## Thursday, May 29, 2014

### Brown-Forsythe F-Test in 4 Steps in Excel as Substitute For Single-Factor ANOVA When Sample Variances Are Not Similar

This is one of the following sixteen articles on Single-Factor ANOVA in Excel

Overview of Single-Factor ANOVA

Single-Factor ANOVA in 5 Steps in Excel 2010 and Excel 2013

Shapiro-Wilk Normality Test in Excel For Each Single-Factor ANOVA Sample Group

Kruskal-Wallis Test Alternative For Single Factor ANOVA in 7 Steps in Excel 2010 and Excel 2013

Levene’s and Brown-Forsythe Tests in Excel For Single-Factor ANOVA Sample Group Variance Comparison

Single-Factor ANOVA - All Excel Calculations

Overview of Post-Hoc Testing For Single-Factor ANOVA

Tukey-Kramer Post-Hoc Test in Excel For Single-Factor ANOVA

Games-Howell Post-Hoc Test in Excel For Single-Factor ANOVA

Overview of Effect Size For Single-Factor ANOVA

ANOVA Effect Size Calculation Eta Squared in Excel 2010 and Excel 2013

ANOVA Effect Size Calculation Psi – RMSSE – in Excel 2010 and Excel 2013

ANOVA Effect Size Calculation Omega Squared in Excel 2010 and Excel 2013

Power of Single-Factor ANOVA Test Using Free Utility G*Power

Welch’s ANOVA Test in 8 Steps in Excel Substitute For Single-Factor ANOVA When Sample Variances Are Not Similar

Brown-Forsythe F-Test in 4 Steps in Excel Substitute For Single-Factor ANOVA When Sample Variances Are Not Similar

# Brown-Forsythe F-Test in 4 Steps in Excel as a Substitute for Single- Factor ANOVA When Samples Groups Do Not Have Similar Variances

Single-Factor ANOVA requires that the variances of all sample groups be similar. Sample groups that have similar variances are said to be homoscedastistic. Sample groups that have significantly different variances are said to be heteroscedastistic.

When groups cannot be shown to have homogeneous (similar) variances, either Welch’s ANOVA or the Brown-Forsythe F-test should be used in place of Single-Factor ANOVA. This blog article will perform the Brown-Forsythe F-test on the original data set of this example.

The Brown-Forsythe F-test calculates a p Value just like Single-Factor ANOVA that determines whether the differences between the sample group means is significant. The Excel formula for the p value that determines whether or not the Brown-Forsythe F-test shows that at least one group mean is significantly different than the others is the following:

p Value = F.DIST.RT(FF-B, dfBetween, dfWithin)

This formula requires the calculation of the Brown-Forsythe F Value (FF-B), dfBetween, and dfWithin. This is shown as follows: (Click Image To See a Larger Version) (Click Image To See a Larger Version)  (Click Image To See a Larger Version)

SSBetween_Groups is taken from the Single-Factor ANOVA output shown here and equals 16.079.

It can then be solved as follows:

### Step 4) Calculate FB-F and the p Value

This Brown-Forsythe F- Test shows the differences between group means to be significant at a Level of Significance (Alpha) of 0.05 since the p Value (0.0378) is less than Alpha (0.05).

The p Value formula shown here is used in Excel versions prior to 2010. The equivalent formula in Excel 2010 and later is the following:

p Value = F.DIST.RT(FF-B, dfBetween, dfWithin)

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Statistical Topics and Articles In Each Topic

• Histograms in Excel
• Bar Chart in Excel
• Combinations & Permutations in Excel
• Normal Distribution in Excel
• t-Distribution in Excel
• Binomial Distribution in Excel
• z-Tests in Excel
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• Hypothesis Tests of Proportion in Excel
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• Pearson Correlation in Excel
• Spearman Correlation in Excel
• Confidence Intervals in Excel
• Simple Linear Regression in Excel
• Multiple Linear Regression in Excel
• Logistic Regression in Excel
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• Two-Factor ANOVA With Replication in Excel
• Two-Factor ANOVA Without Replication in Excel
• Randomized Block Design ANOVA in Excel
• Repeated-Measures ANOVA in Excel
• ANCOVA in Excel
• Normality Testing in Excel
• Nonparametric Testing in Excel
• Post Hoc Testing in Excel
• Creating Interactive Graphs of Statistical Distributions in Excel
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• Chi-Square Population Variance Test in Excel
• Analyzing Data With Pivot Tables
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