"Regression" is a general term for statistical techniques that try to fit a model to a given set of variables to predict the effect that changes in independent variables have on a dependent variable using linear assumptions.
The focus of this brief course is to understand and interpret linear regression analysis output from simple regression, multiple regression, and logistic regression models, using R statistical software. Other topics covered will include evaluating the accuracy of regression models, assumptions, and special cases of regression models, such as ANOVA and ANCOVA.
Instructor:
Linda Farmus, MA. Linda is a PhD student in the Quantitative Methods for Psychology program at York University. Her research interests include statistical suppression, equivalence testing, the teaching of statistics, and fluoride neurotoxicity. Linda was a member of the Statistical Consulting Service at York until 2020.
