EPSE 596: Correlational Designs & Analysis
2021/22, Winter Term 1
Course overview:
This course will cover all the basics and essentials of linear regression analysis for application to correlational designs. These methods have a long history and enjoy broad application in all of the natural, social, and health sciences. We will discuss theory and applications. Mathematical foundations will form a component of the material, though our main focus will be on practical applications for researchers in a variety of disciplines.
Recommended textbooks:
- Applied Linear Regression Models, M.H. Kutner, C.J. Nachtscheim, J. Neter. The quintessential reference for linear regression.
- Introduction to Linear Regression Analysis, D.C. Montgomery, E.A. Peck, G.G. Vining. A personal favourite.
This course will cover all the basics and essentials of linear regression analysis for application to correlational designs. These methods have a long history and enjoy broad application in all of the natural, social, and health sciences. We will discuss theory and applications. Mathematical foundations will form a component of the material, though our main focus will be on practical applications for researchers in a variety of disciplines.
Recommended textbooks:
- Applied Linear Regression Models, M.H. Kutner, C.J. Nachtscheim, J. Neter. The quintessential reference for linear regression.
- Introduction to Linear Regression Analysis, D.C. Montgomery, E.A. Peck, G.G. Vining. A personal favourite.
Class Notes:
-Week 1: Introduction and Stats Review 1 (random variables, statistics, standard errors, confidence intervals, Central Limit Theorem)
-Week 2: Stats Review 2 (conditional probability, p-values, Bayes' Theorem, t-tests, F-tests)
-Week 3: Data structures and relationships
-Week 4: Simple linear regression basics
-Week 5: Prediction; residual diagnostics; curvature
-Week 6: More curvature; interactions; model building
-Week 7: Categorical predictors
-Week 8: Data transformations
-Week 9: Mediation and moderation
-Week 10: Likelihood theory and maximum likelihood estimation
-Week 11: Tools for assessing model fit/quality
-Week 12: Introduction to logistic regression for binary response data
-Week 1: Introduction and Stats Review 1 (random variables, statistics, standard errors, confidence intervals, Central Limit Theorem)
- Extra notes on confounding variables from "Handbook of Biological Statistics" (HBS) by John H. McDonald
- Extra notes on probability from HBS
-Week 2: Stats Review 2 (conditional probability, p-values, Bayes' Theorem, t-tests, F-tests)
- Extra notes on standard error of the mean from HBS
- Extra notes on confidence intervals from HBS
- Extra notes on hypothesis testing from HBS
-Week 3: Data structures and relationships
-Week 4: Simple linear regression basics
-Week 5: Prediction; residual diagnostics; curvature
-Week 6: More curvature; interactions; model building
-Week 7: Categorical predictors
-Week 8: Data transformations
-Week 9: Mediation and moderation
-Week 10: Likelihood theory and maximum likelihood estimation
-Week 11: Tools for assessing model fit/quality
-Week 12: Introduction to logistic regression for binary response data
