Contents

6.7. Summary and Resources

Now,

  1. You can fit a regression with statsmodels or sklearn

  2. You can view the results visually or numerically of your model with either method

  3. You can measure the goodness of fit on a regression

  4. You can interpret the mechanical meaning of the coefficients for

  5. You understand what a t-stat / p-value does and does not tell you

  6. You are aware of common regression analysis pitfalls and disasters

6.7.1. Extra reading and practice on the topic

  1. Chapters 22-24 of R 4 Data Science are an excellent overview of the thought process of modeling

  2. 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

  3. Arthur Turrell’s chapter on regression and python.

6.7.2. Acknowledgments

  • The demo on diamonds is borrowed from R4DS.

  • DS100

  • Alberto Rossi provided excellent lecture notes

6.6. Significant warnings about “statistical significance” 7. Intro to Machine Learning