# 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 1: The language of models

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

Unit 2 - Deck 3: Modeling non-linear relationships

Unit 2 - Deck 4: Linear models with multiple predictors

Unit 2 - Deck 5: Model selection

Unit 2 - Deck 6: Model validation

Unit 2 - Deck 7: Logistic regression and classification

Unit 2 - Deck 8: Quantifying uncertainty

Unit 2 - Deck 9: Hypothesis testing with randomization

Unit 2 - Deck 10: Inference overview

Unit 2 - Deck 11: Simulation based inference review

## 8.2 Labs

Lab 9: Grading the professor, Pt. 1

Simple linear regression, prediction

Lab 10: Grading the professor, Pt. 2

Simple and multiple linear regression, model selection

Lab 11: So what if you smoke when pregnant?

Inference with bootstrap intervals and randomization testing

## 8.3 Homework assignments

HW 6: Bike rentals in DC

Modeling and visualization

HW 7: Exploring the General Social Survey

Inference with bootstrap intervals and randomization testing