Modeling and Multivariate Methods - SAS

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698 Index reliability analysis 453–454 also see Survival platform Remember Settings 565, 590 Remember Solution 260 REML (Recommended) 117 REML method 69 results 122–123 Remove 353 Remove All 140 Remove Contour Grid 583 Repeated Measures 161, 386 repeated measures design 114, 155 example 169, 171–173 replicated points 62 Reset Factor Grid 564 residual 59 residual matrix 166 Residual Statistics 365 residual/restricted maximum likelihood 122–123 Residuals 105, 310 residuals 114, 364, 653 Response data 404 Response Frequencies 387 Response Limits 596 response models 653–656 Response Specification dialog 160 Response Surface 41–42, 222 response surface 42–45 Response Surface Effect 45 Response Surface table 44 Restrict 140, 147 restricted vs. unrestricted parameterization 116 retrospective power analysis 75 rho see loss function right-click 123, 126 RMSE 77, 86, 633 RMSE 260 RMSE, model comparison 633 Robust Engineering 615–621 ROC 212 ROC Curve 337, 634 ROC Curve 337, 499 ROC Curves 336 Root Mean Square Error 58 row diagnostics 100–101 Roy’s Maximum Root 167 R-Square 633 RSquare 58, 141, 362 RSquare (U) 203 RSquare Adj 58, 141, 362 Rules 140 Run Model 71–72, 199, 223, 233 S Salt in Popcorn.jmp 224 sample autocorrelation function 355 sample of effects 113 Sampled 593 SAS GLM procedure 651, 653–666 saturated 62 Save 211, 360, 673 Save As Flash (SWF) 562 Save Best Transformation 99 Save Canonical Scores 162, 500 Save Cluster Hierarchy 467 Save Clusters 466, 472 Save Coding Table 106 Save Columns 44, 195, 365 Save Density Formula 472 Save Desirabilities 564 Save Desirability Formula 564 Save Discrim 173, 683 Save Display Order 466 Save Expanded Formulas 575 Save Expected Values 211–212 Save Gradients by Subject 414, 423 Save Inverse Prediction Formula 264 Save Leaf Label Formula 328 Save Leaf Labels 327 Save Leaf Number Formula 327 Save Leaf Numbers 327 Save Linear Constraints 566, 591 Save Mixture Formulas 472 Save Mixture Probabilities 472 Save Predicted 327, 331, 334 Save Predicted Values 104 Save Prediction Formula 263, 327, 331, 334 Save Probability Formula 211 Save Quantiles 211–212 Save Residual Formula 264 Save Residuals 327, 331, 334 Save Specific Solving Formula 264 Save Spectral Density 358 Save Std Error of Individual 264 Save Std Error of Predicted 105, 263 Save to Data Table 50 Save to Script window 50 Save Tolerant Prediction Formula 331 Save Utility Formula 414
Index 699 SBC 362 Scaled Estimates 82 Scatterplot Matrix 449 scatterplot matrix 445 Schwartz’s Bayesian Criterion 362 Score Options 499 Score Plot 485 Scree Plot 485 screening analysis see Fit Model platform screening design 83 Seasonal ARIMA 351 Seasonal ARIMA 367 seasonal exponential smoothing 380 seasonal smoothing weight 377 Second 353 Second Deriv Method 246 Second Deriv. Method 273 Select Rows 325 select rows 464 Sensitivity Indicator 563 Separate Response 396 Separate Responses 386 Sequential Tests 68 Seriesg.jmp 353 seriesJ.jmp 368 Set Desirabilities 564, 568 Set Random Seed 595 Set Script 565 Set To Data in Row 565 Shaded Ellipses 451 Ship 186 Ship Damage.JMP 186 Ship Damage.jmp 270 Show 562 Show Biplot Rays 472, 499 Show Canonical Details 499 Show Confidence Interval 364–365 Show Constraints 582 Show Correlations 451 Show Current Value 582 Show Derivatives 262, 273 Show Distances to each group 499 Show Formulas 563 Show Graph 325 Show Histogram 451 Show Means CL Ellipses 499 Show Normal 50% Contours 499 Show Points 266, 325, 355, 364–365, 450, 499, 582 Show Prediction Expression 65 Show Probabilities to each group 499 Show Split Bar 326 Show Split Candidates 326 Show Split Count 326 Show Split Prob 326 Show Split Stats 326 Show Tree 325 Sigma 571 Sigma 77 significance of model 101 significance probability 447 stepwise regression 133 simple exponential smoothing 379 Simulate 601 Simulation Experiment 595 Simulator 566, 592 Simulator 566 Sine 359 Single Linkage 468 singularity 661–664, 670 SLS fit see Fit Model platform Small Tree View 326 Smoothing Model dialog 378 smoothing models 351, 376–381 smoothing weight 377 Solubility.jmp 483 Solution table 44–271 Solve for Least Significant Number 77 Solve for Least Significant Value 77 Solve for Power 77 SOM Technical Details 479 SOMs 477 Sort Spit Candidates 326 Source 59, 62–63 sources 639–650 Spearman’s Rho 455 Spearman’s Rho 449 Spec Limits 595–596 Specified Value 325 Spectral Density 357, 359 spectral density plots 351 sphericity 168–169 Split Best 324, 326 Split Here 324 Split History 326 split plot design 114 example 119–122 Split Specific 325 spring fitting machine 675–676 SS 144 SSE 99, 140, 260
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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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526 Scoring Tests Using Item Respon
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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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Page 652 and 653: Contents The Response Models . . .
Page 654 and 655: 654 Statistical Details Appendix A
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Modeling and Multivariate Methods - SAS

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