Foundations of Regression and Predictive Modeling is designed to introduce students to the fundamental concepts behind the relationships between variables, more commonly known as regression modeling. Learners will be exposed not only to a theoretical background of the regression models, but all models will be extensively demonstrated using regression. The emphasis throughout will be on hypothesis testing, model selection, goodness of fit, and prediction.
What You Will Learn
- Learn the key ideas behind regression models.
- Apply the ideas and analysis to various types of regression model.
- Understand and interpret the output from an analysis.
- Understand key procedures such as hypothesis testing, prediction, and Bayesian methods.
- Foundations and Ideas, Simple Linear Model, Correlation; Estimation; Testing.
- Multiple Linear Regression, Vector and matrix notation; Colinearity; Ridge regression.
- Bayes Linear Model; Conjugate model; Prior to posterior analysis; Bayes factor.
- Variable Selection, LASSO, Principal component analysis; Bayesian methods.
- ANOVA Models, One-way ANOVA; Two-way ANOVA; ANOVA Table; F-tests.
- Moderation & Interaction, Testing for interaction; Sobel test.
- Nonlinear Regression, Iterative estimation algorithms; Bootstrap.
- Poisson regression, Analysis of count data, Weighted linear model.
- Generalized Linear Model, Exponential family; GLM theory; Logistic regression.
- Nonparametric Regression, Kernel smoothing; Splines; Regression trees.
- Mixed Effects Model, Fixed and random effects; EM algorithm; Gibbs sampler.
- Multiclass Regression, Classification tree; Multinomial logistic regression.
- Spring 2023