Index 763measures of location, 30
measures of variability
average deviation, 40
coefficient of variation, 44–45
degrees of freedom, 47–48
efficiency, 46
influence of extreme values on the variance and standard
deviation, 43–44
interquartile range and other range statistics, 38–39
mean absolute deviation, 40
range, 38
resistance, 46–47
sample variance as an estimator of the
population variance, 47
standard deviation, 41–42
sufficiency, 45
unbiasedness, 46
variance, 40
measures related to z, 79
median, 32, 34
median, advantages and disadvantages of, 33
mediating and moderating relationships
mediation, 555–559
moderating relationships, 559–563
mediating relationship, 555
mediation, 555–559
mesokurtic distribution, 29
Method I, 595
Method II, 594
Method III, 594
midpoints, 20
Minitab (commercial software), 9, 517
missing observations, 552–553
mixed models, 431
modaliity, 27
mode, 32, 34
mode, advantages and disadvantages of, 33
modeling discussion, 580
model selection, 547
model specification, 637–638
moderating relationships, 557–561
more than two categories example, 144
most significant digits, 24
multicollinearity, 527, 551, 558
multinomial distribution, 133–134
multiple comparisons, 428–429
multiple comparisons among treatment means, 363–408
comparison of the alternative procedures, 397–398
computer solutions, 399–402
confidence intervals and effect sizes for
contrasts, 384–387
error rates, 364–367
multiple comparisons in a simple experiment on morphine
tolerance, 367–369
post hoc comparisons, 389–397
a priori comparisons, 369–384
reporting results, 387–389
test selection, 398
trend analysis, 402–408
multiple correlation coefficient, 532–534
sample sizes, 533–534
testing the significance of r^2 , 533
multiple covariates, use of, 621
multiple linear regression, 516–527
looking at one predictor while controlling
for another, 521
multiple regression, another interpretation of, 524–525
multiple regression, final way to think of, 525–526
multiple regression equation, 521–524
regression equation, 516–519
two variable relationships, 520–521
multiple regression, 515–571
additional predictors, 527–529
versus analysis of variance, 580
another interpretation, 524–525
approximate regression coefficients, 552–553
constructing a regression equation, 546–550
distribution assumptions, 531
final way to think, 525–526
geometric representation of multiple
regression, 534–535
“importance” of individual variables, 551–552
logistic regression, 561–570
mediating and moderating relationships, 553–561
multiple correlation coefficient, 532–534
multiple linear regression, 516–527
partial and semipartial correlation, 535–538
regression diagnostics, 539–546
residual variance, 530–531
standard errors and tests of regression
coefficients, 529–530
suppressor variables, 538–539
multiple regression equation, 521–524
multiplettests, 369
multiplicative independence model, 637
multiplicative law of probability, 115
multistage Bonferroni procedures, 379
multi-tailed test, 155
multivariate normal, 531
multivariate outliers, 540
mutual dependence model, 635–636
mutually exclusive events, 114natural logarithm, 566
negatively skewed, 27
nested designs, 431, 435
nested models, 544
Newman-Keuls test, 393
nominal scales, 6
noncentral Fdistribution, 348