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STAT 571A/MATH 571A − Advanced Statistical Regression Analysis


Description: Regression analysis including simple linear regression and multiple linear regression. Matrix formulation and analysis of variance for regression models. Residual analysis, transformations, regression diagnostics, multicollinearity, variable selection techniques, and response surfaces. Students will be expected to utilize standard statistical software packages for computational purposes.

Prerequisite(s): MATH 363 or equivalent; and MATH 410 or MATH 413, or equivalent.

This course in Advanced Statistical Regression Analysis provides graduate students in statistics, biostatistics, mathematics, and related disciplines with an in-depth course of study in regression models and associated data analyses. It explores advanced regression topics, including regression diagnostics and simultaneous inferences, and other miscellaneous topics.

Fall 2022

The course will meet Tuesdays and Thursdays from 12:30 pm - 1:15 pm in Medical Research Bldg. (MRB) room 102, 1656 E Mabel St. (Bldg. no. 241).
The textbook is Applied Linear Regression Models, 4th Edition (2004) by Michael Kutner, Christopher Nachtsheim, and John Neter. The course syllabus gives complete information.

Attendance

Students who are registerered as "on-campus" (Section 571A-001) are required to attend the class sessions every Tuesday and Thursday in MRB 102, subject to the University's policies on classroom attendance (https://catalog.arizona.edu/policy/class-attendance-and-participation). Students who test positive for COVID-19 are asked to refrain from classroom attendance until such time as they later test negative.

Students registered in the online sections (571A-101, 571A-201, or 571A-401) are not subject to this attendance policy.

Homework Assignments - Fall 2022

Homeworks are based on exercises from the textbook.

Homeworks are due as assigned. No exceptions.
These assignments are subject to revision with prior notice. Complete data for the problems are available in the Textbook's CD or our online Download Site. 

           Textbook 
Date due   Chapters   Exercises
-------------------------------------------------
Sep.  8       1       1.1, 1.5, 1.7, 1.19, 1.20a*d*, 1.23, 1.27*, 1.32

Sep. 29      2-3      2.3, 2.4, 2.5a*b*c*d*, 2.13, 2.24a*b*c*d*, 2.28*, 2.34a, 2.42*,
                      3.3c, 3.3d(only NP plot), 3.3e, 3.7a*,3.7c* 
                      3.7d(only NP plot and Shaprio-Wilk test)*, 3.7e(use Brown-Forsythe), 3.13a*b*, 
                      3.17(use maximum likelihood)*, 3.20, 3.24

Oct. 11      4-5      4.10*, 4.14, 4.15, 4.19, 4.16*, 4.17*, 4.23, 4.24,
                      5.4*, 5.8, 5.12*, 5.15, 5.20, 5.23*, 5.29

Oct. 14      1-5      Mid-Term Exam

Oct. 25      6-7      6.1, 6.9c*, 6.10a*b*, 6.18bc, 6.18e(standard residual plot only), 6.18g, 6.19, 6.20,
                      7.4*, 7.7, 7.8, 7.22, 7.25*, 7.27
  
Nov. 10      8-9      8.4a*b*e*g*, 8.16bcd, 8.20, 8.34ab,
                      9.4, 9.7, 9.10b*c*, 9.11*, 9.13bc, 9.14, 9.18a (use forward selection)

Dec.  1     10-11     10.3, 10.10a*b*d*f*, 10.12abcdf, 10.16*, 10.18b, 10.20a, 10.20b(only NP plot), 10.20c-f, 10.23, 
                      10.21a*b*, 10.22*,
                      11.6abdef, 11.7a*, 11.7b(use Brown-Forsythe), 11.7d*e*f*, 11.25(employ Equ. 11.56)


Dec.  9               Comprehensive Final Exam

* Student solution manual answer available

Specialized Downloads

  • Textbook Errata List.


  • PDF of handwritten notes for components of sampling distribution for b1 from Chapter 2.


  • Sample R code for PRESS plot with data from Chapter 9, Table 1.


  • A technical report giving Tables of P-values for t- and Chi-square distributions.


  • Stigler's 1980 article discussing the Law of Eponymy.


  • Selected online encyclopedia entries on regression analysis (access requires the University Library's online subscription):
    Formulas and identities for calculating the sample variance and associated sums of squares.
    Background on Sir Francis Galton.
    Galton's concept of regression to the mean.
    The Method of Least Squares for linear regression.
    Aspects of Quadratic Forms, including degrees of freedom for mean squares in linear (regression) models.
    For those who are interested, a review of Simpson' Paradox.
    An introduction to Kimball's Inequality.

    • General R Language Downloads/Links

  • Suggested reading: The R Guide, ver. 2.5


  • R language comprehensive archive


  • R language FAQ page


  • R language online introduction



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