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Harris R.J. — A primer of multivariate statistic
Harris R.J. — A primer of multivariate statistic



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Íàçâàíèå: A primer of multivariate statistic

Àâòîð: Harris R.J.

Àííîòàöèÿ:

As he was looking over materials for his multivariate course, Harris (U. of New Mexico) realized that the course had outstripped the current edition of his own textbook. He decided to revise it rather than use someone else's because he finds them veering too much toward math avoidance, and not paying enough attention to emergent variables or to structural equation modeling. He has updated the 1997 second edition with new coverage of structural equation modeling and various aspects of it, new demonstrations of the properties of the various techniques, and computer applications integrated into each chapter rather than appended.


ßçûê: en

Ðóáðèêà: Ìàòåìàòèêà/Âåðîÿòíîñòü/Ñòàòèñòèêà è ïðèëîæåíèÿ/

Ñòàòóñ ïðåäìåòíîãî óêàçàòåëÿ: Ãîòîâ óêàçàòåëü ñ íîìåðàìè ñòðàíèö

ed2k: ed2k stats

Èçäàíèå: third edition

Ãîä èçäàíèÿ: 2001

Êîëè÷åñòâî ñòðàíèö: 609

Äîáàâëåíà â êàòàëîã: 03.06.2005

Îïåðàöèè: Ïîëîæèòü íà ïîëêó | Ñêîïèðîâàòü ññûëêó äëÿ ôîðóìà | Ñêîïèðîâàòü ID
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Ïðåäìåòíûé óêàçàòåëü
F ratio, Hotelling's $T^2$ and      155 188
F ratio, Manova and      218
F ratio, Maximized      56—57 218 243 345
F ratio, null hypothesis and      212
F ratio, stripped (of degrees of freedom)      219 240 242—243 267 455—456
F ratio, t ratio and      159 188
F statistics      See F tests
F tests      75 250 252
F tests, $T^2$ and      161 162 184 189 192 193 200 204 551
F tests, Bonferroni approach and      13—14
F tests, degrees of freedom for      212
F tests, post hoc contrasts and      213—215 215t
F tests, univariate      14
FA      See Factor analysis
Factor analysis (FA)      See also Interpretation; Specific techniques
Factor analysis (FA), approaches to      105 278 318 394—410
Factor analysis (FA), architecture and      See Houses dimensions
Factor analysis (FA), Canned programs      421—443
Factor analysis (FA), communality and      397—406
Factor analysis (FA), confirmatory      365 407 416 420 433—443
Factor analysis (FA), eigenvectors and      404—406 420 421
Factor analysis (FA), Factor Fable      373—378 373 374 375t 377
Factor analysis (FA), factor revelation      427 429 431
Factor analysis (FA), factor score coefficients      109 396 405 410—420
Factor analysis (FA), factor structure      See Factor structure
Factor analysis (FA), factorial invariance      364 417
Factor analysis (FA), higher order factors      442—443 473—480
Factor analysis (FA), in SEM      464
Factor analysis (FA), indeterminacy and      411 412 425 432
Factor analysis (FA), inter-battery      278
Factor analysis (FA), interpretation      404—405 409 410—416 419
Factor analysis (FA), Manova and      264
Factor analysis (FA), maximum likelihood      400 407—410 422 424
Factor analysis (FA), minres      407—408
Factor analysis (FA), multiple group      409
Factor analysis (FA), number of factors      400 407—408
Factor analysis (FA), observable variables and      360 411
Factor analysis (FA), principal component analysis and      35—38 235 238 416—424
Factor analysis (FA), principal factors      See Principal factor analysis
Factor analysis (FA), SAS PROC CALIS program      433—443
Factor analysis (FA), SAS PROC FACTOR program      325—326 338—340 408 421
Factor analysis (FA), SPSS FACTOR program      408 421—433
Factor analysis (FA), texts on      394
Factor analysis (FA), “confirmation” of incorrect interpretations      416
