Essentials of Multiple Regression Analysis

Glauco Peres da Silva, University of São Paulo


This five-day course is designed for students who are interested in reviewing their training in multiple regression analysis. It prepares students for courses offered in the IPSA-USP Summer School that require a background in multiple regression analysis.  The intensive course starts with a discussion of the logic of the multivariate regression model and the central assumptions underlying the ordinary least squares approach. Particular emphasis will be given to multicollinearity, heteroskedasticity, and autocorrelation. To complement lectures, students apply the concepts taught in lectures to analyze problems using Stata.

For those of you considering enrolling in this course, watch the video below to find out more!



This course runs January 22-26,2018.

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 IPSA-USP Summer School, the course will provide an intuitive and basic review of linear regression in theory and practice.  In the five-day course, our classes will be focused on the following topics:

Course Day


Day 1

An Introduction to the Multiple Regression Model

The Linear Regression Model with a Single Regressor

OLS Assumptions

Day 2

The Linear Regression Model with Multiple Regressors

Hypothesis Tests and Confidence Intervals

Assessing Goodness of Fit

Day 3


Day 4


Day 5




The course presumes students have some basic training in mathematics including arithmetic and algebra operations. It also assumes that students have a background in statistics including basic probability; random variables and their distributions; confidence intervals and tests of hypotheses for means, variances, and proportions from one or two populations