MATH 4548/6548 – Learning Outcomes

Title: Methods of Regression and Analysis of Variance

  • Students will understand how to conduct inferences and interpret tests for the slope, intercept, and the regression line in straight-line linear regression analysis.
  • Students will understand the relationship between the correlation coefficient and straight-line regression analysis.
  • Students will be able to conduct tests of hypothesis and construct confidence intervals for the correlation coefficient of one population and the difference between the correlation coefficients of two populations.
  • Students will understand and apply the assumptions concerning multiple regression analysis and the analysis of variance table for a multiple regression.
  • Students will be able to conduct test of hypothesis in a multiple regression such as the test for significant overall regression, partial and multiple partial F-tests.
  • Students will be able to compute multiple, partial and multiple partial correlation coefficient coefficients and conduct the corresponding F-tests.
  • Students will be able to determine interaction and confounding in regression.
  • Students will be able to apply regression diagnostics that include residual analysis to treat outliers and collinearity and scaling problems.
  • Students will be able to use dummy variables in the comparison two straight lines and in the comparison of four regression equations.
  • Students will be able to select the best regression equation using procedures such as the step-wise, forward elimination, the backward elimination and the all-possible regression procedures.
  • Students will be able to conduct one-way and two-way analysis of variance with multiple comparisons.

Assessment of Learning Outcomes

Problems based on the learning objectives will be assigned on a regular basis and may appear in a variety of contexts:

  • Quizzes are used to assess students’ understanding of newly presented topics and previously covered material.
  • Programming assignments in SAS are designed to assess the students’ ability to analyze the various data output and to draw inferences from the outputs.
  • Take-home Exams are course projects. They provide an opportunity for students to work on more complicated problems that involve large data sets and to effectively apply various methods learned in class to solve these problems. The take-home exams are also used to determine whether the students learned the statistical methods.
  • Presentations are designed to assess the students’ ability to express their thought clearly and to demonstrate their understanding of the ideas covered.
  • Homework problems are important and essential in the course. They provide a tool for immediate assessment of students’ understanding of basic concepts and theory underlying multiple regressions.