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This course provides an introduction to the theory, methods, and practice of regression analysis. The goals are to provide students with the skills that are necessary to: (1) read, understand, and evaluate the professional literature that uses regression analysis; (2) design and carry out studies that employ regression techniques for testing substantive theories; and (3) prepare to learn about more advanced statistical procedures. The course will not dwell on statistical theory, but it will also not take a superficial approach. Instead, it will focus on: The utility of regression analysis for evaluating empirical relationships between variables as a critical component of the theory-testing process. We will thoroughly cover the basic elements of the regression model and the development of the regression estimators. We will see that this model depends very heavily on several assumptions. Therefore, we will examine these assumptions in detail, considering why they are necessary, whether they are valid in practical research situations, and the consequences of violating them in particular applications of the regression techniques. These formal, analytic treatments will be counterbalanced by the use of frequent substantive examples and class exercises. Again, the overall course objective is not to turn you into a statistician- instead, the aim is to maximize your research skills as a political scientist.
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Content Preview
Professor Schneider
PLS 802
324B S. Kedzie Hall
Spring 2009
sks@msu.edu
517/355-7682
REGRESSION ANALYSIS
This course provides an introduction to the theory, methods, and practice of regression analysis.
The goals are to provide students with the skills that are necessary to: (1) read, understand, and
evaluate the professional literature that uses regression analysis; (2) design and carry out studies
that employ regression techniques for testing substantive theories; and (3) prepare to learn about
more advanced statistical procedures.
The course will not dwell on statistical theory, but it will also not take a superficial approach.
Instead, it will focus on: The utility of regression analysis for evaluating empirical relationships
between variables as a critical component of the theory-testing process. We will thoroughly
cover the basic elements of the regression model and the development of the regression
estimators. We will see that this model depends very heavily on several assumptions. Therefore,
we will examine these assumptions in detail, considering why they are necessary, whether they
are valid in practical research situations, and the consequences of violating them in particular
applications of the regression techniques. These formal, analytic treatments will be
counterbalanced by the use of frequent substantive examples and class exercises. Again, the
overall course objective is not to turn you into a statistician– instead, the aim is to maximize
your research skills as a political scientist.
Course Prerequisites: Any course of this type must assume a working knowledge of
elementary statistical concepts and techniques. We will conduct a brief review at the beginning
of the course, but students must be familiar with such ideas as descriptive statistics, sampling
distributions, statistical inference, confidence intervals, and hypothesis testing, before moving on
to the more complicated matters that will comprise the majority of the course material. You must
have completed at least one prior course in introductory statistics course– i.e., PLS 801 or the
equivalent.
Course Requirements: Formal course requirements are as follows: (1) Class attendance and
active participation. This is mandatory. Statistical knowledge is cumulative, and gaps in the
material will have detrimental consequences. (2) Completion of homework assignments. Most of
these are computer-based data analysis exercises, designed to familiarize you with the
application of various concepts and techniques. Each of these assignments will focus on a
specific set of topics. However, the latter assignments are cumulative in the sense that they build
upon earlier material in the class. Homework assignments will be given frequently (about once a
week or so). They will not be assigned grades, but they will be checked for completion and
comments will be provided to make sure that you fully understand the material. (3) Two
examinations. A midterm exam will be given in class on Tuesday, March 3; the final will be a
take-home exam, due on Tuesday, May 5, 2009, at 5:00 p.m. (4) Regression Analysis Paper. For
this paper, you are expected to use regression analysis to examine a political science research
question. You should identify a suitable topic and focus for the paper by Friday, March 14, 2009.
Before you select the topic, I strongly encourage each of you to discuss your paper ideas with
me. Final papers are due on or before 5:00 p.m. on Friday, May 1, 2009. I reserve the right to
penalize papers received after the May 1, 2009 deadline.
PLS 802, Spring 2009
Page 2
Assignment of Final Grades:
Homework Assignments & Class Participation
20 %
Midterm Examination
25 %
Regression Analysis Paper
25 %
Final Examination
30 %
Textbooks:
The following are the required texts for the course:
Berry, William D., and Stanley Feldman. 1985. Multiple Regression in Practice. Beverly
Hills, CA: Sage Publications.
