINGDISTRIB DESCRSTAT/P PARAMETRIC STATISTICS SAMPLING T= 2 Comprehension D= 2 GeneralBack to this chapter's Contents

Item is still being reviewed True/False MEAN STANDARDDEVIATIO DESCRSTAT/P PARAMETRIC STATISTICS T= 2 Comprehension D= 3 GeneralBack to this chapter's Contents

Item is still being reviewed True/False SAMPLINGDISTRIB STANDERROROFMEAN MEAN SAMPLING STATISTICS DESCRSTAT/P PARAMETRIC T= 2 Comprehension Application D= 3 GeneralBack to this chapter's Contents

Item is still being reviewed Multiple Choice VARIABILITY/P VARIANCE/OTHER DESCRSTAT/P PARAMETRIC STATISTICS T= 2 Computation Comprehension D= 4 GeneralBack to this chapter's Contents

Based upon item submitted by A. Bugbee - UNH Short Answer VARIABILITY/P STANDARDDEVIATIO I650I DESCRSTAT/P PARAMETRIC STATISTICS T= 5 Comprehension D= 2 GeneralBack to this chapter's Contents

Based upon item submitted by J. Warren - UNH Short Answer VARIABILITY/P STANDERROR/OTHER I650I DESCRSTAT/P PARAMETRIC STATISTICS T= 5 Comprehension D= 3 GeneralBack to this chapter's Contents

Item is still being reviewed Short Answer STANDARDDEVIATIO STANDERROROFMEAN DESCRSTAT/P PARAMETRIC STATISTICS T= 2 Comprehension D= 3 GeneralBack to this chapter's Contents

Based upon item submitted by R. Pruzek - SUNY at Albany True/False STANDERROROFMEAN STANDARDDEVIATIO SAMPLESIZE SAMPLINGDISTRIB DESCRSTAT/P PARAMETRIC STATISTICS SAMPLING T= 5 Comprehension D= 4 GeneralBack to this chapter's Contents

Based upon item submitted by W. J. Hall - Univ. of Rochester True/False STANDARDERROR STANDARDDEVIATIO SAMPLINGDISTRIB PARAMETRIC STATISTICS DESCRSTAT/P SAMPLING T= 2 Comprehension D= 3 GeneralBack to this chapter's Contents

Item is still being reviewed True/False STANDARDDEVIATIO STANDERROR/OTHER DESCRSTAT/P PARAMETRIC STATISTICS T= 2 Comprehension D= 2 GeneralBack to this chapter's Contents

Item is still being reviewed Multiple Choice VARIANCE DESCRSTAT/P PARAMETRIC STATISTICS T= 5 Computation D= 2 GeneralBack to this chapter's Contents

Based upon item submitted by S. Selvin - UC Berkeley Numerical Answer VARIANCE STANDERROR/OTHER OTHER/RV DESCRSTAT/P PARAMETRIC STATISTICS RANDOMVARIABLES PROBABILITY T=10 Computation Comprehension D= 6 General ***Calculator Necessary*** ***Multiple Parts***Back to this chapter's Contents

Based upon item submitted by J. Warren - UNH Numerical Answer VARIANCE VARIANCE/OTHER STANDERROROFMEAN I650I DESCRSTAT/P PARAMETRIC STATISTICS T= 5 Comprehension D= 3 General ***Multiple Parts***Back to this chapter's Contents

Based upon item submitted by R. Pruzek - SUNY at Albany True/False VARIANCE SAMPLINGDISTRIB DESCRSTAT/P PARAMETRIC STATISTICS SAMPLING T= 2 Comprehension D= 3 GeneralBack to this chapter's Contents

Item is still being reviewed Multiple Choice STANDERROROFMEAN DESCRSTAT/P PARAMETRIC STATISTICS T= 2 Comprehension D= 2 GeneralBack to this chapter's Contents

Based upon item submitted by B. Weir - N. C. State & Massey Univ. Multiple Choice STANDERROROFMEAN DESCRSTAT/P PARAMETRIC STATISTICS T= 2 Computation Comprehension D= 2 GeneralBack to this chapter's Contents

Item is still being reviewed Multiple Choice STANDERROROFMEAN DESCRSTAT/P PARAMETRIC STATISTICS T= 2 Computation Comprehension D= 2 General ***Calculator Necessary*** ***Statistical Table Necessary***Back to this chapter's Contents

Based upon item submitted by R. F. Tate - U. of Oregon Multiple Choice STANDERROROFMEAN SAMPLINGDISTRIB CENTRALLIMITTHM I650I DESCRSTAT/P PARAMETRIC STATISTICS SAMPLING CONCEPT T= 2 Computation Comprehension D= 3 GeneralBack to this chapter's Contents

Based upon item submitted by R. Pruzek - SUNY at Albany Multiple Choice STANDERROROFMEAN SAMPLINGDISTRIB I650I DESCRSTAT/P PARAMETRIC STATISTICS SAMPLING T= 2 Comprehension D= 2 GeneralBack to this chapter's Contents

Based upon item submitted by W. J. Hall - Univ. of Rochester Multiple Choice STANDERROROFMEAN SAMPLESIZE DESCRSTAT/P PARAMETRIC STATISTICS SAMPLING T= 2 Comprehension D= 1 GeneralBack to this chapter's Contents

