Modeling and Multivariate Methods - SAS

More documents

Recommendations

Info

522 Fitting Partial Least Squares Models Chapter 21 Statistical Details T 2 Plot The T 2 value for the i th observation is computed as follows: p n T2 i ( n – 1) t2 ij ⁄ t2 = kj j = 1 k = 1 where t ij = X score for the i th row and j th extracted factor, p = number of extracted factors, and n = number of observations used to train the model. If validation is not used, n = total number of observations. The control limit for the T 2 Plot is computed as follows: ((n-1) 2 /n)*BetaQuantile(0.95, p/2, (n-p-1)/2) where p = number of extracted factors, and n = number of observations used to train the model. If validation is not used, n = total number of observations. van der Voet T 2 The van der Voet T 2 test helps determine whether a model with a specified number of extracted factors differs significantly from a proposed optimum model. The test is a randomization test based on the null hypothesis that the squared residuals for both models have the same distribution. Intuitively, one can think of the null hypothesis as stating that both models have the same predictive ability. The test statistic is C i = ( R2 ijk , – R2 opt, jk ) jk where R i, jk is the jth predicted residual for response k for the model with i extracted factors, and R opt, jk is the corresponding quantity for the model based on the proposed optimum number of factors, opt. The significance level is obtained by comparing C i with the distribution of values that results from randomly exchanging R2 i, jk and R2 opt, jk . A Monte Carlo sample of such values is simulated and the significance level is approximated as the proportion of simulated critical values that are greater than C i . Confidence Ellipse for Scatter Scores Plots The Scatter Scores Plots option produces scatterplots with a confidence ellipse. The coordinates of the top, bottom, left, and right extremes of the ellipse are as follows: For a scatterplot of score i versus score j: • the top and bottom extremes are +/-sqrt(var(score i)*z) • the left and right extremes are +/-sqrt(var(score j)*z) where z = ((n-1)*(n-1)/n)*BetaQuantile(0.95, 1, (n-3)/2).
Chapter 22 Scoring Tests Using Item Response Theory Using the Item Analysis Platform Item Response Theory (IRT) is a method of scoring tests. Although classical test theory methods have been widely used for a century, IRT provides a better and more scientifically based scoring procedure. Its advantages include: • scoring tests at the item level, giving insight into the contributions of each item on the total test score • producing scores of both the test takers and the test items on the same scale • fitting nonlinear logistic curves, more representative of actual test performance than classical linear statistics.
Page 1 and 2:
Version 10 Modeling and Multivariat
Page 3:
INDIRECT OR CONSEQUENTIAL DAMAGES O
Page 6 and 7:
6 Emphasis Options for Standard Lea
Page 8 and 9:
8 Models with Crossed, Interaction,
Page 10 and 11:
10 8 Analyzing Screening Designs Us
Page 12 and 13:
12 Examining the Residuals . . . .
Page 14 and 15:
14 Response Frequencies . . . . . .
Page 16 and 17:
16 19 Analyzing Principal Component
Page 18 and 19:
18 Interpreting the Profiles . . .
Page 20 and 21:
20 Discriminant Analysis . . . . .
Page 22 and 23:
Contents Book Conventions. . . . .
Page 24 and 25:
24 Learn about JMP Chapter 1 Book C
Page 26 and 27:
26 Learn about JMP Chapter 1 Book C
Page 28 and 29:
28 Learn about JMP Chapter 1 Additi
Page 30 and 31:
30 Learn about JMP Chapter 1 Additi
Page 32 and 33:
Contents Launch the Fit Model Platf
Page 34 and 35:
34 Introduction to the Fit Model Pl
Page 36 and 37:
Page 38 and 39:
Page 40 and 41:
Page 42 and 43:
Page 44 and 45:
Page 46 and 47:
Page 48 and 49:
Page 50 and 51:
Page 52 and 53:
Page 54 and 55:
Contents Example Using Standard Lea
Page 56 and 57:
56 Fitting Standard Least Squares M
Page 58 and 59:
Page 60 and 61:
