Partial F-Test for Variable Selection in Linear Regression | R Tutorial 5.11| MarinStatsLectures

Partial F-Test for Variable Selection in Linear Regression with R: Learn how to use Partial F-test to compare nested models for regression modelling in R with examples.
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The Partial F-test (also know as incremental F-test or an extra sum of squares F-test) is a useful tool for variable selection when building a regression model. You will also learn what the Sum of Square Error is, and its use in The Partial F-test.

The partial F- test is used to determine whether the extra variables provide enough extra explanatory power as a group to warrant their inclusion in the equation. In other words, the partial F-test tests whether the full model is significantly better than the reduced model.

An F-test is any statistical test in which the test statistic has an F-distribution under the null hypothesis. It is most often used when comparing statistical models that have been fitted to a data set, in order to identify the model that best fits the population from which the data were sampled.

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