Date(s) - 05/19/2016 - 05/21/2016
Categories No Categories
Start Date: 5/19/2016
End Date: 5/21/2016
Temple University Center City
1515 Market Street
Taught by Paul Allison, Ph.D.
Linear regression is the most widely-used method for the statistical analysis of non-experimental (observational) data. Its also the essential foundation for understanding more advanced methods like logistic regression, survival analysis, multilevel modeling, and structural equation modeling. Without a thorough mastery of linear regression, theres little point in trying to learn more complex regression methods.
If youve never had a course on linear regression, or if you took one so long ago that you have forgotten most of it, this seminar will get you up to speed. In three days, well cover almost a semesters worth of material. When its over, youll be a knowledgeable and effective user of regression methods. And you will have the necessary preparation to take most of Statistical Horizons more advanced seminars.
Paul Allison has been teaching courses on linear regression for more than 30 years. He is the author of the popular text, Multiple Regression, which provides a very practical, intuitive, and non-mathematical introduction to the topic of linear regression.
The seminar will begin by focusing on the two major goals of linear regression: prediction and hypothesis testing. Well look at several examples from published articles to see how linear regression is used in practice and how to interpret regression tables.
Next well consider all the things that can go wrong when using linear regression, and well see how to critique the analyses done by others.
Well delve into the mathematical theory behind linear regression, focusing on the essential assumptions, and on the implied properties of the least squares method. Well also spend considerable time on techniques for building non-linearity into linear regression by way of transformations, interactions, and dummy (indicator) variables.
There will be lots of hands-on exercises using either SAS or Stata.