Modeling and Multivariate Methods - SAS

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700 Index Stable 363 Stable Invertible 378 Stack 226 Stacked Daganzo.jmp 433 Standard Deviation 362 standard least squares 48, 55 prediction equation 92 also see Fit Model platform Standardize Data 462 statistical details 651–687 Std Beta 60–61 Std Dev Formula 310 Std Err Scale 86 Std Error 60, 108, 363 Std Error of Individual 311 Std Error of Predicted 105, 311 Std Error of Residual 105 Std Narrow 604 StdErr Pred Formula 105 Step 140, 365 stepwise heredity 137 stepwise regression 48, 135, 147 categorical terms 146–147 example 135, 142–143, 145 Logistic 148 Stochastic Optimization.jmp 606 Stop 140 Stop At Boundaries 574 Studentized Deviance Residuals by Predicted 195 Studentized Pearson Residuals by Predicted 195 Studentized Residuals 105, 310 Subject data 404 Submit to SAS 51, 365 substitution effect 130 subunit effect 114 Sum 160–161, 169, 171 sum M matrix 171 Sum of Squared Errors 362 Sum of Squares 59, 62–63 Sum of Weights 59 Sum Wgts 203 Summary of Fit table 58 Surface Fill 549 Surface Plot 535 Constants 551 Control Panel 544 Dependent Variables 546 Variables 545 Surface Plot 578 Surface Profiler 591 Surface Profiler 194, 262, 310, 312, 537 survival regression see Fit Model platform symbolic derivative 273 synthetic denominator, effect 124 T t Ratio 60, 90, 364 T 2 Statistic 451 Table Format 393 Table Transposed 394 Taguchi 303 Target 568 Term 60, 363 Test 167 Test Details 161–162 Test Each Column Separately Also 160 Test Each Response 395, 397 Test Parallelism 253 Test Reponse Homogeneity 396 Test Response Homogeneity 395 Test Slices 113 Time Frequency 353 Time ID role 353 Time Series Graph 355 Time Series platform 351, 353–381 ARIMA 365–367 commands 355–360 example 353–354 launch 353–354 modeling report 360–365 Seasonal ARIMA 367 smoothing models 376–381 Time Series Plot 354 Time Series role 353 Tiretread.jmp 539–540, 559, 602, 616 tiretread.jmp 570 tooltips 29 Transfer Function 373 Transfer Function Models 368 Transfer Function Models 351 transform covariate 130 Transition Report 395 transmitted variation 615 tree diagram 464 trend 377 Turn At Boundaries 574
Index 701 tutorial examples analysis of covariance 128 compound multivariate model 171–173 contour profiler 93, 577–579 correlation 453 desirability profile 570–571 hierarchical clustering 463 inverse prediction 70–73 logistic regression 214–225, 268 multiple regression 135 one-way Anova 126 probit 269–270 random effects 117–118 repeated measures 169, 171–173 response surface 43–45 split plot design 119–122 stepwise regression 135, 142–143, 145 time series 353–354 tutorials 27 Two way clustering 466 Type 1 sums of squares 68 Type I sums of squares 68 Types III and IV hypotheses 660 W-Z Wald test 205, 220 Ward’s 468 Weekly 353 Weld-Repaired Castings.jmp 236 Whole Effects 140 Whole Model Leverage Plot 101 Whole Model table 166, 201 Wilcoxon rank-sum 674 Wilks’ Lambda 167 Winter’s method 381 within-subject 120, 169, 171 X role 268–270, 353 Y role 353, 462 Y, Prediction Formula 557 zero eigenvalue 44 Zero To One 378 zeroed 656 U uncertainty 674 Unconstrained 378 Unit Odds Ratios 207 Univariate 446 Univariate Tests Also 160, 170 unrestricted vs. restricted parameterization 116 Unthreaded 262 Up Dots 578, 582 USL Chop 605 usual assumptions 672–674 V validity 673 Value 167 Variability Chart platform 114 variance components 114, 116 Variance Effect Likelihood Ratio Tests 310 Variance Estimate 362 Variance Formula 310 Variance Parameter Estimates 309 Varimax 489 Variogram 351, 356, 360 VIF 61
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Version 10 Modeling and Multivariat
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INDIRECT OR CONSEQUENTIAL DAMAGES O
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6 Emphasis Options for Standard Lea
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8 Models with Crossed, Interaction,
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10 8 Analyzing Screening Designs Us
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12 Examining the Residuals . . . .
