Regression Models Course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
Overview: This course provides a comprehensive introduction to regression models, focusing on both theoretical foundations and practical implementation using R. Over approximately 40 hours, learners will progress through key topics including linear regression, multivariable modeling, ANOVA/ANCOVA, and advanced techniques like model selection and smoothing. Each module combines conceptual learning with hands-on programming exercises, culminating in a final project where learners apply regression methods to real data and submit an R Markdown report for peer review.
Module 1: Linear Regression Fundamentals
Estimated time: 10 hours
- Introduction to least squares estimation
- Simple linear regression
- Bias, variance, and regression to the mean
- Fitting, interpreting, and visualizing linear models in R
Module 2: Multivariable Regression
Estimated time: 10 hours
- Regression with multiple predictors
- Understanding confounding and interactions
- Assessing multicollinearity
- Model diagnostics and residual assumption checks
Module 3: ANOVA and ANCOVA Models
Estimated time: 10 hours
- Comparing group means using ANOVA
- Incorporating covariates with ANCOVA
- Interpreting model contrasts
- Handling categorical variables in regression
Module 4: Advanced Regression Techniques
Estimated time: 10 hours
- Model selection using AIC and BIC
- Stepwise regression methods
- Polynomial regression and smoothing
- Non-parametric curve fitting with loess
Module 6: Final Project
Estimated time: 10 hours
- Apply regression techniques to a real-world dataset
- Write a comprehensive analysis report using R Markdown or knitr
- Submit for peer review and receive feedback
Prerequisites
- Familiarity with basic statistical concepts
- Basic knowledge of R programming
- Understanding of data structures and simple plotting in R
What You'll Be Able to Do After
- Understand and explain the assumptions and theory behind regression models
- Implement and interpret linear, multivariable, and ANOVA/ANCOVA models in R
- Use residual plots and diagnostics to evaluate model fit
- Apply variable selection and model-building strategies
- Produce reproducible regression analyses using R Markdown