HUDM 5122: Applied Regression Analysis
Spring 2015
Tuesday: 1:00pm– 2:40pm (lecture)
Labs:
Tuesday 3 – 4:40 (Lauren)
Tuesday 5 – 6:40 (Lauren)
Thursday 11 – 12:40 (Josh)
Thursday 1 – 2:40 (Josh)
Professor: L. Elizabeth Tipton
Office Hours: sign up at: https://tipton.youcanbook.me/
Email: tipton@tc.columbia.edu
Office: Grace Dodge 462
TA1: Lauren Fellers
TA2: Josh Paxton
Email: laf2156@tc.columbia.edu
Email: jap2208@tc.columbia.edu
Office hours:
Office hours:
Prerequisite: HUDM 4122 or permission of instructor
Course Description:
Least squares estimation theory. Traditional simple and multiple regression models and
polynomial regression models, with grouping variables including one-way ANOVA, twoway ANOVA, and analysis of covariance.
Required textbooks:
 Allison, P. (1998) Multiple regression: A primer. The Pine Forge Press series in
research methods and statistics. Thousand Oaks, CA: Sage. ($25 - $45)
 Fox, J. (1991) Regression diagnostics. Sage University Papers Series on Quantitative
Applications in the Social Sciences, 07-079. Newbury Park, CA: Sage. ($5 - $16)
 Hardy, M. (1993) Regression with dummy variables. Sage University Papers Series
on Quantitative Applications in the Social Sciences, 07-093. Newbury Park, CA:
Sage. ($9 - $16)
 Menard, S. (2001) Applied logistic regression analysis. Sage University Papers
Series on Quantitative Applications in the Social Sciences, 07-106. Thousand Oaks,
CA: Sage. ($8 - $16)
Suggested textbooks (on reserve at the library):
Fox, J. (2008) Applied Regression Analysis and Generalized Linear Models. Second
Edition. SAGE. (HA31.3.F69 2008 ON RESERVE)
Kutner, M.H., Nachtsheim, C.J., and Neter, J. (2004) Applied linear regression models.
McGraw Hill. (QA278.2.A65 2004 ON RESERVE)
Field, A. (2009) Discovering statistics using SPSS. Third edition. SAGE. (HA32.F54
2009 ON RESERVE)
Software
We will use SPSS in the laboratory sessions. Use of other software is permitted; please
speak to me if you would like to do this.
Best resource: UCLA statistical computing : http://www.ats.ucla.edu/stat/
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The method of least squares is the automobile of modern statistical analysis: despite its
limitations, occasional accidents, and incidental pollution, this method and its
numerous variations, extensions, and related conveyances carry the bulk of statistical
analyses, and are known and valued by nearly all.
--Stephen M. Stigler (1999, pg 320)
Course objectives:
(1) Identify, code, and recode continuous and discrete variables and choose the
appropriate statistical procedure for their analysis;
(2) Describe relationships between predictor variables and a continuous outcome
variable;
(3) Model, estimate, and interpret point estimates, standard errors, confidence
intervals, and hypothesis tests for regression slopes;
(4) Delineate assumptions of linear statistical models used and evaluate particular
analyses for meeting these assumptions;
(5) Recognize and explain similarities and differences between regression and
ANOVA models;
(6) Conduct analyses to diagnose problems of multi-collinearity, influential points,
model fit, etc.;
(7) Become familiar with a nationally representative dataset and issues associated
with using data of this kind for analysis;
(8) Formulate questions, identify appropriate models, estimate models, and interpret
the results for questions of real social science interest.
Grading:
50% Homework
 There are 6 assignments. The first 5 are REQUIRED. The 6th assignment is
optional and its score can replace your lowest grade. For example, if you had
scores of 79,100,97,95,93 on HW 1-5 you could do HW #6 and replace the 79
with your HW #6 grade.
 You are encouraged to work together on your homework. However, students
must submit separate homework assignments and must run their own analyses.
 NO LATE HOMEWORK WILL BE ACCEPTED.
20% Midterm
 The midterm will cover Weeks 1 – 7.
 You will be allowed one 8 ½ x 11 one-sided “cheat sheet”.
30% Final Project
 You will choose a question, models, hypotheses, and run the analysis using
methods learned in class.
 You will write this up as a research publication, but with a shorter introduction
section and longer methods section.
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Schedule
Unit
Week/ Date
Class Lecture
Reading
(BEFORE
Tuesday)
Intro &
Review
Syllabus,
Introduction,
Review

(1) 1/27
Runyon Ch 11 &
12 (on Moodle)
Regression
(2) 2/3
Simple
regression


Allison Ch 1 & 5
Fox Ch 5 & 6
(on Moodle)
HW #1
{Review}
(3) 2/10
Multiple
regression


Allison Ch 2 & 3
Fox Ch 5 & 6
(on Moodle)
HW #2.1
{Simple
regression}
(4) 2/17
Qualitative
variables

Hardy GB sctns
1 – 3 (pp. 1-29)
HW #2.2
{Multiple Reg}
(5) 2/24
Interactions


Allison Ch 8
Hardy GB sctn 4
(pp 29 – 63)
HW #3.1
{Dummy vars}
(6) 3/3
Non-linearity


Allison Ch 8
Hardy GB pp.
78-82
Fox GB sctn 7
(pp 53 – 61)
HW #3.2
{Interactions}

Midterm
(7) 3/10
Exam (2 hours)
Homework due
date (in
lecture)
HW #3.3 {nonlinearity}
Midterm
(8) 3/17
SPRING
BREAK
3
Working
with real
data
(9) 3/24
Real data
Handout on Moodle:
-Datasets
-Codebooks
-Cleaning/Importing
Midterms
returned
(10) 3/31
Choosing a
model
(causality,
omitted
variable bias,
variable
selection)
On Moodle:
 Gelman & Hill
 Duncan &
Gipson-Davis
 Left-handed
death
 Explain vs.
predict
HW #4.1 {real
data}
(11) 4/7
Outliers,
leverage, and
influence


Allison Ch 6
Fox GB sctn 4
(pp 21 – 40)
HW #4.2
{models}
(12) 4/14
Non-normality, 
Non-constant

variance,
Collinearity
Allison Ch 7
Fox GB sctns 3,
5, 6, 10 (pp 10 –
21; 40 – 53; 75 –
79)
Hardy GB pp. 82
– 84
HW #5.1
{outliers, etc}
Menard GB sctn
1-2.2; 3 (pp 1 –
27; 41 – 67)
Hardy GB pp. 75
– 78
HW #5.2 {nonnorm,etc}
Hardy, pp.56-60
HW #6.1

Extensions
Final
Project
Final Project
information
given out
Generalized
linear models
(e.g., logistic
regression)

(14) 4/28
Other topics in
regression
(e.g., Poisson
regression)

(15) 5/5
Review/
meetings about
final projects
HW #6.2
{extensions}
(16) 5/12
Final project
due
Final Project
[due 5/12,10
am]
(13) 4/21

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