McClendon, McKee. 1994 (reissued 2002). Multiple Regression and Causal Analysis.
Prospect Heights, IL: Waveland Press.
The following books are useful recommended books for more detailed, comprehensive coverage
of the material along with explicit derivations of statistical concepts:
Gujarati, Damodar N. 2003. Basic Econometrics (Fourth Edition). Boston, MA: McGraw-
Hill.
Fox, John. Regression Diagnostics.1991. Beverly Hills, CA: Sage Publications
Kennedy, Peter. 2008. A Guide to Econometrics (Sixth Edition), Malden, MA: Black
Publishing, Inc.
Wooldridge, Jeffrey M. 2006. Introductory Econometrics: A Modern Approach (Third
Edition). Mason, OH: Thomson South-Western.
The following books are useful supplemental books for more basic explanations of key terms
and concepts:
Berry, William D. 1993. Understanding Regression Assumptions. Beverly Hills, CA: Sage
Publications.
Lewis-Beck, Michael. 1980. Applied Regression. Beverly Hills, Sage Publications.
Schroeder, Larry D., David L. Sjoquist, and Paula E. Stephan. 1986. Understanding
Regression Analysis: An Introductory Guide. Newbury Park: Sage Publications.
You should read all the material assigned in the required texts. Most of the recommended and
supplemental books are either too advanced or elementary to be used as central texts in this
course. However, several of them are very good and would be extremely useful books for you to
rely upon for greater detail or additional explanations at various points in the course.
PLS 802, Spring 2009
Page 3
Computing and Software: Computers and statistical software are absolutely necessary for
employing modern statistical techniques in an effective manner. Therefore, they will be closely
integrated into the course material. We will use STATA for most of the class examples,
assignments, and examinations. But, you can also use other statistical software in this course
(e.g., R, SAS, SPSS, SYSTAT, etc.), as long as it has the analytical routines and capacities that
are required to complete the assignments and examinations.
Topics and Reading Assignments
I.
Introduction to Regression Analysis
Reading:
McClendon, pp. 1-19
Gujarati, pp. 15-32
Kennedy, pp. 1-10
Wooldridge, pp. 1-19
II.
Preliminary Material and Statistical Review
A. Frequency Distributions, Univariate Summary Statistics, Probability
Distributions
Reading:
McClendon, pp. 20-25
B. Statistical Inference and the Properties of Statistical Estimators
1. Confidence Intervals & Hypothesis Tests
2. Differences Between Two Means, Two Variances, Etc.
III.
Basic Concepts for Understanding Regression Analysis: Functional
Dependence, Linear Transformations, and Linear Combinations
Reading:
McClendon, pp. 25-28
Wooldridge, pp 707-802
PLS 802, Spring 2009
Page 4
IV. The Bivariate Regression Model
A. Introduction: Basic Ideas and Concepts
Reading:
McClendon, pp. 28-30
Berry, pp. 1-22
Gujarati, pp. 37-57
B. The Least Squares Criterion and Estimation in the Bivariate Regression
Model
Reading:
McClendon, pp.31-41
Berry and Feldman, pp. 9-12
Gujarati, pp. 58-80
Kennedy, pp. 11-59
Wooldridge, pp. 50-66, 89-95, 106-109, 123-126, 176-181, 187-190
C. Goodness of fit, the Correlation Coefficient and R2
Reading:
McClendon, pp. 42-49
Gujarati, pp. 79-94
Schroeder, Sjoquist, and Stephan, pp. 23-29
D. Assumptions Underlying the Bivariate Linear Regression Model
Reading:
McClendon, pp. 133-146
Berry and Feldman, pp. 9-12
Gujarati, pp. 65-74; 107-110
Kennedy, pp. 11-59
Wooldridge, pp. 50-66, 89-95, 106-109, 123-126, 176-181, 187-190
E. Statistical Inference, Confidence Intervals, and Hypothesis Tests
Reading:
McClendon, pp. 147-154
Lewis-Beck, pp. 26-47
Schroeder, Sjoquist, and Stephan, pp. 36-53
Gujarati, pp. 119-163
Kennedy, pp. 51-90
Wooldridge, pp. 126-147
PLS 802, Spring 2009
Page 5
F. Summary, Extensions, and a Preliminary Look at Residuals, Outliers,
and Influential Cases
Reading:
McClendon, pp. 49-59
Berry, pp. 22-88
Gujarati, pp. 164-193
V.
