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Chapter 61: The REG Procedure
Output 61.1.1: Forward Selection Method: PROC REG
Output 61.1.2: Backward Selection Method: PROC REG
Output 61.1.3: Maximum R-Square Improvement Selection Method: PROC REG
Output 61.1.4: Forward Selection Summary
Output 61.1.5: All Models by the RSQUARE Method: PROC REG
Output 61.2.1: Height and Weight Data: Female Children
Output 61.2.2: Height and Weight Data: Male Children
Output 61.2.3: SSCP Matrix
Output 61.2.4: OUTEST Data Set
Output 61.3.1: ANOVA Table and Parameter Estimates
Output 61.3.2: ANOVA Table and Parameter Estimates
Output 61.3.3: Plot of Residual vs. Predicted Values
Output 61.3.4: Plot of Predicted vs. Size
Output 61.4.1: Simple Linear Regression
Output 61.5.1: C
p
Plot
Output 61.6.1: Controlling Plot Appearance and Plotting OUTEST= Statistics
Output 61.7.1: Plotting Model Diagnostic Statistics
Output 61.8.1: Normal Probability-Probability Plot for the Residuals
Output 61.8.2: Normal Quantile-Quantile Plot for the Residuals
Output 61.9.1: Prediction Intervals
Output 61.10.1: Using the RIDEGPLOT Option for Ridge Regression
Output 61.10.2: Ridge Traces Produced with ODS Graphics (Experimental)
Output 61.11.1: Using PROC REG to Plot the VIFs
Output 61.12.1: Fit Diagnostics For the Model Linear in Year (Experimental)
Output 61.12.2: Residual By Year For the Model Linear in Year (Experimental)
Output 61.12.3: Fit Plot with Confidence Band and Prediction Limits (Experimental)
Output 61.12.4: Fit Diagnostics For the Model Linear in Year (Experimental)
Output 61.12.5: Residual Histogram (Experimental)
Chapter 62: The ROBUSTREG Procedure
Output 62.1.2: M Estimates for Data with 10% Contamination
Output 62.1.3: MM Estimates for Data with 10% Contamination
Output 62.1.1: OLS Estimates for Data with 10% Contamination
Output 62.1.4: M Estimates for Data with 40% Contamination
Output 62.1.5: MM Estimates for Data with 40% Contamination
Output 62.1.6: S Estimates for Data with 1% Leverage Points
Output 62.1.7: MM Estimates for Data with 1% Leverage Points
Output 62.2.1: Overall ANOVA
Output 62.2.2: Model ANOVA
Output 62.2.3: Model Fitting Information and Summary Statistics
Output 62.2.4: Model Parameter Estimates
Output 62.2.5: Diagnostics
Output 62.2.6: Test of Significance
Output 62.2.7: ROBUSTREG Output
Output 62.3.1: OLS Estimates
Output 62.3.2: Model Fitting Information and Summary Statistics
Output 62.3.3: M estimates
Output 62.3.4: Diagnostics
Output 62.3.5: Goodness-of-Fit
Output 62.3.6: LTS estimates
Output 62.3.7: Diagnostics and LTS-Rsquare
Output 62.3.8: Final Weighted LS estimates
Chapter 63: The RSREG Procedure
Output 63.1.1: Coding and Response Variable Information
Output 63.1.2: Analyses of Variance
Output 63.1.3: Canonical Analysis
Output 63.1.4: Ridge Analysis
Output 63.1.5: Contour Plot of Predicted Response Surface
Output 63.2.1: Analysis of Variance Ignoring Covariates
Output 63.2.2: Analysis of Variance Including Covariates
Chapter 64: The SCORE Procedure
Output 64.1.1: Creating an OUTSTAT= Data Set with PROC FACTOR
Output 64.1.2: OUTSTAT= Data Set from PROC FACTOR Reproduced with PROC PRINT
Output 64.1.3: OUT= Data Set from PROC SCORE Reproduced with PROC PRINT
Output 64.2.1: Creating an OUTEST= Data Set with PROC REG
Output 64.2.2: OUTEST= Data Set from PROC REG Reproduced with PROC PRINT
Output 64.2.3: Predicted and Residual Scores from the OUT= Data Set Created by PROC SCORE and Reproduced Using PROC PRINT
Output 64.2.4: Listing of the Fitness2 Data Set
Output 64.2.5: Predicted Scores from the OUT= Data Set Created by PROC SCORE and Reproduced Using PROC PRINT
Output 64.3.1: Custom Scoring Data Set and Scored Fitness Data: PROC PRINT
Output 64.3.2: Custom Scored Fitness Data: PROC PRINT
Chapter 65: The SIM2D Procedure
Output 65.1.1: Conditional Simulation of Coal Seam Thickness
Chapter 66: The STDIZE Procedure
Output 66.1.2: Data Is Standardized by PROC STDIZE with METHOD=RANGE
Output 66.1.3: Data Is Standardized by PROC STDIZE with METHOD=AGK(.14)
Output 66.1.4: Data Is Standardized by PROC STDIZE with METHOD=SPACING(.14)
Output 66.1.5. Untransformed Data
Output 66.1.6. Data Is Transformed by PROC ACECLUS
Output 66.1.1: Data Is Standardized by PROC STDIZE with METHOD=STD
Chapter 67: The STEPDISC Procedure
Output 67.1.1: Iris Data: Summary Information
