Data-Centric Statistical Inference Using R and Tidyverse/

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Observed/Fitted Values and Residuals

Learn how to make conclusions from the results of a regression model with multiple input variables.

We'll cover the following...

In this lesson, we’ll only compute the observed values, fitted values, and residuals for the interaction model, which we saved in score_model_interaction.

Say we have an instructor who identifies as female and is 36 years old. What fitted value $\hat y = \widehat{\texttt{score}}$ will our model yield? Say we have another instructor who identifies as male and is 59 years old. What will their fitted value $\hat y$ be?

We answer this question visually first for the female instructor by finding the intersection of the red regression line and the vertical line at $x =$ age ...

Getting Started with Data in R

Data Visualization

Data Wrangling

Data Importing and “Tidy” Data

Basic Regression

Multiple Regression

Statistical Inference with the infer Package

Bootstrapping and Confidence Intervals

Hypothesis Testing

Inference for Regression

Price Prediction With Regression Analysis in R

Tell a Story with Data

Appendix

Uber Data Analysis Using the R Language

Observed/Fitted Values and Residuals