A typical day for many people might involve checking the weather to see when might be a good time to walk the dog, ordering a product from a website, typing a text message to a friend and having it autocorrected, and checking email. Since the beginning of the twenty-first century, mathematical models have become ubiquitous in our daily lives, in both obvious and subtle ways.
The primary influences in the data can be captured mathematically in a useful way, such as in a relationship that can be expressed as an equation. The utility of a model hinges on its ability to be reductive. Models can be used for various purposes, including predicting future events, determining if there is a difference between several groups, aiding map-based visualization, discovering novel patterns in the data that could be further investigated, and more. Models are mathematical tools that can describe a system and capture relationships in the data given to them.
21.1 An example: Inference regarding counts. 20.1 Creating the training set for stacking. 19 When should you trust your predictions?. 18.4 Building global explanations from local explanations. 17.4.4 Uniform manifold approximation and projection. 17.1 A picture is worth a thousand… beans. 14.3.1 Aspects of simulated annealing search. 13.4 Tools for creating tuning specifications. 12.4 Two general strategies for optimization. 12.3 The consequences of poor parameter estimates. 12.1 Tuning parameters for different types of models. 12 Model tuning and the dangers of overfitting. 10.2.4 Rolling forecasting origin resampling. 10 Resampling for evaluating performance. 8.4.1 Encoding qualitative data in a numeric format. 8.1 A simple recipe for the Ames housing data. 7.5 Creating multiple workflows at once. 7.4.1 Special formulas and in-line functions. 7.4 How does a workflow use the formula?. 7.3 Adding raw variables to the workflow. 7.1 Where does the model begin and end?. 3.4 Combining base R models and the tidyverse. 3.3 Why tidiness is important for modeling. 2.1.3 Design for the pipe and functional programming. 
1.3 How does modeling fit into the data analysis process?.
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