This site will be updated as necessary throughout the semester. To navigate the site you can use the buttons on the left. If you have any additional items you would like to see added to the course website let me know and I will try to accommodate.
The web-based course syllabus is presented below. The Word version of the syllabus is available here.
or by appointment.
1) Applied Linear Regression (4th Edition) by Sanford Weisberg.
On Amazon you can rent it for $32, buy used it for around $64, or buy new for about $100. An e-book is available directly from Wiley Publishing for $113. Also see D2L for information about the 3rd edition of this text!
R Primer to accompany textbook: Computing Primer for ALR (4th ed.) by Sanford Weisberg.
2) Applied Regression Including Computing and Graphics by Dennis Cook and Sanford Weisberg
This one you can currently score used copies of for very cheap on Amazon.com (about 11 copies available when I checked) using the link above. If you cannot score a cheap used copy, don't worry - perhaps you can share with someone else in class.
|Homework and Projects||75 %|
|Final Exam||25 %|
Homework will consist of problems assigned from the text and others that I will write.All assignments will require the use of statistical software (JMP 13 Pro or R/R-Studio). All assignments must be submitted in Microsoft Word (.doc or .docx) format into the appropriate dropbox on D2L. You should cleanly incorporate relevant computer output into your assignments. I will demonstrate the process of cleanly capturing output from JMP and R-Studio in class and will likely put together a handout explaining the process. Working in groups of two on homework is acceptable. All course assignments must be turned in on the assigned due date and no late homework will be accepted!
We will be using a variety of statistical software in this course. We will primarily be using JMP 13 Pro and R/R-Studio. R is a free statistical programming environment developed by researchers at the University of Auckland. R-Studio is an integrated development environment (IDE) for R. It includes a console, syntax-highlighting editor that supports direct code execution, as well as tools for plotting, history, debugging, and workspace management.
Tentative Course Outline and Course Notes:
PART I - Introduction (1-2 weeks)
|0 - Brief Introduction to Regression Analysis|
|1 - Univariate Analysis of the Response Variable (review of inference for a single numeric variable)|
|2 - Introduction to Regression and Conditional Distributions|
|3 - Introduction to Smoothing - Nonparametric Regression|
|4 - Bivariate Normality and Regression|
PART II - Tools (8 - 10 weeks)
|5 - Simple Linear Regression (SLR)|
|6 - SLR using Matrices|
|7 - Case Diagnostics for SLR: Influence and Outliers|
|8 - Introduction to Multiple Linear Regression (MLR)|
|9 - Multiple Regression - Predictors and Terms|
|10 - Understanding Coefficients in MLR|
|11 - Factors and Interactions|
|12 - Diagnostics I for MLR: Curvature and Nonconstant Variance|
|13 - Diagnostics II for MLR: Influence, Outliers, and Collinearity|
|14 - Response Transformations|
|15 - Predictor Transformations|
|16 - Model Selection and Prediction Accuracy|
|17 - Weighted Least Squares (WLS) - modeling nonconstant variance and robust regression|
PART III - Introduction to Generalized Linear Models and Nonlinear Regression (2 - 3 weeks)
|18 - Binomial/Logistic Regression|
|19 - Poisson Regression (forthcoming)|
|21 - Nonlinear Regression (forthcoming)|
Final Exam: On your final exam you will be asked to apply all of the techiniques you have learned in the course to analyze a few real data sets. I will give you the final during the last week of class before final exam week. It will due sometime during finals week.