Modeling and Multivariate Methods - SAS

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696 Index Customizing 267 Nonlinear platform 241, 268–270 derivatives 273–274 Fit Curve 247–252 Nonparametric Correlations 449 Nonparametric Measures of Association table 449 nonstationary time series 359 Norm KHC 123 Normal censored 593 normal density ellipse 450 normal distribution 115, 676 Normal Plot 84 Normal Truncated 593 Normal Weighted 625 Normal weighted 593, 597 Nparm 62 Nugget parameters 298 null hypothesis 75 Number 77 Number of Clusters 466 Number of Forecast Periods 359 Number of Plots Across 530 Number of Plotted Points 562 Number of Points 263 NumDeriv 274 Numeric Derivatives Only 262, 269 O Obbiquartimax 489 Obequamax 489 Obfactorparsimax 489 Obj-Criterion 365 Oblimin 489 Obparsimax 490 Obquartimax 490 Observations 59, 203 Obvarimax 490 Odds Ratio 208 Odds Ratio Example 208 Odor Control Original.jmp 43 Offset 186 offset variable 186 opening JMP Starter window 28 Optimal Value 325 Option-click 561 OPTMODEL formulas 563 Ordered Differences Report 113 ordinal crossed model 669 ordinal factor 670 ordinal interaction 668 ordinal least squares means 670 Ordinal Logistic 48 ordinal logistic regression 218 ordinal response model 653, 655–656 Orthog Coded 90 Orthog t-Ratio 85, 90 Orthomax 489 orthonormal response design matrix 168 Other 451 Outlier Analysis 451 Outlier Distance plot 457 Output Grid Table 565–566 Output Random Table 566 Output Split Table 325 outside interval 72 P p, d, q parameters 366 Pairwise Correlations 446 Pairwise Correlations table 447 Parallel Coord Plots 472 Parameter 69, 143, 260, 263 Parameter Contour Profiler 262 Parameter Estimate Population table 89 Parameter Estimates Population table 84 Parameter Estimates table 43, 60, 186, 363 parameter interpretation 657 Parameter Power 75–76 Parameter Profiler 262 Parameter Surface Profiler 262 Parametric Survival 49 Pareto Plot 88 Parsimax 489 Partial Autocorrelation 356, 360 partial autocorrelation 354 Partial Corr table 446 partial correlation 446 Partial Least Squares platform validation 510 partial-regression residual leverage plot 101 Partition 315 Partition Platform 315–323 Paste Settings Script 565 PCA 483 Pearson correlation 447, 455 Pearson Residuals By Predicted 195 Per Mouse Move 578
Index 697 Per Mouse Up 578 Period 358 periodicity 367 Periodogram 359 periodogram 358 Periods Per Season 367, 378 personality 135 Pillai’s Trace 167 Pizza Choice Example 405–427 Pizza Combined.jmp 421 Pizza Profiles.jmp 407, 423 Pizza Responses.jmp 407, 423 Pizza Subjects.jmp 410, 423 Plot Actual by Predicted 634 Plot Actual by Predicted 100, 311 Plot Actual by Predicted (Partition) 326 Plot Effect Leverage 100 Plot Residual By Predicted 100 Plot Residual By Row 100 Plot Residual by Row 634 Plot Studentized Residual by Predicted 311 Plot Studentized Residual by Row 311 PLS 505, 507–522 Statistical Details 520–522 Poisson loss function 270–271 Polynomial 161, 169 Polynomial to Degree 41 population of levels 113 posterior probability 87 power 75, 81 Power Analysis 75–88, 683–685 Power Plot 79 Predicted Values 104 prediction equation 92, 130 Prediction Formula 44, 104, 310 prediction formula 211, 656–669 Prediction Profiler 93, 557 Predictor role 268–270 Predictors report 633 Press 100 pressure cylinders fitting machine 677 Principal Components 487 principal components analysis 483 prior probability 85 Prob to Enter 139 Prob to Leave 139 Prob>|t| 60, 90, 364 Prob>ChiSq 203 Prob>F 60, 62, 64, 167 Prob>F in quotes 144 probit example 269–270 process disturbance 351 product-moment correlation 447, 455 Profile 161, 169 profile confidence limits 271–272 Profile data 404 profile likelihood confidence intervals 271 profile trace 557 Profiler and saved standard error formulas 106 Profiler 93, 194, 214, 262, 310, 312, 414, 557 Profilers 553–621 Prop of Error Bars 563, 571 Proportional Hazards 49 Proportional Hazards also see Fit Model platform Prune Below 325 Prune Worst 325–326 pseudo standard error 92 pure error 62 Q quadratic equation 686 quadratic ordinal logistic regression 224 Quartimax 489 Quartimin 490 QuasiNewton BFGS 262 QuasiNewton SR1 262 questionnaire analysis 453–454 R R 144 &Random 119 Random 593 Random Effect 45, 119, 135 random effects 69, 114 introduction 114 Range Odds Ratios 207 RASE 633 RASE, model comparison 633 Rater Agreement 387 Reactor.jmp 144 reduce dimensions 483 Reduced 202 Ref Labels 582 Ref Lines 582 references 639–650 relative significance 673
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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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Page 654 and 655: 654 Statistical Details Appendix A
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Modeling and Multivariate Methods - SAS

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