Chapter 8 Making rigorous conclusions

In this part we introduce modelling and statistical inference for making data-based conclusions. We discuss building, interpreting, and selecting models, visualizing interaction effects, and prediction and model validation. Statistical inference is introduced from a simulation based perspective, and the Central Limit Theorem is discussed very briefly to lay the foundation for future coursework in statistics.

8.1 Slides & application exercises

Unit 2 - Deck 2: Linear models with a single predictor

[Slides] [Source]

Unit 2 - Deck 3: Modeling non-linear relationships

[Slides] [Source]

Unit 2 - Deck 4: Linear models with multiple predictors

[Slides] [Source]

Unit 2 - Deck 5: Model selection

[Slides] [Source]

Unit 2 - Deck 6: Model validation

[Slides] [Source]

Unit 2 - Deck 7: Logistic regression and classification

[Slides] [Source]

Unit 2 - Deck 8: Quantifying uncertainty

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Unit 2 - Deck 9: Hypothesis testing with randomization

[Slides] [Source]

Unit 2 - Deck 10: Inference overview

[Slides] [Source]

Unit 2 - Deck 11: Simulation based inference review

[Slides] [Source]

8.2 Labs

Lab 9: Grading the professor, Pt. 1

Simple linear regression, prediction

[Instructions] [Source] [Starter]

Lab 10: Grading the professor, Pt. 2

Simple and multiple linear regression, model selection

[Instructions] [Source]

Lab 11: So what if you smoke when pregnant?

Inference with bootstrap intervals and randomization testing

[Instructions] [Source]

8.3 Homework assignments

HW 6: Bike rentals in DC

Modeling and visualization

[Instructions] [Source] [Starter]

HW 7: Exploring the General Social Survey

Inference with bootstrap intervals and randomization testing

[Instructions] [Source] [Starter]