Index
A
Analysis of variance (ANOVA)
applications, 439–440
multiple comparison constants,
616
one-way, 440
between samples sum of squares,
445–446, 453
multiple comparisons of sample means,
450–452
null hypothesis, 442, 445–446
sum of squares identity, 447–450
unequal sample sizes, 452–453
within samples sum of squares, 443–445,
452–453
overview, 440–442
two-way, 440, 454–457
column sum of squares, 461
error sum of squares, 459, 465
grand mean, 456, 463
hypothesis testing, 458–462
null hypothesis, 464, 467–468
parameter estimation, 458–459
row and column interaction,
463–468
row sum of squares, 460–461
Approximately normal data set, 31
Assignable cause, 545
Attribute, quality control, 545
Axioms of probability, 59–61
B
Bar graph, 10
Basic principle of counting
generalized, 63
proof, 62–63
Bayes, T., 75
Bayes estimator, 272–274
Bernoulli random variables, 273–274
normal mean, 274–275
life testing, 596–598
Bayes formula, 70–76
Behrens-Fisher problem, 319
Bernoulli, J., 5, 141
Bernoulli random variable, 141, 157–158
approximate confidence interval for mean of
distribution, 260–264
Bayes estimator, 273–274
testing equality of parameters in two Bernoulli
populations, 327–329
Beta distribution, 274
Between samples sum of squares, 445–446, 453
Bias of an estimator, 267, 272
Bimodal data set, 33–34
Binomial distribution function, 147–148
Binomial random variable
definition, 141
hypergeometric random variable relationship,
159–160, 219–220
probability calculations, 142–146
probability mass function, 142617