Modeling and Multivariate Methods - SAS

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694 Index test for curvature 46 Kruskal-Wallis 674 L L matrix 681 lack of fit error 62, 130 Lack of Fit table 62, 203, 220 Lag 363 Laptop Profile.jmp 415 Laptop Runs.jmp 416 Laptop Subjects.jmp 417 Latin Square design 124 layered design 115, 124 Leaf Report 326 least significant number 75, 80–81, 683–685 least significant value 75, 80–81, 683–685 Least Sq Mean 108 least squares fit introduction 653 prediction equation 92 least squares means 660, 670 Least Squares Means table 108 Legend 466 Lenth’s method 92 Lenth’s PSE 233, 239 Level 108 level smoothing weight 377 Leverage Plot 101–104, 130, 678–680 confidence curves 103 interpret 103–104 Lift Curve 339, 635 Lift Curves 339 likelihood confidence intervals 271 Likelihood Ratio test 205, 220 Likelihood Ratio Tests 409 Likelihood Ratio Tests 205, 414 limitations of techniques 672–674 linear combination 483 Linear Constraints 583 Linear Constraints 575 linear dependence 661–664 linear exponential smoothing 380 linear rank tests 674 Link Profilers 565 Load Version 3 Model 51 Loading Plot 485 Lock 143, 325 Lock Columns Partition platform 326 Lock Factor Setting 562, 565 Lock Z Scale 545 Log Iterations 591 Log Variance Effect 45 Logistic platform 197 example 214–225, 268 response function 197 also see Fit Model platform, Fit Y by X platform Logistic Stepwise Regression 148 Logistic w Loss.jmp 268 –LogLikelihood 202 Log-Linear Variance 303 LogLinear Variance 48 Loglinear Variance 305 LogLinear Variance Model 303, 305–314 log-variance effect 305 longitudinal data 169 Loss 268–269 loss function custom 272 Lost DFs 62 Lower CL and Upper CL 260 LSL Chop 604 LSMeans 109 LSMeans Contrast 110, 128 LSMeans Plot 109 LSN 75, 80–81, 683–685 LSV 75, 80–81, 683–685 Lung Cancer Responses.jmp 428 Lung Cancer.jmp 430 M M matrix 160, 166, 171, 681 machines of fit 674 Macros 41, 43, 215 MAE 363 Mahalanobis distance 452, 457 plot 451 Make Model 140, 147–148, 233 Make Table 595, 597 MANOVA 115, 167 Manova 48 manova 115 Manova test tables 167 MAPE 362 Mauchly criterion 169 Max 263 Max RSq 64 Maximization Options 563
Index 695 maximization step 462 Maximize 568 Maximize Desirability 563 Maximize for Each Grid Point 564 Maximize for each Grid Point 413 maximum likelihood 199, 654 Maximum Value 562 Mean 108, 161, 169 mean 653 Mean Abs Dev 634 Mean Confidence Interval 105, 311 Mean Line 355 Mean -Log p 633 mean model 305 Mean of Response 59 mean shift 604 Mean Square 60, 64 measurement study 114 Measures of Fit report 633 menu tips 29 Mesh 549 Method 117 Min 263 Minimal Report 50 Minimize 568 Minimum Setting 562 Minimum Size Split 326 Minimum Theta Value 295 Minute 353 Misclassification Rate 634 missing cells 661–664, 670 nominal vs. ordinal factor 670–672 missing value 446 missing values 447 missing values (Fit Model) 52 Mixed 139 mixed model 113, 115 Mixture Effect 45 Mixture Profiler 579 Mixture Response Surface 41 Model 59, 202 Model Averaging 634 Model Comparison 633 Model Comparison platform 627 example 629–632, 635–637 launch 632 options 634 report 633 Model Comparison table 360 Model Dialog 414 model effects buttons 39–40 Model Library 264 model significance 101 Model Summary table 361, 367 Monte Carlo simulation 231 Monthly 353 Moving Average Order 366 MSE 140, 260 Multiple Delimited 387 multiple inference 673 multiple regression example 135 Multiple Response 387 Multiple Response by ID 387 multiple response fitting 559, 570 Multivariate 594 Multivariate 441, 443 multivariate analysis of variance see Fit Model platform multivariate least-squares means 161 multivariate mean 155, 452, 457 multivariate outliers 452 Multivariate platform 680–683 example 453 principal components 483 N N Runs 595 N Strata 595, 626 nDF 143 Negative Exponential.jmp 273 negative log-likelihood 201, 675 negative variance components 116 Nest 39–40 nested effect 120, 124, 659, 665 neutral values 107 New Column 111 Newton 262 Newton-Raphson method 273 No Noise 594 No Rules 140 Noise Factors 615–621 Noise Factors 557 Nominal 203 nominal factor 657, 660–664 Nominal Logistic 48 nominal response model 653–655 nonestimable 656 nonlinear fit options 261 Nonlinear Model Library 264
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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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Page 652 and 653: Contents The Response Models . . .
Page 654 and 655: 654 Statistical Details Appendix A
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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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