Simulation-Based Inference for Regression

Explore simulation-based techniques for regression inference using R and the infer package. This lesson guides you through constructing bootstrap confidence intervals and conducting permutation tests to interpret population slopes, helping you understand regression relationships beyond theory-based methods.

We'll cover the following...

Confidence interval for a slope

When we interpreted the third through seventh columns of a regression table, we stated that R doesn’t do simulations to compute these values. Rather, R uses theory-based methods that involve mathematical formulas.

We’ll use the simulation-based methods to recreate the values in the regression table. In particular, we’ll use the infer package workflow to:

Construct a 95% confidence interval for the population slope $\beta_1$ using bootstrap resampling with replacement. We did this with the pennies data and with the mythbusters_yawn data.
Conduct a hypothesis test of $H_0\space : \space \beta_1=0$ vs. $H_A\space : \space \beta_1 \neq0$ ...

1.Getting Started with Data in R

2.Data Visualization

3. Data Wrangling

4.Data Importing and “Tidy” Data

5.Basic Regression

6.Multiple Regression

7.Statistical Inference with the infer Package

8.Bootstrapping and Confidence Intervals

9.Hypothesis Testing

10.Inference for Regression

Project

11. Tell a Story with Data

12.Appendix

Project

Simulation-Based Inference for Regression