Factor pattern (matrix)      321 330 336 340—342 357 369 378 381
Factor structure      43 334—335 343 344t 355 358 355 369—372 375 381 394—443
Factor structure, number of factors      400 408 413 425
Factor structure, orthogonality of      396 409 413 413t
Factor structure, overfactoring      369
Factor structure, quartimax      366
Factor structure, rotation of      362 387 393 395 397 406 408 409 415
Factor structure, simple      361 413 416 422 443
Factor structure, standard-score pattern      372
Factor structure, vs. pattern in oblique solutions      409 415
Factorial design      11n 39 242
Factorial design, full-model vs. sequential approaches to      117—121
Faculty salaries, gender bias in      116—121 134—135
FEDS      See Fraction of expected difference significant criterion
Feldt, L.S.      190—191
Femininity scale      345
Ferguson, G.A.      362
Ferraro, D.      273
Fidell, L.S.      190 233 479
Fienberg, S.E.      480
Finite intersection test      21 238—240
Finite intersection test, Bonferroni-based      215
Finite intersection test, computer programs for      240
Finn, J.D.      170
Fit, indices of      437 466—469
Fixed model      76
Flatness      173—177 189 196 201 204—207 224 226 228 252
Flatness, of profile      173
Flatness, slope measures, and      175
Flatness, Student's t test and      196
Flatness, test for      180—181 228 255
Flint, R.      166 282 285
Floor effects      446
Football numbers, statistical analysis of      447—448
FORTRAN computer language      517
Four-interchange problems      156
Fraction of expected difference significant (FEDS) criterion for sample size      6—9
Frankel, M.R.      451
Functions, continuous      482
Functions, derivative of      482—483
Furby, L.      40
Gabriel, K.R.      233—234 244—245
Gaito, J.      448
Game(s), canonical analysis of      281—283
Game(s), experimental      281—283 283t 282—287 336 354
Game(s), game theory      166
Game(s), outcomes of      336
Game(s), payoffs in      335
Game(s), two-person      108
Games, P.      213 215 217 252
Garbage rate      2
GCR      See Greatest characteristic root
GCRCMP program      239 246 283 532
Gender bias example      116—121 134—135 468—470
Generating variables      316 328 330 332—333
Geometry, m-dimensional      61
Gestalt judgments      59
Glass, G.V.      59 445
Gleason, T.C.      293
Gollob, H.F.      244
Gonter, R.      236 278
Gonzales, R.      23
Goodman — Kruskal measures of correlation      30 446—447
Goodman — Kruskal measures of correlation, chi-square      448
Goodman — Kruskal measures of correlation, Goodness-of-fit tests      5—6 44 407
Goodman, L.A.      30 446
Gorsuch, R. L.      374 378 394—395 401—403 409 415 422 479
Graphical techniques      81
Greatest characteristic root (gcr)      25 26 37 231 234 236—240 269 450—452 458—459 464
Greatest characteristic root (gcr), conservative tests      237
Greatest characteristic root (gcr), effects on of nonlinearity      455
Greatest characteristic root (gcr), efficiency of tests      239
Greatest characteristic root (gcr), gcr criterion      25 26 37 220 232—240 246—250 266—267
Greatest characteristic root (gcr), GCRCMP program      239 246 283 532
Greatest characteristic root (gcr), gcrinter program      532
Greatest characteristic root (gcr), maximization procedure      220 243 248
Greatest characteristic root (gcr), multiple-root tests and      233—240
Greatest characteristic root (gcr), robustness of      232 237—238
Greatest characteristic root (gcr), stepdown procedure for testing individual roots      235—237 270 279
Greatest characteristic root (gcr), tables for      459 517 518t-529t
Green, B.F.      419
Green, S.B.      79
Greenhouse — Geisser correction      190
