We’ve already seen what F-score is. Now let’s see what
F-test. Side note: I came across it when I was writing
Elbow Method and my thoughts were, cool another F-word for my readers, so
Here you go:
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F-test is any stats test that uses F-distribution
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It is often used when comparing stats models that have been fitted to a data set.. Ahh.. That
sounds no different from F-score then.. May be just different
fields(Statistics and Machine Learning) have different naming conventions?? Anyway two different
F-words.. So let’s just say what F-score/test?? Why two names for samething and move on…
Examples:
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Null Hypothesis: Means of a given set of normally distributed populations all having same standard deviation being equal.(used in ANOVA)
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The hypothesis that a proposed regression model fits the data well.
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The hypothesis that a data set in a regression analysis follows the simpler of two proposed linear models that are nested within each other.
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It(non-regression type) is also a test of homoskedasticity
Drawbacks:
- F-test is sensitive to non-normality.
- Regression F-test is sensitive to homoskedasticity
)
Formula
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Formula:
or
Ok. That doesn’t sound like the F-score
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Formula(for regression models):