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Afifi A.A., Clark V. — Computer-Aided Multivariate Analysis
Îáñóäèòå êíèãó íà íàó÷íîì ôîðóìå Íàøëè îïå÷àòêó?
Íàçâàíèå: Computer-Aided Multivariate AnalysisÀâòîðû: Afifi A.A., Clark V. Àííîòàöèÿ: Increasingly, researchers need to perform multivariate statisticalanalyses on their data. Unfortunately, a lack of mathematical training prevents many from taking advantage of these advanced techniques, in part, because books focus on the theory and neglect to explain how to perform and interpret multivariate analyses on real-life data.
ßçûê: Ðóáðèêà: Computer science /Ñòàòóñ ïðåäìåòíîãî óêàçàòåëÿ: Ãîòîâ óêàçàòåëü ñ íîìåðàìè ñòðàíèö ed2k: ed2k stats Èçäàíèå: Third EditionÃîä èçäàíèÿ: 1996Êîëè÷åñòâî ñòðàíèö: 455Äîáàâëåíà â êàòàëîã: 09.12.2009Îïåðàöèè: Ïîëîæèòü íà ïîëêó |
Ñêîïèðîâàòü ññûëêó äëÿ ôîðóìà | Ñêîïèðîâàòü ID
Ïðåäìåòíûé óêàçàòåëü
172 184 Accelerated failure time model 317 Adjusted multiple correlation 171 177 181—183 Agglomerative clustering 391—395 AIC 172—173 185 Antilogarithm 53 ASCII files 32 Augmented partial residual plots 190 Bartlett's chi-square test for canonical correlation 233 Bonferroni inequality 139 Calibration 113—114 Canonical correlation analysis 6—7 227—241 Canonical correlation analysis, Bartlett's chi-square test 233 Canonical correlation analysis, canonical, correlation 229 232—233 Canonical correlation analysis, canonical, discriminant function 269—272 Canonical correlation analysis, canonical, loadings 236 Canonical correlation analysis, canonical, structural coefficients 236 Canonical correlation analysis, canonical, variable loadings 236 Canonical correlation analysis, canonical, variable scores 234 Canonical correlation analysis, canonical, variables 230 232—233 Canonical correlation analysis, coefficients 230—233 Canonical correlation analysis, computer programs 237—240 Canonical correlation analysis, correlation matrix 229 Canonical correlation analysis, first canonical correlation 230—232 Canonical correlation analysis, interpretation 230—233 Canonical correlation analysis, plots 234—235 Canonical correlation analysis, redundancy analysis 237 Canonical correlation analysis, second canonical correlation 232—233 Canonical correlation analysis, standardized coefficients 232 Canonical correlation analysis, tests of hypotheses 233—234 Canonical correlation analysis, what to watch for 240—241 Canonical discriminant function 269—272 Canonical variables 230 269—270 categorical variables 16 CESD 4 228 341 Classification of variables 13—16 Cluster analysis 9 381—409 Cluster analysis, agglomerative clustering 391—395 Cluster analysis, centroid 392—393 Cluster analysis, city-block distance 390 Cluster analysis, computer programs 404—406 Cluster analysis, dendrogram 394 399—400 Cluster analysis, distance matrix 389 Cluster analysis, distance measures 389—391 Cluster analysis, Euclidian distance 390—391 Cluster analysis, F test 398 402—403 Cluster analysis, graphical techniques 385—389 Cluster analysis, hierarchical clustering 391—395 398—400 Cluster analysis, icicles 394 Cluster analysis, K-means clustering 395—8 400—403 Cluster analysis, linkage methods 394 Cluster analysis, Mahalanobis distance 390 Cluster analysis, number of clusters 395 397—398 Cluster analysis, outliers 389 Cluster analysis, profile diagram 385—388 Cluster analysis, profile plot of means 402—403 Cluster analysis, scatter diagrams 383 385 Cluster analysis, seeds 397 Cluster analysis, similarity 389 Cluster analysis, standardized distance 390 Cluster analysis, standardized variables 390 Cluster analysis, taxonomic classification 381—382 Cluster analysis, tree graph 394 399—400 Cluster analysis, what to watch for 406 Code book construction 40—42 Coefficient of determination 138 Coefficient of variation 73 75 Combining data sets 35—36 Conditional distribution 134 confidence intervals 92—93 129 287 289 Continuous variables 16 Cook's distance 107 145 Correlation 94 96—99 132—137 140—143 169 181—183 229—233 256 338 356 Correlation, adjusted multiple 171 177 181—183 Correlation, matrix 132—133 140—141 169 229 338 356 Correlation, multiple 134—135 142—143 229 256 correlation, partial 135 143 Correlation, simple 94 96—99 Costs of misclassification 261—262 Covariance matrix 132—133 Cox versus log-linear regression 320—322 Cox versus logistic regression 322—324 Cox's proportional hazards model 319—320 Cumulative distribution 58 312—314 Data entry 28—32 Data