Modeling and Multivariate Methods - SAS

More documents

Recommendations

Info

Contents Launch the Fit Model Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .33 Example of the Fit Model Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 Specify Different Model Types. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .37 Construct Model Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .39 Add . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .39 Cross . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 Nest. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 Macros . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .41 Attributes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .45 Transformations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 Fitting Personalities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 Emphasis Options for St<strong>and</strong>ard Least Squares . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 Model Specification Options. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 Validity Checks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .52
Chapter 2 Introduction to the Fit Model Platform 33 Launch the Fit Model Platform Launch the Fit Model Platform Launch the Fit Model platform by selecting Analyze > Fit Model. Figure 2.2 The Fit Model Launch Window Note: When you select Analyze > Fit Model in a data table that has a script named Model (or model), the launch window is filled in based on the script. Table 2.1 Description of the Fit Model Launch Window Model Specification Select Columns Y The red triangle menu next to Model Specification contains options such as the following: • Centering polynomials • Setting the alpha level • Saving the model specifications • Interacting with <strong>SAS</strong> See “Model Specification Options” on page 50. Lists the columns in the current data table. Identifies one or more response variables (the dependent variables) for the model.
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 34 and 35: 34 Introduction to the Fit Model Pl
Page 54 and 55: Contents Example Using Standard Lea
Page 56 and 57: 56 Fitting Standard Least Squares M
Page 82 and 83:
82 Fitting Standard Least Squares M
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 486 and 487:
Page 488 and 489:
Page 490 and 491:
Page 492 and 493:
Contents Introduction . . . . . . .
Page 494 and 495:
494 Performing Discriminant Analysi
Page 496 and 497:
Page 498 and 499:
Page 500 and 501:
Page 502 and 503:
Page 504 and 505:
Page 506 and 507:
Contents Overview of the Partial Le
Page 508 and 509:
508 Fitting Partial Least Squares M
Page 510 and 511:
Page 512 and 513:
Page 514 and 515:
Page 516 and 517:
Page 518 and 519:
Page 520 and 521:
Page 522 and 523:
Page 524 and 525:
Contents Introduction to Item Respo
Page 526 and 527:
526 Scoring Tests Using Item Respon
Page 528 and 529:
Page 530 and 531:
Page 532 and 533:
Page 534 and 535:
Page 536 and 537:
Contents Surface Plots . . . . . .
Page 538 and 539:
538 Plotting Surfaces Chapter 23 La
Page 540 and 541:
Page 542 and 543:
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 558 and 559:
Page 560 and 561:
Page 562 and 563:
Page 564 and 565:
Page 566 and 567:
Page 568 and 569:
Page 570 and 571:
Page 572 and 573:
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?