This is the fifth post in a series on the multiple linear regression model. In previous posts, I introduced the theory behind the model, exploring using Python’s scikit-learn and statsmodels libraries, and discussed potential problems with the model, such as collinearity and correlation of the error terms. [Read More]

This post is the fourth in a series on the multiple linear regression model. In previous posts, I explored the topic, including methods of relaxing various assumptions made by the model. I also performed a comparison of Python’s scikit-learn and statsmodels libraries for multiple linear regression. [Read More]

This post is the third in a series on the multiple linear regression model. In previous posts, I introduced the multiple linear regression model and explored a comparison of Python’s scikit-learn and statsmodels libraries. However, both of these previous posts exclusively explored quantitative predictors. [Read More]

This post is the second in a series on the multiple linear regression model. In a previous post, I introduced the model and much of it’s associated theory. In this post, I’ll continue exploring the multiple linear regression model with an example in Python, including a comparison of the scikit-learn... [Read More]

Multiple linear regression is an extension of the simple linear regression model. In its simplest form it is a relatively inflexible method that can produce insightful results and accurate predictions under certain conditions. However, multiple linear regression has also been shown to be highly extensible, and many of these extensions... [Read More]