Page 62 and 63:
Page 64 and 65:
Page 66 and 67:
Page 68 and 69:
Page 70 and 71:
Page 72 and 73:
Page 74 and 75:
Page 76 and 77:
Page 78 and 79:
Page 80 and 81:
Page 82 and 83:
Page 84 and 85:
Page 86 and 87:
Page 88 and 89:
Page 90 and 91:
Page 92 and 93:
Page 94 and 95:
Page 96 and 97:
Page 98 and 99:
Page 100 and 101:
100 Fitting Standard Least Squares
Page 102 and 103:
Page 104 and 105:
Page 106 and 107:
Page 108 and 109:
Page 110 and 111:
Page 112 and 113:
Page 114 and 115:
Page 116 and 117:
Page 118 and 119:
Page 120 and 121:
Page 122 and 123:
Page 124 and 125:
Page 126 and 127:
Page 128 and 129:
Page 130 and 131:
Page 132 and 133:
Page 134 and 135:
Contents Overview of Stepwise Regre
Page 136 and 137:
136 Fitting Stepwise Regression Mod
Page 138 and 139:
Page 140 and 141:
Page 142 and 143:
Page 144 and 145:
Page 146 and 147:
Page 148 and 149:
Page 150 and 151:
Page 152 and 153:
Page 154 and 155:
Page 156 and 157:
Contents Example of a Multiple Resp
Page 158 and 159:
158 Fitting Multiple Response Model
Page 160 and 161:
Page 162 and 163:
Page 164 and 165:
Page 166 and 167:
Page 168 and 169:
Page 170 and 171:
Page 172 and 173:
Page 174 and 175:
Page 176 and 177:
Page 178 and 179:
Contents Overview of Generalized Li
Page 180 and 181:
180 Fitting Generalized Linear Mode
Page 182 and 183:
Page 184 and 185:
Page 186 and 187:
Page 188 and 189:
Page 190 and 191:
Page 192 and 193:
Page 194 and 195:
Page 196 and 197:
Page 198 and 199:
Contents Introduction to Logistic M
Page 200 and 201:
200 Performing Logistic Regression
Page 202 and 203:
Page 204 and 205:
Page 206 and 207:
Page 208 and 209:
Page 210 and 211:
Page 212 and 213:
Page 214 and 215:
Page 216 and 217:
Page 218 and 219:
Page 220 and 221:
Page 222 and 223:
Page 224 and 225:
Page 226 and 227:
Page 228 and 229:
Page 230 and 231:
Contents Overview of the Screening
Page 232 and 233:
232 Analyzing Screening Designs Cha
Page 234 and 235:
Page 236 and 237:
Page 238 and 239:
Page 240 and 241:
Page 242 and 243:
Contents Introduction to the Nonlin
Page 244 and 245:
244 Performing Nonlinear Regression
Page 246 and 247:
Page 248 and 249:
Page 250 and 251:
Page 252 and 253:
Page 254 and 255:
Page 256 and 257:
Page 258 and 259:
Page 260 and 261:
Page 262 and 263:
Page 264 and 265:
Page 266 and 267:
Page 268 and 269:
Page 270 and 271:
Page 272 and 273:
Page 274 and 275:
Page 276 and 277:
Page 278 and 279:
Contents Overview of Neural Network
Page 280 and 281:
280 Creating Neural Networks Chapte
Page 282 and 283:
Page 284 and 285:
Page 286 and 287:
Page 288 and 289:
Page 290 and 291:
Page 292 and 293:
Page 294 and 295:
Contents Launching the Platform. .
Page 296 and 297:
296 Modeling Relationships With Gau
Page 298 and 299:
Page 300 and 301:
Page 302 and 303:
Page 304 and 305:
Contents Overview of the Loglinear
Page 306 and 307:
306 Fitting Dispersion Effects with
Page 308 and 309:
Page 310 and 311:
Page 312 and 313:
Page 314 and 315:
Page 316 and 317:
Contents Introduction to Partitioni
Page 318 and 319:
318 Recursively Partitioning Data C
Page 320 and 321:
Page 322 and 323:
Page 324 and 325:
Page 326 and 327:
Page 328 and 329:
Page 330 and 331:
Page 332 and 333:
Page 334 and 335:
Page 336 and 337:
Page 338 and 339:
Page 340 and 341:
Page 342 and 343:
Page 344 and 345:
Page 346 and 347:
Page 348 and 349:
Page 350 and 351:
Page 352 and 353:
Contents Launch the Platform . . .
Page 354 and 355:
354 Performing Time Series Analysis
Page 356 and 357:
Page 358 and 359:
Page 360 and 361:
Page 362 and 363:
Page 364 and 365:
Page 366 and 367:
Page 368 and 369:
Page 370 and 371:
Page 372 and 373:
Page 374 and 375:
Page 376 and 377:
Page 378 and 379:
Page 380 and 381:
Page 382 and 383:
Page 384 and 385:
Contents The Categorical Platform .
Page 386 and 387:
386 Performing Categorical Response
Page 388 and 389:
Page 390 and 391:
Page 392 and 393:
Page 394 and 395:
Page 396 and 397:
Page 398 and 399:
Page 400 and 401:
Page 402 and 403:
Contents Introduction to Choice Mod
