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Eberly College of Science

STAT 462

Applied Regression Analysis

R Software Help

The following pages contain details of R code to carry out the procedures detailed in each Lesson.

R Help 1: Foundations
R Help 2: SLR Model
R Help 3: SLR Evaluation
R Help 4: SLR Assumptions, Estimation & Prediction
R Help 5: MLR Model & Evaluation
R Help 6: MLR Assumptions, Estimation & Prediction
R Help 7: Transformations & Interactions
R Help 8: Categorical Predictors
R Help 9: Influential Points
R Help 10: Regression Pitfalls
R Help 11: Model Building
R Help 12: Logistic, Poisson & Nonlinear Regression

R Help 1: Foundations ›

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Lessons

Lesson 1: Statistical Inference Foundations
Lesson 2: Simple Linear Regression (SLR) Model
Lesson 3: SLR Evaluation
Lesson 4: SLR Assumptions, Estimation & Prediction
Lesson 5: Multiple Linear Regression (MLR) Model & Evaluation
Lesson 6: MLR Assumptions, Estimation & Prediction
Lesson 7: Transformations & Interactions
Lesson 8: Categorical Predictors
Lesson 9: Influential Points
Lesson 10: Regression Pitfalls
Lesson 11: Model Building
Lesson 12: Logistic, Poisson & Nonlinear Regression

Resources

Website for Applied Regression Modeling, 2nd edition
Notation Used in this Course
R Software Help
Minitab Software Help

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