We start our machine learning applications with regression for a few simple reasons:

- Regression is fundamental method for estimating the relationship between a variable ("y") that condition on many ("X") variables.
- But the coefficients obtained can also be used to generate predictions.
*Note: The focus in this section is on RELATIONSHIP paradigm*- Many issues that confront researchers have well understood solutions when regression is the model being used.
- Regression coefficients are easy to interpret.

Overall objectives

After this subchapter,

- You can fit a regression with
`statsmodels`

or`sklearn`

- You can view the results visually or numerically of your model with either method
- You can measure the goodness of fit on a regression
- You can interpret the mechanical meaning of the coefficients for
- continuous variables
- categorical a.k.a qualitative variables with two or more values (aka "dummy", "binary", and "categorical" variables
- interaction terms between two X variables changes interpretation
- variables in models with other controls included (including categorical variables)

- You understand what a t-stat / p-value does and does not tell you
- You are aware of common regression analysis pitfalls and disasters