Diving into the F-test for nested fashions with algorithms, examples and code
When analyzing knowledge, one usually wants to check two regression fashions to find out which one matches finest to a chunk of information. Typically, one mannequin is a less complicated model of a extra advanced mannequin that features extra parameters. Nevertheless, extra parameters don’t at all times assure {that a} extra advanced mannequin is definitely higher, as they might merely overfit the information.
To find out whether or not the added complexity is statistically vital, we will 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) as a result of extra 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, exhibit its implementation utilizing pseudocode, and supply Matlab code that you may run instantly 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 features, on which I didn’t wish 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.