Regression Analysis (PSY 443)

Instructor: W. Joel Schneider
Office: DeGarmo 456
Phone: 438-8410
e-mail: wjschne@ilstu.edu
Office hours: Mondays & Wednesdays 11am–noon, and by appointment
Semester: Spring 2016
Times: MW 12:35–1:50pm

Course Objectives

Textbooks

Keith, T. Z. (2015). Multiple regression and beyond (2nd ed.). New York: Routledge.

Fox, J. (2016). Applied regression analysis & generalized linear models (3rd ed.). Thousand Oaks, CA: Sage.

Fox, J. & Weisberg, S. (2011). An R companion to applied regression (2nd ed.). Thousand Oaks, CA: Sage.

Software

Evaluation

Class Participation

Homework Exercises

Exams

The midterm and final exams will involve a closed-notes portion in which no computer will be allowed and an open-notes portion in which a computer will be required.

Grading Procedure

All pass/fail assignments must be passed to receive a grade in the course. With instructor permission, failed assignments may be attempted a second time.

Points
Homework Exercises 400 points
Midterm Exam 300 points
Final Exam 300 points
Total 1000 points

Grades are assigned in the traditional manner.

Final Grade
Performance Grade
900-1000 A
800-899 B
700-799 C
600-699 D
0-599 F

Special Accommodations

If you need a special accommodation to fully participate in this class, please contact Disability Concerns at 438-5853 (voice), 438-8620 (TDD).

Academic Integrity

Plagiarizing and cheating on exams and other assignments are not tolerated. Any student exhibiting academic dishonesty will receive an F in the course and will be referred for disciplinary action. Disciplinary action may include expulsion from the university.

Communication

The best way to reach me is by email or in person, before or after class. I do not check my office telephone messages nearly as often as I check my email. I may, from time to time, email you about various matters.

Additional information


Tentative Course Outline
Week Topics Readings
1-11 Random Variables
markdown
1-13 Introduction to RMarkdown
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In-class Exercise 1
MRAB Ch. 1
ARAGLM Ch. 1
RCAR Ch. 1
1-20 In-class Exercise 2
1-25 Correlation
markdown
In-class Exercise 3
2-3 Simple Regression
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In-class Exercise 4
2-15 Multiple Regression
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In-class Exercise 5
MRAB Ch. 2–3
RCAR Ch. 4
2-17 Outliers and Regression Diagnostics
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MRAB Ch. 9
ARAGLM Ch. 11–12
RCAR Ch. 6
2-22 In-class Exercise 6 MRAB Ch. 4–5
2-24 Homework 1 is due
3-21 Midterm Exam Midterm Study Guide
3-23 Categorial Predictors
markdown
MRAB Ch. 6
3-28 Moderator Analysis
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Homework 2 (Due date 4/25 at 11:55pm)
3-30 Midterm Review Optional Practice Midterm
4-4 Moderators & Polynomials MRAB Ch. 7–8
ARAGLM Ch. 17
RCAR Ch. 4
4-6 Miderm 2.0
4-11 In-class Exercise 7 (Due 4/18 at 11:55pm)
4-13 Bootstrapping
markdown
4-18 Mediation
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4-25 Logistic Regression, Poisson Regression, & the Generalized Linear Model
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5-2 Final Exam (7:50–9:50) Final Exam Study Guide