Modeling and Multivariate Methods - SAS

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688 Statistical Details Appendix A Details of Random Effects
Index JMP Modeling and Multivariate Methods Symbols ^, redundant leaf labels 327 &LogVariance 305 &Random 119 Numerics 2D Gaussian Process Example.jmp 295 –2LogLikelihood 363 95% bivariate normal density ellipse 450 A AAE 633 AAE, model comparison 633 Accept Current Estimates 262 Actual by Predicted 195 Actual by Predicted Plot 336 actual vs. regressor plot see Leverage Plot Add 39–40, 42 Add Column 69 Add Multivariate Noise 594 Add Random Noise 594 Add Random Weighted Noise 594 added variable plot 101 ADF tests 370 Adjusted Power and Confidence Interval 78–79, 81, 683–685 agglomerative clustering 461 Agreement Statistic 395 AIC 362 AIC 141 Akaike’s Information Criterion 362 algorithms 651–687 Aligned Responses 386 Alpha 77 Alt-click 561 Alter Linear Constraints 566, 591 alternative methods 674 analysis of covariance example 128 analysis of variance example 126 Analysis of Variance table 44, 59, 201 Angular Frequency 359 Annual 353 Append Settings to Table 565 Approx. F 167 approximate F test 681 ApproxStdErr 260 AR Coefficients 351, 357 ArcBall 551 ARIMA 351, 365–367, 377 assess validity 673 assumptions 672–674 AttributeGauge.jmp 398 AUC Comparison 634 Augmented Dickey-Fuller test 370 Autocorrelation 355, 360 autocorrelation 354–355 Autocorrelation Lags 353 Automatic Histogram Update 595 autoregressive coefficients see AR coefficients Autoregressive Order 366 Average Linkage 467 B Backward 139 Baltic.jmp 507 bar chart of correlations 447 Bartlett’s Kolmogorov-Smirnov 358 Baseball.jmp 117 Bayes Plot 85–87 between-subject 120, 171 bibliographic references 639–650 BIC 362 Big Class.jmp 63 Biplot 472 Biplot 3D 472 Biplot Options 472 Biplot Ray Position 472, 499 biplot rays 486 Biquartimax 489 Biquartimin 489
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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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Page 652 and 653: Contents The Response Models . . .
Page 654 and 655: 654 Statistical Details Appendix A
Page 690 and 691: 690 Index Birth Death Subset.jmp 46
Page 692 and 693: 692 Index Ellipses Transparency 451
Page 694 and 695: 694 Index test for curvature 46 Kru
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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