m17076 F Distribution and ANOVA: The F Distribution And The F Ratio 1.7 2008/06/23 14:40:52 GMT-5 2009/02/05 18:35:58.974 US/Central Susan Dean Susan Dean deansusan@deanza.edu Barbara Illowsky Dr. Barbara Illowsky illowskybarbara@deanza.edu Susan Dean Susan Dean deansusan@deanza.edu Barbara Illowsky Dr. Barbara Illowsky illowskybarbara@deanza.edu Connexions Connexions cnx@cnx.org Maxfield Foundation Maxfield Foundation cnx@cnx.org alternate hypothesis ANOVA degrees of freedom F Distribution F Ratio hypothesis test means square null hypothesis One-Way Analysis of Variance population sample Sir Ronald Fisher statistics sum of squares variance Mathematics and Statistics This module describes how to calculate the F Ratio and F Distribution based on the hypothesis test for the ANOVA. en The distribution used for the hypothesis test is a new one. It is called the F distribution, named after Sir Ronald Fisher, an English statistician. The F statistic is a ratio (a fraction). There are two sets of degrees of freedom; one for the numerator and one for the denominator.For example, if F follows an F distribution and the degrees of freedom for the numerator are 4 and the degrees of freedom for the denominator are 10, then F ~ F 4 , 10 .To calculate the F ratio, two estimates of the variance are made.Variance between samples: An estimate of σ 2 that is the variance of the sample means. If the samples are different sizes, the variance between samples is weighted to account for the different sample sizes. The variance is also called variation due to treatment or explained variation. Variance within samples: An estimate of σ 2 that is the average of the sample variances (also known as a pooled variance). When the sample sizes are different, the variance within samples is weighted. The variance is also called the variation due to error or unexplained variation. SS between = the sum of squares that represents the variation among the different samples. SS within = the sum of squares that represents the variation within samples that is due to chance.To find a "sum of squares" means to add together squared quantities which, in some cases, may be weighted. We used sum of squares to calculate the sample variance and the sample standard deviation in Descriptive Statistics.MS means "mean square." MS between is the variance between groups and MS within is the variance within groups.k size 12{k} {} = the number of different groups nj size 12{n rSub { size 8{j} } } {} = the size of the jth size 12{ ital "jth"} {} group sj size 12{s rSub { size 8{j} } } {}= the sum of the values in the jth size 12{ ital "jth"} {} group N size 12{N} {} = total number of all the values combined. (total sample size: ∑nj size 12{ Sum {n rSub { size 8{j} } } } {}) x = one value: ∑x=∑sj size 12{ Sum {x} = Sum {s rSub { size 8{j} } } } {} Sum of squares of all values from every group combined: ∑x2 size 12{ Sum {x rSup { size 8{2} } } ={}} {} Between group variability: SS total = ∑ x 2 − ∑ x 2 N size 12{ ital "SS" rSub { size 8{ ital "total"} } = Sum {x rSup { size 8{2} } } - { { left ( Sum {x} right ) rSup { size 8{2} } } over {N} } } {} Total sum of squares: ∑ x 2 − ( ∑ x ) 2 N size 12{ Sum {x rSup { size 8{2} } } - { { \( Sum {x} \) rSup { size 8{2} } } over {N} } } {} Explained variation- sum of squares representing variation among the different samples SS between = ∑ [ ( sj ) 2 n j ] − ( ∑ s j ) 2 N size 12{ ital "SS" rSub { size 8{ ital "between"} } = Sum { \[ { { \( ital "sj" \) rSup { size 8{2} } } over {n rSub { size 8{j} } } } \] } - { { \( Sum {s rSub { size 8{j} } \) rSup { size 8{2} } } } over {N} } } {} Unexplained variation- sum of squares representing variation within samples due to chance: SS within = SS total − SS between size 12{ ital "SS" rSub { size 8{ ital "within"} } = ital "SS" rSub { size 8{ ital "total"} } - ital "SS" rSub { size 8{ ital "between"} } } {} df's for different groups (df's for the numerator): df between = k − 1 size 12{ ital "df" rSub { size 8{ ital "between"} } =k - 1} {} Equation for errors within samples (df's for the denominator): df within = N − k size 12{ ital "df" rSub { size 8{ ital "within"} } =N - k} {} Mean square (variance estimate) explained by the different groups: MS between = SS between df between size 12{ ital "MS" rSub { size 8{ ital "between"} } = { { ital "SS" rSub { size 8{ ital "between"} } } over { ital "df" rSub { size 8{ ital "between"} } } } } {} Mean square (variance estimate) that is due to chance (unexplained): MS within = SS within df within size 12{ ital "MS" rSub { size 8{ ital "within"} } = { { ital "SS" rSub { size 8{ ital "within"} } } over { ital "df" rSub { size 8{ ital "within"} } } } } {} MS between and MS within can be written as follows: MS between = SS between df between = SS between k − 1 MS within = SS within df within = SS within N − k The ANOVA test depends on the fact that MS between can be influenced by population differences among means of the several groups. Since MS within compares values of each group to its own group mean, the fact that group means might be different does not affect MS within .The null hypothesis says that all groups are samples from populations having the same normal distribution. The alternate hypothesis says that at least two of the sample groups come from populations with different normal distributions. If the null hypothesis is true, MS between and MS within should both estimate the same value.The null hypothesis says that all the group population means are equal. The hypothesis of equal means implies that the populations have the same normal distribution because it is assumed that the populations are normal and that they have equal variances. F = MS between MS within If MS between and MS within estimate the same value (following the belief that H o is true), then the F-ratio should be approximately equal to 1. Only sampling errors would contribute to variations away from 1. As it turns out, MS between consists of the population variance plus a variance produced from the differences between the samples. MS within is an estimate of the population variance. Since variances are always positive, if the null hypothesis is false, MS between will be larger than MS within . The F-ratio will be larger than 1.The above calculations were done with groups of different sizes. If the groups are the same size, the calculations simplify somewhat and the F ratio can be written as: F = n ⋅ ( s − x ) 2 ( s pooled ) 2 size 12{F= { {n cdot \( s rSub { size 8{ - x} } \) rSup { size 8{2} } } over { \( s rSub { size 8{ ital "pooled"} } \) rSup { size 8{2} } } } } {} (s−x)2= size 12{ \( s rSub { size 8{ - x} } \) rSup { size 8{2} } ={}} {}the variance of the sample means n= size 12{n={}} {}the sample size of each group (spooled)2= size 12{ \( s rSub { size 8{ ital "pooled"} } \) rSup { size 8{2} } ={}} {}the mean of the sample variances (pooled variance) df numerator = k − 1 size 12{ ital "df" rSub { size 8{ ital "numerator"} } =k - 1} {} df denominator = k ( n − 1 ) = N − k size 12{ ital "df" rSub { size 8{ ital "denominator"={}} } k \( n - 1 \) =N - k} {} The ANOVA hypothesis test is always right-tailed because larger F-values are way out in the right tail of the F-distribution curve and tend to make us reject H o .

The notation for the F distribution is F ~ F df(num) , df(denom) where df(num) = df between and df(denom) = df within The mean for the F distribution is μ = df(num) df(denom) − 1