Essentials of Data and Multiple Regression Analysis
Glauco Peres da Silva, University of São Paulo
COURSE DESCRIPTION
This course is designed for students who are interested in reviewing their training in data analysis and multiple regression analysis. It prepares students for courses offered in the IPSAUSP Summer School that require a background in statistics and in multiple regression analysis including the Time Series Analysis and Pooled Time Series Analyses, and Spatial Econometrics courses. The course will take place in the week preceding the commencement of the Summer School. The intensive course starts with a discussion of the logic of the data analysis, based on including basic probability; random variables and their distributions; confidence intervals and tests of hypotheses. After that it covers the basic assumptions of multivariate regression model and the central assumptions underlying the ordinary least squares approach. Similar to other IPSAUSP courses, the Essentials of Data and Multiple Regressions Analysis takes a “hands on” approach. To complement lectures, students apply the concepts taught in lectures to analyze problems using software packages commonly used in quantitative social science research including Excel and Stata.
For those of you considering enrolling in this course, watch the video below to find out more!
DATES
This course runs January 1418,2019.
TEACHING FELLOW: Mauricio Izumi, University of São Paulo
This course departs from the premise that the most effective way to learn multivariate statistics is by actively using the concepts discussed in class to solve problems. For each topic, we will have lectures that will be followed by sessions in which students will use empirical data to answer questions that are important to political scientists. For those students who will be studying multivariate regression analysis in the IPSAUSP Summer School, the course will provide an intuitive and basic review of linear regression in theory and practice.

Topics 
Monday, January 14^{th} 
Lecture 1. Probability Lecture 2. Distribution of random variables 
Tuesday, January 15^{th} 
Lecture 3. Joint distributions Lecture 4. Confidence Intervals 
Wednesday, January 16^{th} 
Lecture 5. An Introduction to the Multiple Regression Model Lecture 6. The Linear Regression Model with a Single Regressor 
Thursday, January 17^{th} 
Lecture 7. Hypothesis Tests and Confidence Intervals Lecture 8. Assumptions of Ordinary Least Squares 
Friday, January 18^{th} 
Lecture 9. The Linear Regression Model with Multiple Regressors Lecture 10. Assessing Goodness of Fit 
In the afternoon, classes will be focused on labs activities.
PREREQUISITES
The course presumes students have some basic training in mathematics including arithmetic and algebra operations.
REQUIRED READINGS
Casella, George, and Roger Berger. 2008. Statistical Inference. 2^{nd} ed. Duxbury Advanced Series. Cengage Learning.
Kellstedt, Paul M., and Guy D. Whitten. 2013. The Fundamentals of Political Science Research. 2nd ed. Cambridge ;New York: Cambridge University Press.
Stock, James H., and Mark W. Watson. 2011. Introduction to Econometrics. 3rd ed. Boston: Pearson/Addison Wesley.
FURTHER READINGS
Gelman, Andrew, and Hal Stern. 2006. The Difference Between “Significant” and “Not Significant” is not Itself Statistically Significant. The American Statistician 60 (4): 328331.
Gujarati, Damodar N., and Dawn C. Porter. 2009. Basic econometrics. 5th ed. Boston: McGrawHill Irwin.
Greene, W. H. 2012. Econometric analysis. 7th ed. Upper Saddle River, NJ: Pearson Prentice Hall.
Wooldridge, Jeffrey M. 2009. Introductory Econometrics: A Modern Approach. Cincinnati, OH: SouthWestern College.