1. Looking Back, Looking Forward
Congratulations! You have reached the final lesson of the course. Let's close out with a look at where we've been.
This review also serves to help orient you for what might come next in how you apply what you've seen, to work or to other courses you might take.
2. Exploring Linear Relationships
We started the course by exploring linear relationships with some motivations for building linear models, methods for visualizing linear relationships with matplotlib, and methods for quantifying linear relationships between two variables, such as correlation.
3. Building Linear Models
Next, we looked at the parts that go into building a linear model, including the parameters slope and intercept, in a conceptual context of Taylor Series, and in physical contexts, where we computed optimal values for slope and intercept, using least-squares, numpy, statsmodels, and scikit-learn.
4. Model Predictions
We applied our models to real data and made predictions, exploring some of their limitations, by quantified goodness-of-fit with RMSE and R-squared.
5. Model Parameter Distributions
In our final chapter, we used maximum likelihood and bootstrap resampling to estimate distributions for linear model parameters, and used the results to make probabilistic statements about our confidence in the model parameters.
In so doing, we made connections from linear modeling to inferential statistics.
6. Goodbye and Good Luck!
My hope is that having completed this course, you feel confident and prepared to use what you have learned and excited to continue learning.
Thank you so much for taking the course. I wish you all the best on the road ahead.