Greg and Patrick explore the generalized linear model as a powerful framework for building regression models for binary and other discretely distributed dependent variables. Along the way they also discuss stealing property, statistical conspiracy theories, mic drops, coming uncorked, getting punched by biostatisticians, big logistic, tapping out, the Oakland Raiders, being 8.5 feet tall, sheep bones, cleaning up after the party so your parents don’t find out, arm strength, the regression whisperer, what we giveth we taketh away, and sultry voices.
Additional Show Notes
Agresti, A. (2003). Categorical data analysis (Vol. 482). John Wiley & Sons.
Beaujean, A. A., & Grant, M. B. (2016). Tutorial on using regression models with count outcomes using R. Practical Assessment, Research, and Evaluation, 21, Article 2.
DeMaris, A. (1995). A tutorial in logistic regression. Journal of Marriage and the Family, 956-968.
Huang, F. L. (2019). Alternatives to logistic regression models in experimental studies. The Journal of Experimental Education, 1-16.
Hosmer Jr, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression (Vol. 398). John Wiley & Sons.
Kaplow, J. B., Curran, P. J., & Dodge, K. A. (2002). Child, parent, and peer predictors of early-onset substance use: A multisite longitudinal study. Journal of Abnormal Child Psychology, 30, 199-216.
McCullagh, P., & Nelder, J. A. (2019). Generalized linear models. Routledge.