Page 408 - Fundamentals of Probability and Statistics for Engineers

Subject Index 391

Pascal dist ribution, see N egative binomia l Sample moment, 263–264
distribution Sample point, 12
Poisson distribution, 173–176, 184 Sample space, 12
mean, 176, 184 Sample variance, 262–263
table, 367 mean, 262
variance, 176, 184 variance, 262
Population, 259 Schwarz inequality, 92
Probability, 13 Set, 8–12
assignment, 16, 17 complement of, 9
conditional, 20–21 countable (enumerable), 8
function, 13 disjoint, 10
measure, 13 element, 8
Probability density function (pdf), 44–46 empty, 9
conditional, 62–63 finite, 8
joint (jpdf), 49–51 infinite, 8
marginal, 57 subset of, 8
Probabilitydistribution function (PDF),39–41 uncountable (nonenumerable), 8
bivariate, 49 Set operation, 9–12
conditional, 61 difference, 10
joint (JPDF), 49–51 intersection (product), 10
marginal, 50 union (sum), 9
mixed-type, 46 Significance level, 319
Probability mass function (pmf), 41, 43 Spreadsheet, 3
conditional, 61 Standard deviation, 79–81
joint (jpmf), 51–55 Statistic, 260
marginal, 52 sufficient, 275
St atistica l independence, see
Random experiment, 12 Independence
R a ndom sa mple, see Sa mple Sterling’s formula, 107
Random variable, 37–39 Student’s t-distribution, 298–299

continuous, 38 table, 370
discrete, 38 Sum of random variables, 93,
function of, 120 145–146
sum of, 145
Random vector characteristic function, 104–105
moment, 94
Random walk, 52 probability distribution, 106, 146
Range space, 120
Regression coefficient, 336 Test of hypothesis, 316
confidence interval, 347 Total probability theorem, 23
least-square estimate, 344 Tree diagram, 27–28
test of hypothesis, 316
Relative likelihood, 16–17 Unbiasedness, 265
Reliability, 60, 218 Uniform distribution, 57, 189, 236
Residual, 337 bivariate, 193
Return period, 169 mean, 192, 236
variance, 192, 236
Sample, 259 Unimodal distribution, 79
size, 260
value, 260 Variance, 79, 82
Sample mean, 97, 261 Venn diagram, 9
mean, 261
variance, 261 Weibull distribution, 235
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