- How Bad is Multicollinearity? - Sep 17, 2019.
For some people anything below 60% is acceptable and for certain others, even a correlation of 30% to 40% is considered too high because it one variable may just end up exaggerating the performance of the model or completely messing up parameter estimates.
- You have created your first Linear Regression Model. Have you validated the assumptions? - Nov 15, 2017.
Linear Regression is an excellent starting point for Machine Learning, but it is a common mistake to focus just on the p-values and R-Squared values while determining validity of model. Here we examine the underlying assumptions of a Linear Regression, which need to be validated before applying the model.
- Pros and Pitfalls of Observational Research - May 3, 2017.
Why the connection between beer brand and region? Climate? Tradition? Or simply distribution? Some combination of the three, plus other factors?
- A Brief Primer on Linear Regression – Part 2 - Jun 13, 2016.
This second part of an introduction to linear regression moves past the topics covered in the first to discuss linearity, normality, outliers, and other topics of interest.
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