Unformatted text preview: ndependent simple random samples, one
from each of m populations
• Each population i is normally disributed about
its unknown mean µi
– If sample size is large enough, the Central
Limit Theorem will kickin and inferences
based on sample means will be OK even if
the populations distributions are not exactly normal.
– If you don’t have normality, you could use
a nonparametric test, such the KruskallWallis test which is based on the ranks
of the yvalues rather than the yvalues
themselves.
• The populations have the same variance, σ 2 – You can use Levene’s Test (in the car library) to test for nonconstant variance. 19 20 Step two: individual ttests with correction for multiple comparisons H0 : σ1 = σ2 = σ3
> leveneTest(rate, group)
Levene’s Test for Homogeneity of Variance
Df F value Pr(>F)
group 2 0.0028 0.9973
42 100 Since the pvalue is not less than 0.05, we
do not reject. Constant variance is reasonable. 80 90 ● • If we reject the overall F test, we proceed to
further analysis.
• The most common tests of interest are ‘all
pairwise comparisons’ (µ1 vs. µ2, µ1 vs. µ3,
..., µm−1 vs. µm,).
• We can use the Bonferroni
correction to
m
make sure the set of all
tests is do...
View
Full
Document
This note was uploaded on 06/12/2013 for the course MATHEMATIC MAT7870 taught by Professor Sun during the Winter '13 term at Wayne State University.
 Winter '13
 Sun
 Statistics, Linear Regression, Variance

Click to edit the document details