Skip to main content
grasp.study
About
Explore
Pricing
Log in
Try it for free
Back to course
👋
Welcome to a Grasp lesson
Create a free account
to keep studying and create your own study path.
Learn R programming
·
Module 10
Regression Modeling
1
Simple Linear Regression with `lm()`
Fit and interpret a simple linear regression model using `lm()`.
2
Assessing Linear Regression Assumptions with Diagnostic Plots
Assess linear regression assumptions using diagnostic plots.
3
Multiple Linear Regression Models
Fit and interpret a multiple linear regression model.
4
Binomial Logistic Regression with `glm()`
Fit and interpret a binomial logistic regression model using `glm()`.
5
Log-Odds to Odds Ratios: Interpretation
Convert logistic regression log-odds to odds ratios for interpretation.
6
Tidy and Glance Model Summaries with Broom
Summarize model coefficients and fit statistics using `broom::tidy()` and `broom::glance()`.
Comparing Statistical Models: ANOVA and AIC
Compare nested models using `anova()` and non-nested models using AIC.
Previous module
Hypothesis Testing and Tidy Analysis
Next module
Advanced Statistical Modeling