July 2025
Beginner to intermediate
448 pages
15h 1m
English
Content preview from Statistics Every Programmer Needs
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4 Fitting a linear regression
This chapter covers
- Model fitting
- Model evaluation
- Model assumption tests
- Data exploration, testing for normality, and detecting outliers
Linear regression is a supervised learning method, meaning it uses labeled data—where the input features and corresponding outputs are known for a subset of data—to predict a quantitative response from one or more independent variables. This model is then applied to make predictions on new, unknown data. Although linear regression might now lack the spark of random forests (see chapter 5) and other more contemporary methods, it’s still an “implement” at or near the top of every data scientist’s toolbox. Furthermore, linear regression is a foundational model that is easy ...
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