When analyzing information, one usually wants to match two regression fashions to find out which one matches greatest to a chunk of knowledge. Typically, one mannequin is a easier model of a extra advanced mannequin that features further parameters. Nevertheless, extra parameters don’t at all times assure {that a} extra advanced mannequin is definitely higher, as they may merely overfit the information.
To find out whether or not the added complexity is statistically vital, we are able to use what’s known as the F-test for nested fashions. This statistical method evaluates whether or not the discount within the Residual Sum of Squares (RSS) because of the further parameters is significant or simply resulting from likelihood.
On this article I clarify the F-test for nested fashions after which I current a step-by-step algorithm, display its implementation utilizing pseudocode, and supply Matlab code which you can run immediately or re-implement in your favourite system (right here I selected Matlab as a result of it gave me fast entry to statistics and becoming capabilities, on which I didn’t need to spend time). All through the article we are going to see examples of the F-test for nested fashions at work in a few settings together with some examples I constructed into the instance Matlab code.