The Multiple Regression Model
A. Introduction: Notation, Assumptions, and Interpretation
Reading:
McClendon, pp. 60-80
Berry and Feldman, pp. 9-18
Gujarati, pp. 202-211; 230-233
Wooldridge, pp. 73-88
B. Measures of Goodness of Fit
Reading:
McClendon, pp. 80-83
Gujarati, pp. 211-225
Schroeder, Sjoquist, and Stephan, pp. 32-36
C. Statistical Inference and the Role of Hypothesis Testing
Reading:
McClendon, pp. 133-174
Berry and Feldman, pp. 12-18
Gujarati, pp. 248-273
Kennedy, pp. 60-80
Wooldridge, pp. 147-167, 214-218
D. Models of Substantive Phenomena; The Importance of Model
Assumptions
Reading:
McClendon, pp. 83-93, 154-157
Berry, pp. 1-24
Lewis-Beck, pp. 63-66
PLS 802, Spring 2009
Page 6
E. Summary and a Brief Look at Extensions
Reading:
McClendon, pp. 93-118
(McClendon, pp. 119-132)
Gujarati, pp. 273-297
VI.
Model Building in Multiple Regression Analysis
A. Model Specification
Reading:
McClendon, pp. 288-321
Berry and Feldman, pp. 18-26
Berry, pp. 30-45
Gujarati, pp. 506-560
Kennedy, pp. 71-92
Lewis-Beck, pp. 30-45
Schroeder, Sjoquist, and Stephan, pp. 67-70
B. Functional Forms, Nonlinearity and Transformations
Reading:
McClendon, pp. 230-270
Berry and Feldman, pp. 51-72
Berry, pp. 60-66
Gujarati, pp. 561-577
Kennedy, pp. 93-111
Schroeder, Sjoguist, and Stephan, pp. 58-61
Wooldridge, pp. 304-310
C. Nominal Independent Variables
Reading:
McClendon, pp. 198-229; 271-287
Gujarati, pp. 297-334
Kennedy, pp. 248-258
Schroeder, Sjoquist, and Stephan, pp. 56-58
Wooldridge, pp. 230-252
PLS 802, Spring 2009
Page 7
VII.
Potential Problems in Multiple Regression Analysis
A. Interpretation of Results
Reading:
Fox, pp.3-5
B. Multicollinearity and Its Effects
Reading:
McClendon, pp. 161-163
Berry and Feldman, pp. 37-50
Berry, pp. 24-27
Fox, pp. 10-21
Gujarati, pp. 341-370, 506-560
Kennedy, pp. 192-202
Lewis-Beck, pp. 58-63
Schroeder, Sjoquist, and Stephan, pp. 71-72
Wooldridge, pp. 101-105
C. Nonnormal and Nonconstant (Heteroscedastic) Errors
Reading:
McClendon, pp. 174-197
Berry and Feldman, pp. 73-88
Berry, pp. 67, 72-81
Fox, pp. 40-53
Gujarati, pp. 387-428
Kennedy, pp. 133-139
Wooldridge, pp. 181-185
D. Measurement Error
Reading:
Berry and Feldman, pp. 26-37
Berry, pp. 45-60
Gujarati, pp. 524-528
Kennedy, pp. 157-163
Schroeder, Sjoquist, and Stephan, pp. 70-71
Wooldridge, pp. 318-325
PLS 802, Spring 2009
Page 8
E. Residual Analysis, Outliers, and Influential Observations
Reading:
Berry, pp. 27-29
Fox, pp. 21-40
Gujarati, pp. 518-524
Kennedy, pp. 372-388
VIII.
