a.
Test for Equal Variances: Annual wages versus Metropolitan
Area
99% Bonferroni confidence intervals for standard deviations
Metropolitan Area N Lower StDev Upper
CA and Baltimore 15 4.11038 6.46470 13.2577
MD 15 4.93303 7.75854 15.9111
San Francisco 13 5.81409 9.41615 20.8342
Bartlett's Test (Normal Distribution)
Test statistic = 1.77, p-value = 0.412
Levene's Test (Any Continuous Distribution)
Test statistic = 0.96, p-value = 0.391
From above tests we see that p-value>0.01 so there is not
enough evidence to conclude that the variances in annual wages for
three metropolitan areas are different.
b.


Let, A-true mean annual wages for San Francisco; μ2_ true mean annual wages for CA and Baltimore. μ3= true mean annual uages for MD: Null hypothesis. Ho : μ1-μ2-μ3 Alternative hypothesis. HI : At least one μ¡is different from others Let rijannual wages of jth individual selected from ith metropolitan area Then 11 = m Tij sample mean annual wages for San Francisco j-1 60.9692 712 T,Σ xij = sample mean annual wages for CA and Baltimore r. 2- - 45.8667 713 Tij sample mean annual wages for San Francisco 7l 39.1733 7L ΣΣι,-45.0977 rgrand mean- grand mean
SS due to Between metropolitan areasSSetueen - yn,(T, 3423.119 with df 3-1-2 11 Sbetueen-SSbetueen /2 = 3423. 1 19/2 1711 .56 3 i S due total SSatel 5914.91 with df 43 -1 SS due to error SSerrorSStotal- SSbetueen 2491.791 with df-43-3- 40 MSerror SSerror/40 62.2948 F-ratio = MSbetween/M Serror-1711.56/62.2948 = 27.4752 ANOV A table Source ss df MS Betweern 3423.119 2 1711.56 27.4752 Within (E Total 2491.791 40 62.2948 5914.91 42 p-value = P(F > 27.4752F ~ F2.40) = 0.0000 < 0.10 so we reject Ho and conclude that the mean annual wages equal for all three cities