Greenwald, A.G.      4
Grice, G.R.      242
Grice, J.W.      412—414
Groups, canonical analysis of      313—314
Groups, means for      194
Groups, membership variables      17 32—34 36 36 38 39 74 111 313—314 318 544—557 559
Groups, sampling units and      156n
GRP, predictors of      184
Guthrie, D.      4
Guttman procedure      43
Guttman, L.      43 401 408 409 411 418 424
H matrix      219—220 231—232 240—243 245—247
H.o.t.d.v.      See Homogeneity of treatment-difference variances
Hadzi-Pavlovic, D.      233—234 237—238
Hakstian, A.R.      422
Hamagami, F.      480
Hancock, G.R.      217
Hardware      See Computers hardware
Harlow, L.      5
Harman, H.H.      44 360 380 394 398—399 403 406 419 479
Harris, C.W.      40 409
Harris, M.B.      28 59 65 344 345t
Harris, R.J.      4 28 33 113—114 155 155 157n 162 164—166 185 188 192 216—217 220 230 235—236 239 243—245 259—260 263 278 280 284 290 295 306 316 327 344 345t 362 379 380 412 416 450 480 532
Harwell, M.R.      252
Hawks, study of      201 205—209
Hays, W.L.      27 59
Heck, D.L.      25 220
HEINV      See EINVH program
Hendrickson, A.E.      422
Herzberg formula      75
Herzberg, P.A.      75 122
Heuristic formulae      14—45 16t 51
Heywood cases      399 402 408 480
Hierarchical linear modeling      480
Hierarchical structure      327 356
Hilf, F.D.      317
Hinman, S.      394
Hjelm, H.F.      450
Hodge, G.K.      86 96 184
Holland, P.      480
Holland, T.      277
Holzinger, K.J.      400
Homogeneity of treatment-difference variances (h.o.t.d.v.)      190
Homogeneity of treatment-difference variances (h.o.t.d.v.), compound symmetry and      190
Homogeneity of treatment-difference variances (h.o.t.d.v.), departure from      190
Homogeneity of treatment-difference variances (h.o.t.d.v.), test of      190
Homogeneity, and covariance      185—187 232 237 444 450
Homogeneity, of variance      232 237 444 450
Homogeneous equations      512—513
Horn, J.L.      413
Horst's criterion      284
Horst, P.      114 280—281 284 374 479
Hotelling's $T^2$      16t 16—19 155—209 316—317 557—559
Hotelling's $T^2$, associated discriminant function and      546—548
Hotelling's $T^2$, assumptions underlying      185—187
Hotelling's $T^2$, Box test and      187 190
Hotelling's $T^2$, discriminant analysis and      182—184
Hotelling's $T^2$, F tests and      161 162 184 189 192 193 200 204 551
Hotelling's $T^2$, in repeated-measures designs      188—192
Hotelling's $T^2$, limitations of      210
Hotelling's $T^2$, Manova and      220—221
Hotelling's $T^2$, mean vectors and      184
Hotelling's $T^2$, MRAand      184—185 549—551
Hotelling's $T^2$, on difference between groups      170—175 316—317
Hotelling's $T^2$, post hoc procedures      221
Hotelling's $T^2$, programs for      198—200
Hotelling's $T^2$, sampling distribution of      155
Hotelling's $T^2$, significance levels for      160—161 164—165 168 172 193 451
Hotelling's $T^2$, union-intersection principle and      156
Hotelling's $T^2$, via multiple regression      415—416
Hotelling's trace statistic      231—232 257 280
Hotelling, H.      156 280
Houses, dimensions of      373—378 374 375t 377
HSD test procedure      239
Huberty, C.J.      184 168
Huynh — Feldt test of h.o.t.d.v.      191
Huynh, C-L      122
Huynh, H.      190—191
Hyperactivity      86 96 182—184
Hyperplanes      61
Hypothesis testing      See Mathematics; Null Hypothesis significance testing; Path analysis; Structural equations modeling
Image analysis      43 409 410—412 422
Image analysis, indeterminacy and      410
Independence of sets of variables      281—284
Independence property      38
Independence property, irrelevant parameters and      72—74 542
