STAT 360: Regression Analysis

Fall 2014

Name: / Christopher Malone ("Malone")
Office: / Gildemeister 124C
Email: /
Phone: / (507)-457-2989
Office Hours: / My Schedule

(Optional) Text:

Applied Regression Including Computing and Graphics, R.D. Cook and S. Weisberg

Course Notes:

Most material for this course will be presented through a substantial set of course notes. These notes can be printed off from our course website. Additional resources may be made available whenever necessary.

Homework:

Homework assignments will be given throughout the semester. Questions on the assigned homework may be asked at the beginning of the each class period. I will collect manyof your homework assignments. I strongly encourage you to stay current in your homework assignments.Late homework assignments are accessed a 10% late penalty and are not be accepted after they are returned. In-class quizzes may be given at any time. These quizzes may or may not be announced.

Exams:

There will be one midterm exam and one final exam for this course. I will test your ability to make conclusions and/or extensions to current methods.More than likely these exams will consist of an in-class portion and an out-of-class portion. If you know you are going to miss an exam, the exam mustbe taken early. Makeup exams will not be given.

Grades:

Your grade will be determined by your performance on exams, quizzes, homework. My “target” for the number of points istwo exams = 250 pts and homework/quizzes = 200pts. I do no weighting, so a point is worth a point in this class. Your final grade will be determined using the following percentages.

Your Percentage / Grade
greater than 90% / A
80% - 89% / B
70% - 79% / C
60% - 69% / D
less than 60% / F

Computing:

We will be using a two statistical software packages in this course. The primary package will be JMP. WSU has a site license for this software package. The second package, R ( is an open-source package that is popular among statisticians.

Learning Outcomes:

  1. Students will be knowledgeable of the statistical methods presented, and be able to properly obtainand use these methods.
  2. Students will be able to answer real-world research questions with regression models and approach such problems with confidence.
  3. Students will be able to properly apply model diagnostics when solving a particular researchquestion.
  4. Students will be able to effectively communicate results to both non-statisticians and statisticians.
  5. Students will be able to use the software packages SAS and Arc for data analyses.

Topic Outline

  1. Review of Statistical Prerequisites
  1. Graphical Summaries
  2. Statistical Inference
  1. Introduction to Regression
  1. Conditional Distributions
  2. Mean & Variance Functions
  3. Smoothing
  4. Bivariate Distributions
  5. Two-Dimensional Plots
  1. Simple Linear Regression
  1. Model Equation & Assumptions
  2. Least Squares Estimation
  3. Inference for Regression Parameters
  4. ANOVA
  1. Model Comparison
  2. Diagnosing Assumption Violations
  3. Introduction to Multiple Linear Regression
  4. Model Equation & Assumptions
  5. Three-dimensional Plots
  6. Fitting the Model
  7. Fitting the Model using Matrix Computations
  8. Problems with Multiple Linear Regression
  9. Partial Coefficient of Determination and Correlation
  10. Inference for Linear Combinations of Regression Coefficients
  11. Regression Models with Categorical Predictors
  12. Fitting the Model
  13. Predictors with More Than Two Levels
  14. Model Selection
  15. R2
  16. Adjusted R2
  17. Mallow's C Statistic
  18. Forward, Backward and Stepwise Regression
  19. Outliers & Influential Observations
  20. Leverage
  21. Studentized Residuals
  22. Cook's D
  23. Remedial Measures
  24. Adding Higher Powered Terms
  25. Transforming the Predictor Variable
  26. Transforming the Response Variable
  27. Power Curve
  28. Box-Cox Method
  29. Weighted Least Squares
  30. Logistic Regression (Time Permitting)

Extras:

  • I encourage you to use a 3-ring binder for this class because class material will be a combination of note taking, handouts, and lots of computer output.
  • Attendance in mandatory. If you miss class, it is your responsibility to get the material and get yourself caught up.
  • If necessary, I reserve the right to make policy changes for this course as the semester progresses.

Academic Integrity Policy:

The WSU Undergraduate Catalog contains a full listing of policies and procedures pertaining to this issue: that both copying another student’s work and allowing someone to copy your work are clear violations of WSU’s Academic Integrity Policy. If there is reasonable evidence of copying another individual’s or group’s work, it will be construed as an act of plagiarism. The first occurrence of cheating will result in a score of zero on that specific homework assignment or exam portion; the second occurrence will result in failure of the course.