Introduction to Linear Regression Analysis, 5th Edition

INDEX

Activation function in a neural network, 527

Adjusted R², 87, 333

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

Bootstrapping, 411, 517

cases, 518

residuals, 518

Box–Behnken design, 532

Box–Cox method, 182

Breakdown point, 510

Calibration problem, 513, 516

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 ...