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## Levene's test of homogeneity of variance - using R

### Levene's test of homogeneity of variance

#### using absolute residuals from the mean

Gives something like this:

 Call: lm(formula = abs(joh\$res) ~ vetjoh\$trt) Residuals: Min 1Q Median 3Q Max -0.29124 -0.18975 -0.06431 0.08423 1.23705 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.293582 0.054007 5.436 7.98e-07 *** vetjoh\$trtB 0.009168 0.081011 0.113 0.910 vetjoh\$trtC -0.019974 0.075640 -0.264 0.793 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.27 on 68 degrees of freedom Multiple R-Squared: 0.002106, Adjusted R-squared: -0.02724 F-statistic: 0.07175 on 2 and 68 DF, p-value: 0.9308

### Levene's test of homogeneity of variance

#### using absolute residuals from the mean

Gives something like this:

 Analysis of Variance Table Response: con Df Sum Sq Mean Sq F value Pr(>F) trt 2 4108.9 2054.5 7.3555 0.001283 ** Residuals 68 18992.9 279.3 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > summary(lm( abs(joh2\$res) ~ vetjoh\$trt)) Call: lm(formula = abs(joh2\$res) ~ vetjoh\$trt) Residuals: Min 1Q Median 3Q Max -15.625 -6.889 -1.878 2.936 50.175 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 16.289 2.237 7.282 4.39e-10 *** vetjoh\$trtB -5.764 3.355 -1.718 0.0904 . vetjoh\$trtC -8.350 3.133 -2.665 0.0096 ** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 11.18 on 68 degrees of freedom Multiple R-Squared: 0.09772, Adjusted R-squared: 0.07119 F-statistic: 3.682 on 2 and 68 DF, p-value: 0.03031

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