Indeterminacy of factor scores      405 411—412 418 425—427 432
Indeterminacy of factor scores, degree of      419
Index, of summation      64
Inferential statistics      1 3 6 10—12
Intelligence, models of      425 429 436 442—443 477
Inter-battery FA      278
Interactions, and parallelism      225 227
Interactions, between continuous variables      32
Interactions, dichotomous vs. (1, 0, -1) coding of      32n
Interactions, higher-order Anova      243—245 254
Interactions, interpretation of      450
Interactions, optimal c.o.c      244—245
Intercept, and grand mean      111
Intercorrelation matrix, factor analysis and      406—407
Intercorrelation matrix, PCA and      325 339 344t 346 352 366 378—379 383
Interfactor correlation matrix      395 440
Interpretation of canonical variates      269 274—277 289—290
Interpretation of discriminant function      165 173 179 201 226 253 257 263 266
Interpretation of extra roots      269
Interpretation of factors      414—416
Interpretation of interactions      243—244
Interpretation of principal components      327 333 334 341—344 347 347t 348—351 354 360 368 372—378
Interpretation of regression variate      84—85 91 102
Interpretation of results      209
Interpretation, loadings-based vs. coefficients-based      289—293 346 373—378 404—405 409 410—416 419
Interpretation, m+1 orthogonal vs. m oblique factors      442—443
Interval-scale properties      444—446 448
Invariance      See Factorial invariance
Inversion, cofactor method      496 498
Inversion, of diagonal matrices      496
Inversion, of matrices      72 80—81 93 105 119 169 181 493—496
Inversion, via partitioning formulae      105
IQ test      58 67 130—134
Irrelevant parameters, independence of      72—74 542
Isaac, P.D.      277—278
Item analysis      106
Ito, K.      78 186 187 451
Jackknifing tests      3
Jackson, D.N.      420
Jahoda, M.      143
James, L.R.      136
Jennrich, R.I.      409
Johnson, D.W.      243—244
Joint contribution      108
Joliffe, I.T.      327 370
Joreskog, K.G.      365 395 408
Joyce, M.A.      155 157t 162 188
Kaiser invariance      See factorial invariance
Kaiser normalization      363 364 415—417 426
Kaiser, H.F.      4 356 363—365 374 387 408 414—416 422
Kendall's tau      30 446
Kendall, M.G.      3 30 235 446
Kenny, D.A.      136 400
Keren, G.      114
Keselman, H.J.      252
Kirk, R.E.      59 190 213 215 217 243 245
Klett, C.J.      235 479
Klockars, A.J.      217
Knapp, T.R.      277
Knoell, D.M.      409
Known-generator problem      332—333 356—357 365
Koppell, B.      317
Korin, B.P.      451
Krishnaiah, P.R.      239 452
Krus, D.J.      289
Kruskal, W.H.      30 446—447 451
kurtosis      362
Labeling problem      334
Lagrangian multipliers      219 485—486 539 541 552 554 560
Lagrangian multipliers, $\lambda, \Lambda$ (lambda, capital lambda)      See Eigenvalues; Lagrangian multipliers; U statistic
Lagrangian multipliers, eigenvalues and      506
Lancaster, H.O.      235
Lance, C.E.      136
Laplace expansion in computing eigenvalues      398 507
Latent growth-curve modeling      480
Latent variables      41—42 318 334 335 348 355—359 464
Latent variables, rotation of      357—359
Laughlin, J.E.      80
Lawley, D.N.      106 354 364
Learning, measure of      22
Learning, theories of      78
Least squares (LSQ) method      62—69 114 121—122 126 136
Least squares (LSQ) method, alternatives to      121—122
Least squares (LSQ) method, Anova and      111—121 545
Least squares (LSQ) method, BLUE property      122
Least squares (LSQ) method, estimates with      17 448
Least squares (LSQ) method, regression weights      79
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