Now,

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

- Chapters 22-24 of R 4 Data Science are an excellent overview of the thought process of modeling
- Use
`statsmodels.api`

to make nice regression tables by following this guide (you can use different data though). I used this to create the table on the goodness of fit page - Arthur Turrell's chapter on regression and python.