One-Hot Encoding and Their Coefficients

     When dealing with linear regression models we often have to deal with categorical variables. When I began learning about linear regression models, I found it a bit overwhelming, given that with the multiple assumptions the data needed to meet, understanding how to evaluate the models, trying to find an effective process by which to reduce and/or get the 'best' combination of features for the model, amongst several other things, it felt like a lot to take in.  In these situations, I usually like to create a simple example, with very few 'moving parts', to make sure I understand the fundamentals. I will be doing just that here, by: 1.  Creating a simple data set with categorical variables. 2. Separating the features from the target, and applying one-hot encoding to the features with categorical values (pre-processing). 3. Creating a linear regression model with statsmodels. 4. Making a prediction based on the model. 5. Going over, exactly how the results...