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14 Response Frequencies . . . . . .
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16 19 Analyzing Principal Component
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18 Interpreting the Profiles . . .
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20 Discriminant Analysis . . . . .
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Contents Book Conventions. . . . .
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24 Learn about JMP Chapter 1 Book C
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26 Learn about JMP Chapter 1 Book C
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28 Learn about JMP Chapter 1 Additi
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30 Learn about JMP Chapter 1 Additi
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Contents Launch the Fit Model Platf
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34 Introduction to the Fit Model Pl
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Contents Example Using Standard Lea
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56 Fitting Standard Least Squares M
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100 Fitting Standard Least Squares
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Contents Overview of Stepwise Regre
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136 Fitting Stepwise Regression Mod
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Contents Example of a Multiple Resp
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158 Fitting Multiple Response Model
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Contents Overview of Generalized Li
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180 Fitting Generalized Linear Mode
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Contents Introduction to Logistic M
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200 Performing Logistic Regression
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Contents Overview of the Screening
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232 Analyzing Screening Designs Cha
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Contents Introduction to the Nonlin
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244 Performing Nonlinear Regression
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Contents Overview of Neural Network
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280 Creating Neural Networks Chapte
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Contents Launching the Platform. .
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296 Modeling Relationships With Gau
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Contents Overview of the Loglinear
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306 Fitting Dispersion Effects with
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Contents Introduction to Partitioni
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318 Recursively Partitioning Data C
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Contents Launch the Platform . . .
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354 Performing Time Series Analysis
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Contents The Categorical Platform .
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386 Performing Categorical Response
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Contents Introduction to Choice Mod
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404 Performing Choice Modeling Chap
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Contents Launch the Multivariate Pl
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444 Correlations and Multivariate T
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Contents Introduction to Clustering
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462 Clustering Data Chapter 18 The
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464 Clustering Data Chapter 18 Hier
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470 Clustering Data Chapter 18 K-Me
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472 Clustering Data Chapter 18 K-Me
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474 Clustering Data Chapter 18 Norm
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476 Clustering Data Chapter 18 Norm
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478 Clustering Data Chapter 18 Self
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480 Clustering Data Chapter 18 Self
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Contents Principal Components. . .
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484 Analyzing Principal Components
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Contents Introduction . . . . . . .
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494 Performing Discriminant Analysi
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Contents Overview of the Partial Le
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508 Fitting Partial Least Squares M
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Contents Introduction to Item Respo
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Contents Surface Plots . . . . . .
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538 Plotting Surfaces Chapter 23 La
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544 Plotting Surfaces Chapter 23 Th
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546 Plotting Surfaces Chapter 23 Th
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548 Plotting Surfaces Chapter 23 Pl
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550 Plotting Surfaces Chapter 23 Pl
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552 Plotting Surfaces Chapter 23 Ke
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Contents Introduction to Profiling
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556 Visualizing, Optimizing, and Si
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Contents Example of Model Compariso
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630 Comparing Model Performance Cha
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640 References Becker, R.A. and Cle
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642 References Draper, N. and Smith
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644 References Hooper, J. H. and Am
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646 References Matsumoto, M. and Ni
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648 References Rawlings, J.O. (1988
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Page 654 and 655: 654 Statistical Details Appendix A
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Modeling and Multivariate Methods - SAS

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