Additional Topics
A. Dichotomous Dependent Variables
Reading:
Gujarati, pp. 580-636
Schroeder, Sjoquist, and Stephan, pp. 79-80
Wooldridge, pp. 252-258
B. Simultaneous Equation Models
Reading:
McClendon, pp. 288-347
Berry, pp. 1-54
Gujarati, pp. 715-791
Schroeder, Sjoquist, and Stephan, pp. 77-79
C. Nonindependent Disturbances and Time Series Models
Reading:
Berry, pp. 67-72
Gujarati, pp. 792-865
Kennedy, pp. 139-156, 163-179
Schroeder, Sjoquist, and Stephan, pp. 72-75
PLS 802
324B S. Kedzie Hall
Spring 2009
sks@msu.edu
517/355-7682
REGRESSION ANALYSIS
This course provides an introduction to the theory, methods, and practice of regression analysis.
The goals are to provide students with the skills that are necessary to: (1) read, understand, and
evaluate the professional literature that uses regression analysis; (2) design and carry out studies
that employ regression techniques for testing substantive theories; and (3) prepare to learn about
more advanced statistical procedures.
The course will not dwell on statistical theory, but it will also not take a superficial approach.
Instead, it will focus on: The utility of regression analysis for evaluating empirical relationships
between variables as a critical component of the theory-testing process. We will thoroughly
cover the basic elements of the regression model and the development of the regression
estimators. We will see that this model depends very heavily on several assumptions. Therefore,
we will examine these assumptions in detail, considering why they are necessary, whether they
are valid in practical research situations, and the consequences of violating them in particular
applications of the regression techniques. These formal, analytic treatments will be
counterbalanced by the use of frequent substantive examples and class exercises. Again, the
overall course objective is not to turn you into a statistician– instead, the aim is to maximize
your research skills as a political scientist.
Course Prerequisites: Any course of this type must assume a working knowledge of
elementary statistical concepts and techniques. We will conduct a brief review at the beginning
of the course, but students must be familiar with such ideas as descriptive statistics, sampling
distributions, statistical inference, confidence intervals, and hypothesis testing, before moving on
to the more complicated matters that will comprise the majority of the course material. You must
have completed at least one prior course in introductory statistics course– i.e., PLS 801 or the
equivalent.
Course Requirements: Formal course requirements are as follows: (1) Class attendance and
active participation. This is mandatory. Statistical knowledge is cumulative, and gaps in the
material will have detrimental consequences. (2) Completion of homework assignments. Most of
these are computer-based data analysis exercises, designed to familiarize you with the
application of various concepts and techniques. Each of these assignments will focus on a
specific set of topics. However, the latter assignments are cumulative in the sense that they build
upon earlier material in the class. Homework assignments will be given frequently (about once a
week or so). They will not be assigned grades, but they will be checked for completion and
comments will be provided to make sure that you fully understand the material. (3) Two
examinations. A midterm exam will be given in class on Tuesday, March 3; the final will be a
take-home exam, due on Tuesday, May 5, 2009, at 5:00 p.m. (4) Regression Analysis Paper. For
this paper, you are expected to use regression analysis to examine a political science research
question. You should identify a suitable topic and focus for the paper by Friday, March 14, 2009.
Before you select the topic, I strongly encourage each of you to discuss your paper ideas with
me. Final papers are due on or before 5:00 p.m. on Friday, May 1, 2009. I reserve the right to
penalize papers received after the May 1, 2009 deadline.
PLS 802, Spring 2009
Page 2
Assignment of Final Grades:
Homework Assignments & Class Participation
20 %
Midterm Examination
25 %
Regression Analysis Paper
25 %
Final Examination
30 %
Textbooks:
The following are the required texts for the course:
Berry, William D., and Stanley Feldman. 1985. Multiple Regression in Practice. Beverly
Hills, CA: Sage Publications.
McClendon, McKee. 1994 (reissued 2002). Multiple Regression and Causal Analysis.
Prospect Heights, IL: Waveland Press.
The following books are useful recommended books for more detailed, comprehensive coverage
of the material along with explicit derivations of statistical concepts:
Gujarati, Damodar N. 2003. Basic Econometrics (Fourth Edition). Boston, MA: McGraw-
Hill.
