Index 761Fisher’s exact test, 147–148
versus Pearson’s chi square, 148
Fisher’s least significant difference (LSD), 389
Fisher’s protected t, 389
fitting a line to data, 257–258
fitting a normal curve, 21–22
fitting smooth lines to data, 21–24
fixed and random marginals, 148
fixed-model analysis of variance, 343
fixed variable, 246
fixed versus random models, 343
forward selection, 549
fractiles, 52
frequency data, 4
frequency distributions, 17–18
frequentist view, 113
Friedman’s rank test for kcorrelated samples, 684–686
Fstatistic, 328
G*Power to simplify calculations, 238–239, 353–354
gain scores, 197
Galton, Francis, 70
gamma function, 140
Gauss, Carl Friedrich, 69
general linear model, 580–583
design matrix, 581–583
linear model, 581
generating class, 645
geometric mean, 636
geometric representation of multiple regression, 535–535
goodness-of-fit test, 142
guesses, 5
harmonic mean, 234
heavy-tailed distributions, 341
heterogeneity of variance, 321
caution, 216
the robustness of t with heterogeneous variances, 215
sampling distribution of , 214
testing for heterogeneity of variance, 214–215
heterogeneous subsamples, 282
heterogeneous variances, 213
heteroscedasticity, 321
hierarchical and nonhierarchical models, 431, 544, 644–645
hierarchical sums of squares, 595
higher-order factorial designs, 446–453
simple effects, 450
simple interaction effects, 451
variables affecting driving performance, 447
Histograms, 18–21
Holm and Larzelere and Mulaik tests, 380
homogeneity of regression, 600
homogeneity of variance, 213, 320
homogeneity of variance in arrays, 264
homoscedasticity, 320
hyperspace, 534
hypothesis testing, 87, 126–127, 271–278
an alternative view of, 102–114
confidence limits on Y, 276–277
confidence limits versus tests of significance, 277
testing the difference between two
independent bs, 273–274
testing the difference between two independent rs, 275
testing the difference between two
nonindependent rs, 277–278
testing the hypothesis that requals any
specified value, 275–276
testing the significance of b, 272–273
testing the significance of r, 271–272
hypothesis testing, theory
traditional approach to hypothesis testing, 91–92
hypothesis testing revisited, 216–217
hypothesis tests applied to means, 179–217
heterogeneity of variance, the Behrens-Fisher
Problem, 213–216
hypothesis testing revisited, 216–217
hypothesis tests applied to means, two independent
samples, 203–211
hypothesis tests applied to means, two matched
samples, 194–203
sampling distribution of the mean, 180–183
testing a sample mean when s is unknown, the one-sample
ttest, 185–194
testing hypotheses about means, sknown, 183–185
hypothesis tests applied to means, two independent samples
confidence limits on μ 1 – μ 2 , 209
degrees of freedom, 207
distribution of differences between means, 203–205
effect size, 209–210
homophobia and sexual arousal, 207–208
pooling variances, 206–207
reporting results, 210–211
tstatistic, 205–206
hypothesis tests applied to means, two matched samples
using computer software for t tests on matched
samples, 202
confidence limits on d, 201
confidence limits on matched samples, 199
degrees of freedom, 198
d-family of measures, 200–201
difference scores, 197
effect size, 200
matched samples, 201–202
the tstatistic, 197
writing up the results of a dependent t, 203importance, 528
of individual variables, 551–552
measure of, 551
imputing, 549t¿