analysis of variance: inferences about 2 or more means chapter 13 homework: 1, 2, 7, 8, 9
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Analysis of Variance:Inferences about 2 or More Means
Chapter 13
Homework: 1, 2, 7, 8, 9
Analysis of Variance
or ANOVA Procedure for testing hypotheses about 2 or
more means simultaneously e.g., amount of sleep effects on test scores
group 1: 0 hrs
group 2: 4 hrs
group 3: 8 hrs ~
ANOVA: Null Hypothesis
Omnibus H0: all possible H0
H0: 1 = 2 = 3
Pairwise H0: compare each pair of means
H0: 1 = 2
H0: 1 = 3
H0: 2 = 3
ANOVA: assume H0 true for all comparisons ~
ANOVA: Alternative Null Hypothesis
Best way to state: the null hypothesis is false at least one of all the possible H0 is
false Does not tell us which one is false
Post hoc tests (Ch 14) ~
Experimentwise Error
Why can’t we just use t tests? Type 1 error: incorrectly rejecting H0 each comparison = .05 but we have multiple comparisons
Experimentwise probability of type 1 error P (1 or more Type 1 errors)
ANOVA: only one H0 ~
Experimentwise Error
H0: 1 = 2 = 3
Approximate experimentwise error H0: 1 = 2 = .05
H0: 1 = 3 = .05
H0: 2 = 3 = .05
experimentwise .15
ANOVA Notation
Test scores
0 hrs 4 hrs 8 hrs
10 14 22
8 16 14
8 18 16
6 16 20
32 64 72
ANOVA Notation columns = groups
jth group j = 2 = 2d column = group 2 (4hrs)
k = total # groups (columns) k = 3
nj = # observations in group j
n3 = # observations in group 3 ~
ANOVA Notation
sj2 = variance of group j
Xi = ith observation in group
X4 = 4th observation in group
Xij = ith observation in group j
X31 = 3d observation in group 1 ~
ANOVA Notation
subscript G = grand refers to all data points in all groups
taken together Grand mean:
G
ijG
n
XX
Xij = sum of all Xi in all groups = 168
nG = n3 + n2 + n3 = 12 ~
Logic of ANOVA
Assume all groups from same population with same and 2
Comparing means are they far enough apart to reject H0? ask same question for ANOVA
MORE THAN 2 MEANS ~
Logic of ANOVA
ANOVA: 2 point estimates of
Between groups variance of means
Within groups pooled variance of all individual
scores s2
pooled ~
Logic of ANOVA
Are differences between groups (means)
bigger than difference between individuals? If is H0 false then distance between groups should be
larger We will work with groups of equal size
n1 = n2 = n3 Unequal n
different formulas same logic & overall method ~
Mean Square Between Groups also called MSB
Mean Square Between Groups
1
2
2
k
XXs
Gj
X
variance of the group means find deviations from grand mean
)(2 nsMSXB
Mean Square Within Groups
also MSW: Within Groups Variance Pooled variance
pool variances of all groups similar to s2 pooled for t test
k
sssspooled
23
22
212
formula for equal n only different formula for unequal n ~
F ratio
F test Compare the 2 point estimates of 2
W
B
MS
MSF
F ratio
If H0 is true then MSB = MSW then F = 1 if means are far apart then MSB > MSW
F > 1
Set criterion to reject H0
determine how much greater than 1 Test statistic: Fobs
compare to FCV Table A.4 (p 478) ~
F ratio: degrees of freedom
Required to determine FCV ~ df for numerator and denominator of F
dfB = (k - 1) (number of groups) - 1
dfW = (nG - k)
df1 + df2 + df3 +.... + dfk ~ ANOVA nondirectional
even though shade only right tail F is always positive ~
TABLE A.4: Critical values of F (a = .05)
Partitioning Sums of Squares
Sums of Squares sum of squared deviations
2)( GjB XXSS
2)( jijW XXSS
1kdfB
kndf GW
2)( GijT XXSS 1 GT ndf
Partitioning Sums of Squares
Finding Mean Squares MS = variance
B
BB df
SSMS
W
WW df
SSMS
Partitioning Sums of Squares
Calculating observed value of F
W
Bobs MS
MSF
ANOVA Summary Table
Output of most computer programs partitioned SS
_________________________________
Source SS df MS F
Between SSB dfB MSB Fobs
Within SSW dfW MSW
Total SST dfT