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Statistics: Scientific method · Research methods · Experimental design · Undergraduate statistics courses · Statistical tests · Game theory · Decision theory
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An F test is any statistical test in which the test statistic has an F-distribution if the null hypothesis is true. The name was coined by George W. Snedecor, in honour of Sir Ronald A. Fisher. Fisher initially developed the statistic as the variance ratio in the 1920s^{[1]}. Examples include:
- The hypothesis that the means of multiple normally distributed populations, all having the same standard deviation, are equal. This is perhaps the most well-known of hypotheses tested by means of an F-test, and the simplest problem in the analysis of variance (ANOVA).
- The hypothesis that a proposed regression model fits well. See Lack-of-fit sum of squares.
- The hypothesis that the standard deviations of two normally distributed populations are equal, and thus that they are of comparable origin.
Note that if it is equality of variances (or standard deviations) that is being tested, the F-test is extremely non-robust to non-normality. That is, even if the data displays only modest departures from the normal distribution, the test is unreliable and should not be used.
Formula and calculationEdit
The formula for an F- test in multiple-comparison ANOVA problems is: F = (between-group variability) / (within-group variability)
(Note: When there are only two groups for the F-test: F-ratio = t^{2} where t is the Student's t statistic.)
In many cases, the F-test statistic can be calculated through a straightforward process. In the case of regression: consider two models, 1 and 2, where model 1 is nested within model 2. That is, model 1 has p_{1} parameters, and model 2 has p_{2} parameters, where p_{2} > p_{1}. (Any constant parameter in the model is included when counting the parameters. For instance, the simple linear model y = mx + b has p = 2 under this convention.) If there are n data points to estimate parameters of both models from, then calculate F as
- $ F=\frac{\left(\frac{\mbox{RSS}_1 - \mbox{RSS}_2 }{p_2 - p_1}\right)}{\left(\frac{\mbox{RSS}_2}{n - p_2}\right)} $ ^{[2]}
where RSS_{i} is the residual sum of squares of model i. If your regression model has been calculated with weights, then replace RSS_{i} with χ^{2}, the weighted sum of squared residuals. F here is distributed as an F-distribution, with (p_{2} − p_{1}, n − p_{2}) degrees of freedom; the probability that the decrease in χ^{2} associated with the addition of p_{2} − p_{1} parameters is solely due to chance is given by the probability associated with the F distribution at that point. The null hypothesis, that none of the additional p_{2} − p_{1} parameters differs from zero, is rejected if the calculated F is greater than the F given by the critical value of F for some desired rejection probability (e.g. 0.05).
Table on F-testEdit
A table of F-test critical values can be found here and is usually included in most statistical texts.
One-way anova exampleEdit
a_{1} | a_{2} | a_{3} |
---|---|---|
6 | 8 | 13 |
8 | 12 | 9 |
4 | 9 | 11 |
5 | 11 | 8 |
3 | 6 | 7 |
4 | 8 | 12 |
a_{1}, a_{2}, and a_{3} are the three levels of the factor that your are studying. To calculate the F- Ratio:
Step 1: calculate the A_{i} values where i refers to the number of the condition. So:
- $ A_1 = \sum a_1 = 6 + 8 + 4 + 5 + 3 + 4 = 30 $
- $ A_2 = \sum a_2 = 8 + 12 + 9 + 11 + 6 + 8 = 54 $
- $ A_3 = \sum a_3 = 13 + 9 + 11 + 8 + 7 + 12 = 60 $
Step 2: calculate Ȳ_{Ai} being the average of the values of condition a_{i}
- $ \overline{Y}_{A1} = \frac{A_1}{n} = \frac{30}{6} = 5 $
- $ \overline{Y}_{A2} = \frac{A_2}{n} = \frac{54}{6} = 9 $
- $ \overline{Y}_{A3} = \frac{A_3}{n} = \frac{60}{6} = 10 $
Step 3 calculate these values:
- Total:
- $ T = \sum A_i = A_1 + A_2 + A_3 = 30 + 54 + 60 = 144 $
- Average overall score:
- $ \overline{Y}_T = \frac{T}{a(n)} = \frac{144}{3(6)} = 8 $
- Where $ a $ = the number of conditions and $ n $ = the number of participants in each condition.
- $ [Y] = \sum{\left(Y^2\right)} = 1304 $
- This is every score in every condition squared and then summed.
- $ [A] = \frac{\sum({A_i}^2)}{n} = 1236 $
- $ [T] = \frac{T^2}{a(n)} = 1152 $
Step 4 calculate the Sum of Squared Terms:
- $ SS_A = [A] - [T] = 84 $
- $ SS_{S/A} = [Y] - [A] = 68 $
Step 5 the Degrees of Freedom are now calculated:
- $ df_a = a-1 = 2 $
- $ df_{S/A} = a(n-1) = 15 $
Step 6 the Means Squared Terms are calculated:
- $ MS_A = \frac{SS_A}{df_A} = 42 $
- $ MS_{S/A} = \frac{SS_{S/A}}{df_{S/A}} = 4.5 $
Step 7 finally the ending F-Ratio is now ready:
- $ F = \frac{MS_A}{MS_{S/A}} = 9.27 $
Step 8 look up the F_{crit} value for the problem:
F_{crit}(2,15) = 3.68 at α = .05 so being that our F value 9.27 ≥ 3.68 the results are significant and one could reject the null hypothesis.
Note F(x, y) notation means that there are x degrees of freedom in the numerator and y degrees of freedom in the denominator.
See alsoEdit
FootnotesEdit
- ↑ Lomax, Richard G. (2007) "Statistical Concepts: A Second Course", p. 10, ISBN 0-8058-5850-4
- ↑ GraphPad Software Inc. How the F test works to compare models. GraphPad Software Inc.
ReferencesEdit
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