Page 404 and 405:
404 Performing Choice Modeling Chap
Page 406 and 407:
Page 408 and 409:
Page 410 and 411:
Page 412 and 413:
Page 414 and 415:
Page 416 and 417:
Page 418 and 419:
Page 420 and 421:
Page 422 and 423:
Page 424 and 425:
Page 426 and 427:
Page 428 and 429:
Page 430 and 431:
Page 432 and 433:
Page 434 and 435:
Page 436 and 437:
Page 438 and 439:
Page 440 and 441:
Page 442 and 443:
Contents Launch the Multivariate Pl
Page 444 and 445:
444 Correlations and Multivariate T
Page 446 and 447:
Page 448 and 449:
Page 450 and 451:
Page 452 and 453:
Page 454 and 455:
Page 456 and 457:
Page 458 and 459:
Page 460 and 461:
Contents Introduction to Clustering
Page 462 and 463:
462 Clustering Data Chapter 18 The
Page 464 and 465:
464 Clustering Data Chapter 18 Hier
Page 466 and 467:
Page 468 and 469:
Page 470 and 471:
470 Clustering Data Chapter 18 K-Me
Page 472 and 473: 472 Clustering Data Chapter 18 K-Me
Page 474 and 475: 474 Clustering Data Chapter 18 Norm
Page 476 and 477: 476 Clustering Data Chapter 18 Norm
Page 478 and 479: 478 Clustering Data Chapter 18 Self
Page 480 and 481: 480 Clustering Data Chapter 18 Self
Page 482 and 483: Contents Principal Components. . .
Page 484 and 485: 484 Analyzing Principal Components
Page 492 and 493: Contents Introduction . . . . . . .
Page 494 and 495: 494 Performing Discriminant Analysi
Page 506 and 507: Contents Overview of the Partial Le
Page 508 and 509: 508 Fitting Partial Least Squares M
Page 524 and 525: Contents Introduction to Item Respo
Page 526 and 527: 526 Scoring Tests Using Item Respon
Page 536 and 537: Contents Surface Plots . . . . . .
Page 538 and 539: 538 Plotting Surfaces Chapter 23 La
Page 544 and 545: 544 Plotting Surfaces Chapter 23 Th
Page 546 and 547: 546 Plotting Surfaces Chapter 23 Th
Page 548 and 549: 548 Plotting Surfaces Chapter 23 Pl
Page 550 and 551: 550 Plotting Surfaces Chapter 23 Pl
Page 552 and 553: 552 Plotting Surfaces Chapter 23 Ke
Page 554 and 555: Contents Introduction to Profiling
Page 556 and 557: 556 Visualizing, Optimizing, and Si
Page 572 and 573:
572 Visualizing, Optimizing, and Si
Page 574 and 575:
Page 576 and 577:
Page 578 and 579:
Page 580 and 581:
Page 582 and 583:
Page 584 and 585:
Page 586 and 587:
Page 588 and 589:
Page 590 and 591:
Page 592 and 593:
Page 594 and 595:
Page 596 and 597:
Page 598 and 599:
Page 600 and 601:
Page 602 and 603:
Page 604 and 605:
Page 606 and 607:
Page 608 and 609:
Page 610 and 611:
Page 612 and 613:
Page 614 and 615:
Page 616 and 617:
Page 618 and 619:
Page 620 and 621:
Page 622 and 623:
Page 624 and 625:
Page 626 and 627:
Page 628 and 629:
Contents Example of Model Compariso
Page 630 and 631:
630 Comparing Model Performance Cha
Page 632 and 633:
Page 634 and 635:
Page 636 and 637:
Page 638 and 639:
Page 640 and 641:
640 References Becker, R.A. and Cle
Page 642 and 643:
642 References Draper, N. and Smith
Page 644 and 645:
644 References Hooper, J. H. and Am
Page 646 and 647:
646 References Matsumoto, M. and Ni
Page 648 and 649:
648 References Rawlings, J.O. (1988
Page 650 and 651:
650 References Umetrics (1995), Mul
Page 652 and 653:
Contents The Response Models . . .
Page 654 and 655:
654 Statistical Details Appendix A
Page 656 and 657:
Page 658 and 659:
Page 660 and 661:
Page 662 and 663:
Page 664 and 665:
Page 666 and 667:
Page 668 and 669:
Page 670 and 671:
Page 672 and 673:
Page 674 and 675:
Page 676 and 677:
Page 678 and 679:
Page 680 and 681:
Page 682 and 683:
Page 684 and 685:
Page 686 and 687:
Page 688 and 689:
Page 690 and 691:
690 Index Birth Death Subset.jmp 46
Page 692 and 693:
692 Index Ellipses Transparency 451
Page 694 and 695:
694 Index test for curvature 46 Kru
Page 696 and 697:
696 Index Customizing 267 Nonlinear
Page 698 and 699:
698 Index reliability analysis 453-
Page 700 and 701:
700 Index Stable 363 Stable Inverti
Page 702:
702 Index
show all

Modeling and Multivariate Methods - SAS

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?