In this short course we will cover how to analyze simple and multiple linear regression models. You will learn concepts in linear regression such as:
1) How to use the F-test to determine if your predictor variables have
a statistically significant relationship with your outcome/response variable.
2) What are the assumptions for linear regression analysis and what
you should do to meet these assumptions?
3) Why adjusted R2 is smaller than R2 and what these numbers mean when
comparing between several models.
4) For which predictors are the regression coefficients significantly
different than zero and how can you select the significant variables?
5) What is the difference between regression and ANOVA and when are
Examples will be given in SPSS and SAS. At the end of the course statistical collaborators from LISA will be on hand to answer specific questions about your research.