Fox, John. Regression Diagnostics.1991. Beverly Hills, CA: Sage Publications
Kennedy, Peter. 2008. A Guide to Econometrics (Sixth Edition), Malden, MA: Black
Publishing, Inc.
Wooldridge, Jeffrey M. 2006. Introductory Econometrics: A Modern Approach (Third
Edition). Mason, OH: Thomson South-Western.
The following books are useful supplemental books for more basic explanations of key terms
and concepts:
Berry, William D. 1993. Understanding Regression Assumptions. Beverly Hills, CA: Sage
Publications.
Lewis-Beck, Michael. 1980. Applied Regression. Beverly Hills, Sage Publications.
Schroeder, Larry D., David L. Sjoquist, and Paula E. Stephan. 1986. Understanding
Regression Analysis: An Introductory Guide. Newbury Park: Sage Publications.
You should read all the material assigned in the required texts. Most of the recommended and
supplemental books are either too advanced or elementary to be used as central texts in this
course. However, several of them are very good and would be extremely useful books for you to
rely upon for greater detail or additional explanations at various points in the course.
PLS 802, Spring 2009
Page 3
Computing and Software: Computers and statistical software are absolutely necessary for
employing modern statistical techniques in an effective manner. Therefore, they will be closely
integrated into the course material. We will use STATA for most of the class examples,
assignments, and examinations. But, you can also use other statistical software in this course
(e.g., R, SAS, SPSS, SYSTAT, etc.), as long as it has the analytical routines and capacities that
are required to complete the assignments and examinations.
Topics and Reading Assignments
I.
Introduction to Regression Analysis
Reading:
McClendon, pp. 1-19
Gujarati, pp. 15-32
Kennedy, pp. 1-10
Wooldridge, pp. 1-19
II.
Preliminary Material and Statistical Review
A. Frequency Distributions, Univariate Summary Statistics, Probability
Distributions
Reading:
McClendon, pp. 20-25
B. Statistical Inference and the Properties of Statistical Estimators
1. Confidence Intervals & Hypothesis Tests
2. Differences Between Two Means, Two Variances, Etc.
III.
Basic Concepts for Understanding Regression Analysis: Functional
Dependence, Linear Transformations, and Linear Combinations
Reading:
McClendon, pp. 25-28
Wooldridge, pp 707-802
PLS 802, Spring 2009
Page 4
IV. The Bivariate Regression Model
A. Introduction: Basic Ideas and Concepts
Reading:
McClendon, pp. 28-30
Berry, pp. 1-22
Gujarati, pp. 37-57
B. The Least Squares Criterion and Estimation in the Bivariate Regression
Model
Reading:
McClendon, pp.31-41
Berry and Feldman, pp. 9-12
Gujarati, pp. 58-80
Kennedy, pp. 11-59
Wooldridge, pp. 50-66, 89-95, 106-109, 123-126, 176-181, 187-190
C. Goodness of fit, the Correlation Coefficient and R2
Reading:
McClendon, pp. 42-49
Gujarati, pp. 79-94
Schroeder, Sjoquist, and Stephan, pp. 23-29
D. Assumptions Underlying the Bivariate Linear Regression Model
Reading:
McClendon, pp. 133-146
Berry and Feldman, pp. 9-12
Gujarati, pp. 65-74; 107-110
Kennedy, pp. 11-59
Wooldridge, pp. 50-66, 89-95, 106-109, 123-126, 176-181, 187-190
E. Statistical Inference, Confidence Intervals, and Hypothesis Tests
Reading:
McClendon, pp. 147-154
Lewis-Beck, pp. 26-47
Schroeder, Sjoquist, and Stephan, pp. 36-53
Gujarati, pp. 119-163
Kennedy, pp. 51-90
Wooldridge, pp. 126-147
PLS 802, Spring 2009
Page 5
F. Summary, Extensions, and a Preliminary Look at Residuals, Outliers,
and Influential Cases
Reading:
McClendon, pp. 49-59
Berry, pp. 22-88
Gujarati, pp. 164-193
V.
