Chapter 1 Software for modeling

Learning objectives:

• Recognize the principles around which the {tidymodels} packages were designed.
• Classify models as descriptive, inferential, and/or predictive.
• Define descriptive model.
• Define inferential model.
• Define predictive model.
• Differentiate between supervised and unsupervised models.
• Differentiate between regression and classification models.
• Differentiate between quantitative and qualitative data.
• Understand the roles that data can have in an analysis.
• Apply the data science process.
• Recognize the phases of modeling.

The utility of a model hinges on its ability to be reductive. 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.

There are two reasons that models permeate our lives today: an abundance of software exists to create models and it has become easier to record data and make it accessible.