management capabilities 33—36 Data screening 36—39 54—59 62—64 64—66 102—109 Data screening, independence 64—66 108 Data screening, normality 54—59 62—64 Data screening, outliers 104—108 347 Data transfer 32 dependent variables 17 85—86 125 Depression code book 41—42 Depression study 3—4 40—42 228—229 245 341—344 372—374 Depression study, CESD 3—4 228 341 Depression study, code book 42 Depression study, data 43 Depression study, definition of depression 245 Discrete variables 16 Discriminant analysis 7 243—279 Discriminant analysis, 253—254 263—264 Discriminant analysis, canonical variables 269—272 Discriminant analysis, classification 243—252 Discriminant analysis, classification function 257 Discriminant analysis, coefficients 257 Discriminant analysis, computer programs 272—275 Discriminant analysis, cost of misclassification 261—262 Discriminant analysis, cross-validation 263 Discriminant analysis, description 244 Discriminant analysis, dividing point 248 259—262 Discriminant analysis, dummy variable 256 Discriminant analysis, Fisher discriminant function 250—253 Discriminant analysis, Hotelling 265 Discriminant analysis, jackknife 263 Discriminant analysis, Mahalanobis distance 253—254 256 263—264 272 Discriminant analysis, minimizing misclassification 260 Discriminant analysis, more than two groups 267—269 Discriminant analysis, pooled variance of discriminant function 253 Discriminant analysis, posterior probabilities 258—259 Discriminant analysis, prediction 244 Discriminant analysis, prediction by guessing 264 Discriminant analysis, prior probabilities 259—261 Discriminant analysis, quadratic discriminant analysis 274 Discriminant analysis, regression analogy 255—256 Discriminant analysis, renaming groups 257 Discriminant analysis, standardized coefficients 258 Discriminant analysis, tests of hypotheses 265—266 Discriminant analysis, variable selection 266—267 Discriminant analysis, what to watch for 275—276 Discriminant analysis, Wilks'lambda 272 Dummy variables 202—209 256 269 286 Durbin — Watson statistic 108 e as base 52 Eigenvalues 335—336 341—343 359 365 Ellipse of concentration 96—98 234 249—250 Event history analysis 307 Exponential function 53 Factor analysis 8 354—379 Factor analysis, based on correlation 356 362 Factor analysis, common factors 357 Factor analysis, communality 357—358 360 362—363 Factor analysis, computer programs 374—376 Factor analysis, direct quartimin rotation 369—370 Factor analysis, eigenvalues 359 365—376 Factor analysis, factor diagram 361 Factor analysis, factor loadings 357—358 360 363 Factor analysis, factor model 356—357 Factor analysis, factor rotation 365—370 Factor analysis, factor score coefficients 371—372 Factor analysis, factor scores 371—372 Factor analysis, factor structure matrix 359 Factor analysis, initial factor extraction 359—365 Factor analysis, iterated principal components 362—363 Factor analysis, Kaiser normalization 366 Factor analysis, latent factors 378 Factor analysis, loading of 357—358 360 Factor analysis, Mahalanobis distance 374 Factor analysis, maximum likelihood 365 Factor analysis, nonorthogonal rotations 368—371 Factor analysis, number of factors 364—365 374 Factor analysis, oblique rotation 368—371 374 Factor analysis, orthogonal rotation 366—368 Factor analysis, outliers 376 Factor analysis, pattern matrix 359 Factor analysis, principal axis factoring 362 Factor analysis, principal components 358—362 Factor analysis, principal factor analysis 362—365 Factor analysis, regression procedure 372 Factor analysis, rotated factors 365—371 Factor analysis, scree method 365 Factor analysis, specificity 357 360 Factor analysis, standardized x 356 372 Factor analysis, storing factor scores 372 Factor analysis, unique factors 357 Factor analysis, varimax rotation 366—368 Factor analysis, what to watch for 376—377 Failure time analysis 307 Fisher discriminant function 250—253 Forced expiratory volume 1 sec (FEV1) 8 64 86 125 Forced vital capacity (FVC) 8 64 Forecasting 114 General F test 153—154 173—174 Geometric mean 75 Harmonic mean 75 Hierarchical clustering 391—395 398—400 Homoscedasticity 88 Hotelling 265 Independence, assessing 64—67 108 145 415—416 421 427—428 431—432 Independent variables 17 85 125 Indicator variables 203 Influence of observation 107 Interaction 146—148 207—208 287 Interquartile range 74 Interval variables 15 74 Jackknife procedure 263 Join 35—36 JOIN MATCH 35 K-means clustering 395—398 400—403 Kolmogorov — Smirnov D test 63 Least squares method 89—91 129 Leverage 105—106 144 Likelihood ratio chi-square 418 421 434 Log-linear analysis 9 410—442 Log-linear analysis, both explanatory and response variables 432 Log-linear analysis, comparison with logistic 437 Log-linear analysis, computer programs 437—439 Log-linear analysis, conditional independence model 423 Log-linear analysis, degrees of freedom 419 440 Log-linear analysis, exploratory model construction 425—430 Log-linear analysis, fit of model 430—431 Log-linear analysis, hierarchical models 411 418 424 Log-linear analysis, homogenous association model 423—424 Log-linear analysis, likelihood ratio chi-square 418 421 434 Log-linear analysis, logit model 435—437 Log-linear analysis, marginal association test 428 Log-linear analysis, multiway frequency tables 411—414 421—437 Log-linear analysis, mutual independence model 422 Log-linear analysis, notation 415 Log-linear analysis, odds ratio 421 Log-linear analysis, one variable jointly independent model 423 Log-linear analysis, partial association test 428 Log-linear analysis, Pearson chi-square 417—418 421 433 Log-linear analysis, sample size 432—434 Log-linear analysis, sampling 415 431—432 Log-linear analysis, saturated model 417 424 Log-linear analysis, standardized deviates 431 Log-linear analysis, stepwise selection 429—430 Log-linear analysis, structural zeros 433 Log-linear analysis, tests of hypotheses 415 416 421 427—428 431—432 Log-linear analysis, what to watch for 439—440 Log-linear regression model 317—319 Logarithmic transformation 48—53 111 Logistic regression 8 281—305 Logistic regression, adjust constant 297 Logistic regression, applications 296—299 Logistic regression, assumption 284—285 Logistic regression, case-control sample 297—299 Logistic regression, categorical variables 285—287 Logistic regression, coefficients 284—289 Logistic regression, computer programs 299—301 Logistic regression, confidence intervals categorical data 287 Logistic regression, confidence intervals continuous data 289 Logistic regression, continuous variables 288—289 Logistic regression, cross-sectional sample 297 Logistic regression, cutoff point 293—294 Logistic regression, dummy variables 285—287 Logistic regression, goodness of fit chi-square 292—293 Logistic regression, improvement chi-square 291 Logistic regression, interaction 287 293 Logistic regression, logarithm odds 284 Logistic regression, logistic function 283—284 Logistic regression, logit 284 Logistic regression, matched samples 297—299 Logistic regression, maximum likelihood 285 Logistic regression, model fit 291—293 Logistic regression, odds 284 Logistic regression, odds ratio 286—287 Logistic regression, prior probabilities 288 Logistic regression, probability population 285 288 Logistic regression, ROC curves 295—296 Logistic regression, sensitivity 295 Logistic regression, specificity 295 Logistic regression, standard error 287—289 Logistic regression, step wise variable selection 290—291 Logistic regression, versus Cox's regression model 322—324 Logistic regression, what to watch for 301—302 Logit model 435—437 Lung cancer code book 310 Lung cancer survival data 448 Lung function code book 444 Lung function data 446 Lung function definition 8 64 Mahalanobis distance 253—254 256 261—264 390 Median 74 MERGE 35—36 Missing at random 198 Missing completely at random 198 Missing values 36—38 197—202 Missing Values, imputation 199 Missing Values, maximum likelihood substitution 200 Missing Values, mean substitution 199 MODE 73 Multicollinearity 149 212—219 331 345—347 Multiple regression 6 124—224 Multiple regression, 172 184 Multiple regression, additive model 137 146 Multiple regression, adjusted multiple correlation 171 177 181—183 Multiple regression, AIC 172—173 185 Multiple regression, analysis of variance 137—138 Multiple regression, augmented partial residual plots 190 Multiple regression, backward elimination 178—179 Multiple regression, Bonferroni inequality 139—140 Multiple regression, coefficient of determination 139 Multiple regression, comparing regression planes 150—153 Multiple regression, computer programs 154—157 185—187 Multiple regression, conditional distribution 134 Multiple regression, confidence intervals 130 Multiple regression, Cook's distance 145 Multiple regression, correlation matrix 132—133 140—141 Multiple regression, covariance matrix 132—133
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