The Multiple Regression Model
A. Introduction: Notation, Assumptions, and Interpretation
Reading:
McClendon, pp. 60-80
Berry and Feldman, pp. 9-18
Gujarati, pp. 202-211; 230-233
Wooldridge, pp. 73-88
B. Measures of Goodness of Fit
Reading:
McClendon, pp. 80-83
Gujarati, pp. 211-225
Schroeder, Sjoquist, and Stephan, pp. 32-36
C. Statistical Inference and the Role of Hypothesis Testing
Reading:
McClendon, pp. 133-174
Berry and Feldman, pp. 12-18
Gujarati, pp. 248-273
Kennedy, pp. 60-80
Wooldridge, pp. 147-167, 214-218
D. Models of Substantive Phenomena; The Importance of Model
Assumptions
Reading:
McClendon, pp. 83-93, 154-157
Berry, pp. 1-24
Lewis-Beck, pp. 63-66
PLS 802, Spring 2009
Page 6
E. Summary and a Brief Look at Extensions
Reading:
McClendon, pp. 93-118
(McClendon, pp. 119-132)
Gujarati, pp. 273-297
VI.
Model Building in Multiple Regression Analysis
A. Model Specification
Reading:
McClendon, pp. 288-321
Berry and Feldman, pp. 18-26
Berry, pp. 30-45
Gujarati, pp. 506-560
Kennedy, pp. 71-92
Lewis-Beck, pp. 30-45
Schroeder, Sjoquist, and Stephan, pp. 67-70
B. Functional Forms, Nonlinearity and Transformations
Reading:
McClendon, pp. 230-270
Berry and Feldman, pp. 51-72
Berry, pp. 60-66
Gujarati, pp. 561-577
Kennedy, pp. 93-111
Schroeder, Sjoguist, and Stephan, pp. 58-61
Wooldridge, pp. 304-310
C. Nominal Independent Variables
Reading:
McClendon, pp. 198-229; 271-287
Gujarati, pp. 297-334
Kennedy, pp. 248-258
Schroeder, Sjoquist, and Stephan, pp. 56-58
Wooldridge, pp. 230-252
PLS 802, Spring 2009
Page 7
VII.
Potential Problems in Multiple Regression Analysis
A. Interpretation of Results
Reading:
Fox, pp.3-5
B. Multicollinearity and Its Effects
Reading:
McClendon, pp. 161-163
Berry and Feldman, pp. 37-50
Berry, pp. 24-27
Fox, pp. 10-21
Gujarati, pp. 341-370, 506-560
Kennedy, pp. 192-202
Lewis-Beck, pp. 58-63
Schroeder, Sjoquist, and Stephan, pp. 71-72
Wooldridge, pp. 101-105
C. Nonnormal and Nonconstant (Heteroscedastic) Errors
Reading:
McClendon, pp. 174-197
Berry and Feldman, pp. 73-88
Berry, pp. 67, 72-81
Fox, pp. 40-53
Gujarati, pp. 387-428
Kennedy, pp. 133-139
Wooldridge, pp. 181-185
D. Measurement Error
Reading:
Berry and Feldman, pp. 26-37
Berry, pp. 45-60
Gujarati, pp. 524-528
Kennedy, pp. 157-163
Schroeder, Sjoquist, and Stephan, pp. 70-71
Wooldridge, pp. 318-325
PLS 802, Spring 2009
Page 8
E. Residual Analysis, Outliers, and Influential Observations
Reading:
Berry, pp. 27-29
Fox, pp. 21-40
Gujarati, pp. 518-524
Kennedy, pp. 372-388
VIII.
Additional Topics
A. Dichotomous Dependent Variables
Reading:
Gujarati, pp. 580-636
Schroeder, Sjoquist, and Stephan, pp. 79-80
Wooldridge, pp. 252-258
B. Simultaneous Equation Models
Reading:
McClendon, pp. 288-347
Berry, pp. 1-54
Gujarati, pp. 715-791
Schroeder, Sjoquist, and Stephan, pp. 77-79
C. Nonindependent Disturbances and Time Series Models
Reading:
Berry, pp. 67-72
Gujarati, pp. 792-865
Kennedy, pp. 139-156, 163-179
Schroeder, Sjoquist, and Stephan, pp. 72-75
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