A Minimal Book Example
1
Prerequisites
2
Parallel Slopes
What if you have two groups
2.1
Fitting a parallel slopes model
Exercise
Reasoning about two intercepts
2.2
Visualizing parallel slopes models
2.3
Using
geom_line()
and
augment()
Exercise
Interpreting parallel slopes coefficients
Intercept interpretation
Common slope interpretation
Three ways to describe a model
2.4
Syntax from math
Exercise
2.5
Syntax from plot
Exercise
3
Evaluating and extending parallel slopes model
Model fit, residuals, and prediction
3.1
R-squared vs. adjusted R-squared
Exercise
3.2
Prediction
Exercise
Understanding interaction
Thought experiments
3.3
Fitting a model with interaction
Exercise
3.4
Visualizing interaction models
Exercise
Simpson’s paradox
3.5
Consequences of Simpson’s paradox
3.6
Simpson’s paradox in action
Exercise
4
Multiple Regression
4.1
Fitting a MLR model
4.2
Tiling the plane
Exercise
4.3
Models in 3D
Exercise
Coefficient magnitude
Practicing interpretation
4.4
Visualizing parallel planes
Exercise
Parallel plane interpretation
Interpretation of coefficient in a big model
5
Logistic Regression
What is logistic regression?
5.1
Fitting a line to a binary response
Exercise
5.2
Fitting a line to a binary response (2)
Exercise
5.3
Fitting a model
Exercise
5.3.1
Visualizing logistic regression
5.4
Using geom_smooth()
Exercise
5.5
Using bins
Exercise
5.5.1
Three scales approach to interpretation
5.6
Odds scale
Exercise
5.7
Log-odds scale
Exercise
Interpretation of logistic regression
Using a logistic model
5.8
Making probabilistic predictions
Exercise
5.9
Making binary predictions
Exercise
6
Chapter 6 Case Study: Italian restaurants in NYC
Italian restaurants in NYC
Exploratory data analysis
Exercise
6.1
SLR models
Exercise
Incorporating another variable
Exercise
6.1.1
Visualizing logistic regression
6.2
Parallel lines with location
6.3
A plane in 3D
Exercise
Higher dimensions
6.4
Parallel planes with location
Exercise
Interpretation of location coefficient
6.5
Impact of location
Exercise
6.6
Full Model
Wrap up
References
Published with bookdown
Multiple and Logistic Regression
References