Chapter 3: Simple Linear Regression (Part 1)

3.1 The Simple Linear Regression Model 79
          Using Temperature to Forecast Natural Gas Consumption
                          Time Series Data Treated as Cross Sectional
                           "Causal" model
          Conditional mean, y intercept, slope, error

           Using Value of House to Forecast Upkeep Expenditures

3.2 The Least Squares Point Estimates 88
           Skim page 88 to the middle of  page 90
           Point Prediction, Residuals, Sum of Squared Residuals
            Least Squares Prediction Equation   (b versus beta)

3.3 Point Estimates and Point Predictions 92
           Interpolation Within the Experimental Region
           Extrapolation Outside the Experimental Region
                        Natural Gas Consumption when the temperature is 0 or 100
        
3.4 Model Assumptions and the Standard Error 96
          Epsilon = the different potential effects on y of all factors other than x
                     Specifically, the way these factors move y above or below its conditional mean
          Regression Assumptions: really assumptions about epsilon
                     Zero mean, Constant Variance, Normal Distribution, Independence
          Be able to explain Figure 3.8 on page 98 to bosses, clients, politicians, news reporters
           Mean Squared Error, Standard Error, Degrees of Freedom
                     Standard error estimates the standard deviation of y around the true conditional mean



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