Modeling and Multivariate Methods - SAS

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690 Index Birth Death Subset.jmp 463 bivariate normal density ellipse 450 Borehole Latin Hypercube.jmp 300 Boston Housing.jmp 629, 635 Bounded 378 Box-Jenkins model see ARIMA Brown smoothing 379 C C. Total 58–59 calculation details 651–687 calibration 70–73 Candidates report, Partition platform 320 Canonical 3D Plot 500 canonical axis 163 Canonical Corr 163 canonical correlation 155, 164–165, 682 Canonical Curvature report 44 Canonical Options 499 canonical variables 161 Car Poll.jmp 396 Center Polynomials 50, 82 centroid 452, 457, 682 Centroid Method 467 Centroid Plot 161, 163 Centroid Val 163 Cheese.jmp 219 Chi Square 203 Chi-Square 202 Choice Modeling 401–438 Choice Platform Launch Platform 406 Response Data 407 Segmentation 423 Single Table Analysis 420 subject effects 410 Cholesterol.jmp 171 citations 639–650 classification variable 173 Close All Below (Partition Platform) 325 closing JMP Starter window 28 Cluster platform 459–476 compare methods 461 example 463 hierarchical 463–468 introduction 461–462 k-means 469–476 launch 462–463 normal mixtures 473–477 Cluster the Correlations 448 Clustering History table 464–465 Coding column property 83 collinearity 104 Color Clusters and Mark Clusters 466 Color Map 466 Color Map On Correlations 448 Color Map On p-values 448 Color Points 328, 499 Column Contributions 326 Column Info 104 column property Coding 83 Combine 140, 147 Common Factor Analysis 487 Compare Parameter Estimates 254 comparing models 633 Complete Linkage 468 Compound 161 compound multivariate example 171–173 computational details 651–687 Conditional Confidence CI 123 Conditional Mean CI 123 Conditional Pred Formula 123 Conditional Pred Values 123 Conditional Predictions 566 Conditional Residuals 123 Confidence Intervals 206, 367, 378, 414, 563 Confidence Limits 260, 270 Connecting Lines 355 Connecting Lines Report 112 constant estimate 363 Constrain fit 367 Constraints 575 contaminating 85 Continue 52 continuous response model 653 Contour Grid 578, 582 Contour Label 578 Contour Profiler 194, 262, 310, 312, 577–616 Contrast 233 Contrast 161–162, 169, 171, 192 contrast 110 contrast M matrix 160, 171, 681 Control-click 126 Cook’s D Influence 105 Copy Settings Script 565 correlation matrix 445 Correlation of Estimates 81–82, 194, 414 Correlation Type 295
Index 691 Cosine 359 count data 226 Covariance of Estimates 194 Covarimin 489 C p 141 Cp 141 CrabSattelites.jmp 183 Create SAS Job 365 Cronbach’s alpha 453–454 statistical details 458 Cross 39–40, 42 Cross Correlation 370 cross-product term 144, 558 Crosstab 394 Crosstab Report 112 Crosstab Transposed 394 Cube Plots 97 Cubic 295 cumulative logistic probability plot 209 Current Estimates table 143 current predicted value 93, 557 Current Value 562 current value 93, 557 Custom 161, 378 custom loss function 272 Custom Profiler 591 Custom Test 68–69, 76, 161, 192 CV for variance components 126 Cytometry.jmp 470 D Daganzo Trip.jmp 431 Daily 353 damped-trend linear exponential smoothing 380 Danger.jmp 453 Data Filter 566 data table of frequencies 226 decision trees 317 Default N Levels 566 Defect Parametric Profile 595, 603 Defect Profiler 600 Defect Profiler 595, 602 degenerate interval 72 degrees of freedom 59 Delta 77 DenDF 167 dendrogram 461, 464 Dendrogram Scale command 466 Density Ellipse 450 derivative 273–274 design code 651 design matrix 668 desirability confidence curve 93, 557 Desirability Functions 563, 567 desirability trace 567 Detailed Comparisons Report 113 Detergent.jmp 214 deviance 182 Deviance Residuals by Predicted 195 DF 59, 62–63, 202, 361 DFE 140, 260 Difference 202, 359 Differencing Order 366 Differencing Specification dialog 359 dimensionality 483 discriminant analysis 155, 173, 683 Dispersion Effects 303–314 distance column 174 Distance Graph 466 Distance Scale 466 Distribution platform 673 Dogs.jmp 170 Dose Response.jmp 208 double exponential smoothing 379 drag 93, 98, 353, 464, 557, 559, 568, 577 Drug.jmp 129, 131 dummy coding 67, 131, 657 Durbin-Watson Test 101 E E matrix 159, 166, 173, 681 Edit Formula 104 effect 656–672 Effect Attributes and Transformations 39–48 Effect Leverage 50, 55 Effect Leverage Pairs 105 Effect Macros 42 Effect Marginals 414, 419 Effect Screening 50, 82–89 Effect Screening table 92 effect sparsity 83, 231 effective hypothesis tests 661 EigenValue 163 eigenvalue decomposition 44, 483–484, 682 Eigenvectors 485 Eigvec 163 Ellipse alpha 451 Ellipse Color 451
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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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Contents Overview of Stepwise Regre
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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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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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Page 640 and 641: 640 References Becker, R.A. and Cle
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Page 652 and 653: Contents The Response Models . . .
Page 654 and 655: 654 Statistical Details Appendix A
Page 692 and 693: 692 Index Ellipses Transparency 451
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Page 696 and 697: 696 Index Customizing 267 Nonlinear
Page 698 and 699: 698 Index reliability analysis 453-
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Modeling and Multivariate Methods - SAS

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