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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.

Class Notes:

-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
  • Draper et al (1993)

-Week 4: Simple linear regression basics
  • Bond & Diamond (2009)
  • Shaffar et al (2013)

-Week 5: Prediction; residual diagnostics; curvature

-Week 6: More curvature; interactions; model building

-Week 7: Categorical predictors
  • Wootton (1997)
  • Morrison et al (1987)
  • Llewellyn et al (2008)

-Week 8: Data transformations
  • Astivia & Kroc (2019)

-Week 9: Mediation and moderation

-Week 10: Likelihood theory and maximum likelihood estimation

-Week 11: Tools for assessing model fit/quality
  • Silva et al (2021) 

-Week 12: Introduction to logistic regression for binary response data

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