April 2012
Beginner
672 pages
17h 39m
English
book
Introduction to Linear Regression Analysis, 5th Edition
by Douglas C. Montgomery, Elizabeth A. Peck, G. Geoffrey Vining
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INDEX
Activation function in a neural network, 527
Akaike information criterion (AIC), 336
All possible regressions, 336, 342
Allocated codes and indicator variables, 273
Analysis of covariance, 272
Analysis of variance (ANOVA) in regression, 25, 84
Analysis of variance identity for regression, 26
Analysis of variance model as a regression model, 275
Artificial neural networks, 526
Assumptions in regression, 129
Asymptotic covariance matrix, 406
Asymptotic efficiency, 511
Asymptotic inference in nonlinear regression, 409, 411
Asymptotically unbiased estimators, 52
Autocorrelation function, 475
Autocorrelation in regression data, 474
Average prediction variance, 531
Backpropagation, 528
Backward elimination, 346
Bayesian estimation, 312
Bernoulli random variable, 432
Best linear unbiased estimators, 19, 587
Biasing parameter in ridge regression, 306
BIC, 336
Binary response variable, 422. See also Logistic regression
Binomial distribution, 422, 608
Bivariate normal distribution, 53
Bonferroni method for confidence intervals, 102
Bootstrap confidence intervals, 519
cases, 518
residuals, 518
Box–Behnken design, 532
Box–Cox method, 182
Breakdown point, 510
Candidate regressors, 328
Canonical link, 451
Categorical variables in regression, 260
Central composite design, 243, 532
Central limit theorem, 575
Chi-square distribution, definition, 575
Classical calibration estimator, 514
Classification and regression ...
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