Item is still being reviewed Multiple Choice STANDERROROFMEAN DESCRSTAT/P PARAMETRIC STATISTICS T= 2 Comprehension D= 2 GeneralBack to this chapter's Contents

Item is still being reviewed Multiple Choice STANDERROROFMEAN DESCRSTAT/P PARAMETRIC STATISTICS T= 2 Comprehension D= 2 GeneralBack to this chapter's Contents

Item is still being reviewed Multiple Choice STANDERROROFMEAN DESCRSTAT/P PARAMETRIC STATISTICS T= 2 Computation D= 2 General ***Calculator Necessary***Back to this chapter's Contents

Item is still being reviewed Multiple Choice STANDERROROFMEAN DESCRSTAT/P PARAMETRIC STATISTICS T= 2 Comprehension D= 6 General EducationBack to this chapter's Contents

Item is still being reviewed Multiple Choice SAMPLINGDISTRIB STANDERROROFMEAN SAMPLING STATISTICS DESCRSTAT/P PARAMETRIC T= 2 Comprehension D= 3 GeneralBack to this chapter's Contents

Item is still being reviewed Multiple Choice STANDERROROFMEAN DESCRSTAT/P PARAMETRIC STATISTICS T= 2 Comprehension D= 4 General EducationBack to this chapter's Contents

Item is still being reviewed Multiple Choice STANDERROROFMEAN DESCRSTAT/P PARAMETRIC STATISTICS T= 2 Comprehension D= 5 General EducationBack to this chapter's Contents

Based upon item submitted by J. Warren - UNH Essay ***Calculus Necessary*** STANDERROROFMEAN I650I DESCRSTAT/P PARAMETRIC STATISTICS T= 5 Comprehension D= 5 General GeneralBack to this chapter's Contents

Item is still being reviewed True/False STANDERROROFMEAN DESCRSTAT/P PARAMETRIC STATISTICS T= 2 Comprehension D= 3 GeneralBack to this chapter's Contents

Based upon item submitted by R. Pruzek - SUNY at Albany True/False STANDERROROFMEAN DESCRSTAT/P PARAMETRIC STATISTICS T= 5 Comprehension D= 3 GeneralBack to this chapter's Contents

Based upon item submitted by R. Pruzek - SUNY at Albany True/False STANDERROROFMEAN VARIANCE/OTHER SAMPDIST/C CENTRALLIMITTHM DESCRSTAT/P PARAMETRIC STATISTICS ESTIMATION CONCEPT T= 5 Comprehension D= 3 GeneralBack to this chapter's Contents

Based upon item submitted by W. J. Hall - Univ. of Rochester True/False SAMPLESIZE STANDERROROFMEAN SAMPLINGERROR SAMPLING STATISTICS DESCRSTAT/P PARAMETRIC T= 2 Comprehension D= 2 GeneralBack to this chapter's Contents

Based upon item submitted by J. L. Mickey -UCLA True/False STANDERROROFMEAN DESCRSTAT/P PARAMETRIC STATISTICS T= 2 Comprehension D= 3 GeneralBack to this chapter's Contents

Item is still being reviewed Multiple Choice STANDERROR/OTHER MEDIAN DESCRSTAT/P PARAMETRIC STATISTICS DESCRSTAT/NP NONPARAMETRIC T= 2 Comprehension D= 4 General EducationBack to this chapter's Contents

Item is still being reviewed Fill-in SAMPLINGDISTRIB STANDERROR/OTHER SAMPLING STATISTICS DESCRSTAT/P PARAMETRIC T= 2 Comprehension D= 3 GeneralBack to this chapter's Contents

Item is still being reviewed Multiple Choice SAMPLINGDISTRIB VARIANCE/OTHER SAMPLING STATISTICS DESCRSTAT/P PARAMETRIC T= 2 Comprehension D= 2 GeneralBack to this chapter's Contents

Item is still being reviewed Multiple Choice SAMPLINGDISTRIB SAMPLING STATISTICS T= 2 Comprehension D= 2 GeneralBack to this chapter's Contents

Item is still being reviewed Multiple Choice SAMPLINGDISTRIB SAMPLING STATISTICS T= 2 Comprehension D= 3 General EducationBack to this chapter's Contents

Item is still being reviewed Multiple Choice SAMPLINGDISTRIB SAMPLING STATISTICS T= 2 Comprehension D= 2 GeneralBack to this chapter's Contents

Item is still being reviewed Short Answer SAMPLE SAMPLINGDISTRIB SAMPLING STATISTICS T= 5 Comprehension D= 3 GeneralBack to this chapter's Contents

Based upon item submitted by D. Kleinbaum - Univ of North Carolina True/False SAMPLESIZE SAMPLINGDISTRIB STANDERROROFMEAN SAMPLING STATISTICS DESCRSTAT/P PARAMETRIC T= 2 Computation Comprehension D= 2 GeneralBack to this chapter's Contents

Item is still being reviewed True/False SAMPLINGDISTRIB SAMPLING STATISTICS T= 2 Comprehension D= 4 GeneralBack to this chapter's Contents

Item is still being reviewed True/False SAMPLESIZE VARIABILITY/P SAMPLING STATISTICS DESCRSTAT/P PARAMETRIC T= 2 Comprehension D= 4 GeneralBack to this chapter's Contents

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