ANOVA  Analysis of Variance


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1 ANOVA  Analysis of Variance
2 ANOVA  Analysis of Variance Extends independentsamples t test Compares the means of groups of independent observations Don t be fooled by the name. ANOVA does not compare variances. Can compare more than two groups
3 ANOVA Null and Alternative Hypotheses Say the sample contains K independent groups ANOVA tests the null hypothesis H 0 : μ 1 = μ 2 = = μ K That is, the group means are all equal The alternative hypothesis is H 1 : μ i μ j for some i, j or, the group means are not all equal
4 Example: Accuracy of Implant Placement Implants were placed in a manikin using placement guides of various widths. 15 implants were placed using each guide. Error (discrepancies with a reference implant) was measured for each implant. Mean Implant Height Error (mm) Mean Error by Guide Width 4mm 6mm 8mm Guide Width
5 Example: Accuracy of Implant Placement The overall mean of the entire sample was mm. This is called the grand mean, and is often denoted by X. If H 0 were true then we d expect the group means to be close to the grand mean. Mean Implant Height Error (mm) Mean Error by Guide Width 4mm 6mm 8mm Guide Width
6 Example: Accuracy of Implant Placement The ANOVA test is based on the combined distances from X. If the combined distances are large, that indicates we should reject H 0. Mean Implant Height Error (mm) Mean Error by Guide Width 4mm 6mm 8mm Guide Width
7 The Anova Statistic To combine the differences from the grand mean we Square the differences Multiply by the numbers of observations in the groups Sum over the groups ( ) 2 ( ) 2 ( ) X X + 15 X X + X 2 SSB = 15 X 4mm 6mm 15 where the are the group means. X * 8mm SSB = Sum of Squares Between groups Note: This looks a bit like a variance.
8 How big is big? For the Implant Accuracy Data, SSB = Is that big enough to reject H 0? As with the t test, we compare the statistic to the variability of the individual observations. In ANOVA the variability is estimated by the Mean Square Error, or MSE
9 MSE Mean Square Error Implant Height Error by Guide Width The Mean Square Error is a measure of the variability after the group effects have been taken into account. 1 MSE = N K ( x ij X j ) where x ij is the i th observation in the j th group. j i 2 Implant Height Error (mm) mm 6mm 8mm Guide Width
10 MSE Mean Square Error Implant Height Error by Guide Width The Mean Square Error is a measure of the variability after the group effects have been taken into account. 1 MSE = N K = ( x ij X j ) Note that the variation of the means seems quite small compared to the variance of observations within groups j i 2 Implant Height Error (mm) mm 6mm 8mm Guide Width
11 Notes on MSE If there are only two groups, the MSE is equal to the pooled estimate of variance used in the equalvariance t test. ANOVA assumes that all the group variances are equal. Other options should be considered if group variances differ by a factor of 2 or more.
12 ANOVA F Test The ANOVA F test is based on the F statistic F = SSB ( K 1) MSE where K is the number of groups. Under H 0 the F statistic has an F distribution, with K1 and NK degrees of freedom (N is the total number of observations)
13 Implant Data: F test pvalue F(2,42) distribution To get a pvalue we compare our F statistic to an F(2, 42) distribution F
14 Implant Data: F test pvalue F(2,42) distribution To get a pvalue we compare our F statistic to an F(2, 42) distribution. In our example F = = The pvalue is P ( F(2,42) >. 211) = P = F
15 ANOVA Table Results are often displayed using an ANOVA Table Sum of Squares df Mean Square F Sig. Between Groups Within Groups Total Sum of Squares Between (SSB) Mean Square Error (MSE) F Statistic p value
16 Post Hoc Tests NHANES I data, women yrs old. Compare cholesterol between periodontal groups. The ANOVA shows good evidence (p = 0.002) that the means are not all the same. Sum of Squares Mean Square F Sig. df Between Groups Within Groups Total Which means are different? Can directly compare the subgroups using post hoc tests.
17 Least Significant Difference test Std. N Mean Deviation Healthy Gingivitis Periodontitis Edentulous Sum of Squares Mean Square F Sig. df Between Groups Within Groups Total The most simple post hoc test is called the Least Significant Difference Test. The computation is very similar to the equalvariance t test. Compute an equalvariance t test, but replace the pooled standard deviation, s pooled, with.
18 Least Significant Difference Test: Examples Std. N Mean Deviation Healthy Gingivitis Periodontitis Edentulous Compare Healthy group to Periodontitis group: Pvalue = Sum of Squares Mean Square F Sig. df Between Groups Within Groups Total Compare Gingivitis group to Periodontitis group: Pvalue =
19 Post Hoc Tests: Multiple Comparisons Posthoc testing usually involves multiple comparisons. For example, if the data contain 4 groups, then 6 different pairwise comparisons can be made Healthy Gingivitis Periodontitis Edentulous
20 Post Hoc Tests: Multiple Comparisons Each time a hypothesis test is performed at significance level α, there is probability α of rejecting in error. Performing multiple tests increases the chances of rejecting in error at least once. For example: if you did 6 independent hypothesis tests at the α = 0.05 If, in truth, H 0 were true for all six. The probability that at least one test rejects H 0 is 26% P(at least one rejection) = 1P(no rejections) = =.26
21 Bonferroni Correction for Multiple Comparisons The Bonferroni correction is a simple way to adjust for the multiple comparisons. Bonferroni Correction Perform each test at significance level α. Multiply each pvalue by the number of tests performed. The overall significance level (chance of any of the tests rejecting in error) will be less than α.
22 Example: Cholesterol Data posthoc comparisons Group 1 Group 2 Mean Difference (Group 1  Group 2) Least Significant Difference pvalue Bonferroni pvalue Healthy Gingivitis Healthy Periodontitis Healthy Edentulous Gingivitis Periodontitis Gingivitis Edentulous Periodontitis Edentulous Conclusion: The Edentulous group is significantly different than the Healthy group and the Gingivitis group (p < 0.05), after adjustment for multiple comparisons
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