Ch. 1 - Parallel Slopes
What if you have two groups?
Fitting a parallel slopes model
Reasoning about two intercepts
Visualizing parallel slopes models
Using geom_line() and augment()
Interpreting parallel slopes coefficients
Intercept interpretation
Common slope interpretation
Three ways to describe a model
Syntax from math
Syntax from plot
Ch. 2 - Evaluating and extending parallel slopes model
Model fit, residuals, and prediction
R-squared vs. adjusted R-squared
Prediction
Understanding interaction
Thought experiments
Fitting a model with interaction
Visualizing interaction models
Simpson’s Paradox
Consequences of Simpson’s paradox
Simpson’s paradox in action
Ch. 3 - Multiple Regression
Adding a numerical explanatory variable
Fitting a MLR model
Tiling the plane
Models in 3D
Conditional interpretation of coefficients
Coefficient magnitude
Practicing interpretation
Adding a third (categorical) variable
Visualizing parallel planes
Parallel plane interpretation
Higher dimensions
Interpretation of coefficient in a big model
Ch. 4 - Logistic Regression
What is logistic regression?
Fitting a line to a binary response
Fitting a line to a binary response (2)
Fitting a model
Visualizing logistic regression
Using geom_smooth()
Using bins
Three scales approach to interpretation
Odds scale
Log-odds scale
Interpretation of logistic regression
Using a logistic model
Making probabilistic predictions
Making binary predictions
Ch. 5 - Case Study: Italian restaurants in NYC
Italian restaurants in NYC
Exploratory data analysis
SLR models
Incorporating another variable
Parallel lines with location
A plane in 3D
Higher dimensions
Parallel planes with location
Interpretation of location coefficient
Impact of location
Full model
Wrap-up
About Michael Mallari
Michael is a hybrid thinker and doer—a byproduct of being a StrengthsFinder “Learner” over time. With nearly 20 years of engineering, design, and product experience, he helps organizations identify market needs, mobilize internal and external resources, and deliver delightful digital customer experiences that align with business goals. He has been entrusted with problem-solving for brands—ranging from Fortune 500 companies to early-stage startups to not-for-profit organizations.
Michael earned his BS in Computer Science from New York Institute of Technology and his MBA from the University of Maryland, College Park. He is also a candidate to receive his MS in Applied Analytics from Columbia University.
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