STAT 462: Applied Regression Analysis
Instructor: Qing (Wendy) Wang
Email: quw104@psu.edu
Office: 422 Thomas Building
Office Hours: Mon 2:00-3:00 pm, or by appointment.
Phone: 865-8634
TA: Wei Wang
Email: wzw114@psu.edu
Office: 301 Thomas Building
Office Hours: Mon 12:30-1:30 pm
Phone: 863-2314
Text: Applied Linear Regression Models, 4th edition, by Neter, Nachtsheim, Kutner, and
Wasserman (recommended but NOT required)
Description: STAT 462 is an applied linear regression course that involves "hands on" data
analysis. Students enrolling for this course should have taken at least one other Statistics
course and should be familiar with the basic fundamentals of statistical testing and estimation.
Computer Usage and Data Sets Data analysis is emphasized, so computers will be used
frequently during the course. Throughout the course, Minitab for Windows will be used to
analyze the data for handouts and lecture demonstrations in class. Those wishing to install
Minitab on their own computers may go to www.minitab.com/education for details.
Data sets can be found at the PSU ANGEL website.
Grading: There are 500 equally weighted points.
(200pts)
(100pts)
(100pts)
(100pts)
Homework
Exam 1
Exam 2
Final Exam
Homework: The homework is worth 40% of the semester grade and consists of weekly lab
activities and textbook exercises. Lab activities need to be submitted on Angel during the
class time. Each week’s homework assignment is collected during the following Tuesday’s lab
unless otherwise specified. Late work will not be accepted unless the student has the
permission from the instructor. (However, no late homework will be accepted if solutions have
been posted.)
Exams: Each exam is worth 20% of the semester grade. Most questions will be short answer
and will include Minitab analysis output. A simple calculator may be used, and some formulas
and tables will be provided. Dates for these exams are July 16, 30 and August 13 (tentative).
Conflicts on exam dates must be resolved in advance. An unexcused absence on an exam
date will incur at least a 10% penalty and may result in a score of 0. Please allow 24 hours for
email response.
Letter Grades:
93 – 99%
90 – 92%
87 – 89%
83 – 86%
80 – 82%
Semester grades are assigned
A
77 – 79%
A70 – 76%
B+
60 – 69%
B
0 – 59%
B-
according to this scale.
C+
C
D
F
Announcements: Lecture notes, lab activities, assignments, and all due dates will be posted
on ANGEL, available at www.angel.psu.edu or from the PSU homepage. Students are
expected to check this several times a week for updates.
Academic Integrity: This course will follow the guidelines found in Section 49-20 of the
University Faculty Senate Policies for Students.
See http://www.science.psu.edu/academic/Integrity concerning academic integrity for details.
Specific Topics Usually Covered
1. Simple Linear Regression Model
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Model for E(Y), model for distribution of errors
Least squares estimation of model for E(Y)
Estimation of variance
2. Inferences for Simple Linear Model
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Inferences concerning the slope ( confidence intervals and t-test)
Confidence interval estimate of the mean Y at a specific X
Prediction interval for a new Y
Analysis of Variance partitioning of variation in Y
R-squared calculation and interpretation
3. Diagnostic procedures for aptness of model
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Residual analyses
o Plots of residuals versus fits, residuals versus x
o Tests for normality of residuals
o Lack of Fit test, Pure Error, Lack of Fit concepts
Transformations as solution to problems with the model
4. Matrix Notation and Literacy
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X matrix, vector, y vector
(X'X)-1 X'Y estimates coefficient vector
Variance- Covariance matrix
5. Multiple Regression Models and Estimation
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Hyperplane extension to simple linear model
Interaction models
Basic estimation and inference for multiple regression
6. General Linear F test and Sequential SS
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Reduced and Full models
F test for general linear hypotheses
Effects of a variable controlled for other predictors
o Sequential SS
o Partial correlation
7. Multicollinearity between X variables
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Effect on standard deviations of coefficients
Problems interpreting effects of individual variables
Apparent conflicts between overall F test and individual variable t tests
Benefits of designed experiments
8. Polynomial Regression Models
9. Categorical Predictor Variables
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Indicator Variables
Interpretation of models containing indicator variables
Piecewise regression
10. More Diagnostic Measures and Remedial Measures for Lack of Fit
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Variance Inflation Factors
Deleted Residuals
Influence statistics - Hat matrix, Cook's D and related measures
11. Examining All Possible Regressions
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R2, MSE , Cp, and PRESS criteria
Stepwise algorithms
12. Miscellaneous Topics as Time Permits
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Estimating the regression model if residuals have autocorrelation.
Logistic regression models
Nonlinear Regression