Output 67.1.2: Iris Data: Between-Class and Total-Sample SSCP Matrices
Output 67.1.3: Iris Data: Stepwise Selection Step 1
Output 67.1.4: Iris Data: Stepwise Selection Step 2
Output 67.1.5: Iris Data: Stepwise Selection Step 3
Output 67.1.6: Iris Data: Stepwise Selection Step 4
Output 67.1.7: Iris Data: Stepwise Selection Step 5
Output 67.1.8: Iris Data: Stepwise Selection Summary
Chapter 68: The SURVEYFREQ Procedure
Output 68.1.1: Data Summary and Stratum Information
Output 68.1.2: Two-Way Table of Department by Response
Output 68.1.3: Table of Department by Response with Row Percentages
Output 68.1.4: Wald Chi-Square Test
Output 68.2.1: Multiway Table of Department by SchoolType by Response
Output 68.3.1: ResponseTable Output Data Set
Output 68.3.2: ResponseSummary Output Data Set
Chapter 69: The SURVEYLOGISTIC Procedure
Output 69.1.1: Web Design Survey Sample (First 20 Observation)
Output 69.1.2: Web Design Survey, Model Information
Output 69.1.3: Web Design Survey, Testing the Proportional Odds Assumption
Output 69.1.4: Web Design Survey, Model Information
Output 69.1.5: Web Design Survey, Class Level Information
Output 69.1.6: Web Design Survey, Parameter and Odds Ratio Estimates
Output 69.2.1: 1999 Full-year MEPS (First 30 Observations)
Output 69.2.2: MEPS, Model Information
Output 69.2.3: MEPS, Number of Observations
Output 69.2.4: MEPS, Response Profile
Output 69.2.5: MEPS, Classification Levels
Output 69.2.6: MEPS, Parameter Estimates
Output 69.2.7: MEPS, Odds Ratios
Chapter 70: The SURVEYMEANS Procedure
Output 70.1.1: Data Summary and Class Information
Output 70.1.2: Stratum Information
Output 70.1.3: Statistics
Output 70.2.1: Company Profile Study
Output 70.2.2: Domain Analysis for Company Profile Study
Output 70.3.1: Estimate Ratios
Output 70.4.2: Analysis of Incomplete Ice Cream Data Treating Respondents as a
Output 70.4.1: Analysis of Incomplete Ice Cream Data Excluding Observations
Chapter 71: The SURVEYREG Procedure
Output 71.1.1: Summary of Regression Using Simple Random Sampling
Output 71.1.2: Regression Coefficient Estimates
Output 71.2.1: Regression Analysis for Simple Random Cluster Sampling
Output 71.2.2: Regression Analysis for Simple Random Sampling
Output 71.3.1: Use the Regression Estimator to Estimate the Population Total
Output 71.4.1: Data Summary and Stratum Information Fitting Model I
Output 71.4.2: Estimated Regression Coefficients and the Estimated Covariance Matrix
Output 71.4.3: Regression Results from Fitting Model II
Output 71.4.4: Regression Results for Fitting Model III
Output 71.5.1: Regression Estimator for the Total of CornYield under Model I
Output 71.5.2: Regression Estimator for the Total of CornYield under Model II
Output 71.5.3: Regression Estimator for the Total of CornYield under Model III
Output 71.6.1: Summary of Data and Regression
Output 71.6.2: Stratification Information
Output 71.6.3: Parameter Estimates and Effect Tests
Output 71.6.4: Summary of Data and Regression
Output 71.6.5: Stratification Information
Output 71.6.6: Parameter Estimates and Effect Tests
Chapter 72: The SURVEYSELECT Procedure
Output 72.1.1: Sample Selection Summary
Output 72.1.2: Customer Sample (First Stratum)
Output 72.2.1: Sampling Frame
Output 72.2.2: Sample Selection Summary
Output 72.2.3: Sample Hospitals
Output 72.3.1: Sampling Frame
Output 72.3.2: Sample Selection Summary
Output 72.3.3: Audit Sample
Chapter 73: The TPHREG Procedure (Experimental)
Output 73.1.1: Reference Coding of CLASS Variables
Output 73.1.2: Wald Tests for Individual Model Effects
Output 73.1.3: Inference about the Regression Parameters
Output 73.1.4: Overall Test for All Paired Cell -type Groups
Output 73.1.5: Hazards Ratios for All Paired Cell-type Groups
Chapter 74: The TPSPLINE Procedure
Output 74.1.1: Output from PROC TPSPLINE
Output 74.1.2: Plot of TPSPLINE Fit from the Partial Spline Model
Output 74.2.1: Output from PROC TPSPLINE with M=3
Output 74.2.2: Quadratic Surface Model: The REG Procedure
Output 74.2.3: Output from PROC TPSPLINE Using M=3 and DF=6
Output 74.3.1: Partial Output from PROC TPSPLINE for Data Set SO4
Output 74.3.2: GCV Function of SO4 Data Set
Output 74.3.3: Output from PROC TPSPLINE for Data Set SO4 with LOGNLAMBDA=
ˆ
2.56
Output 74.3.4: Contour Plot of TPSPLINE Estimates with Different Lambdas
Output 74.4.1: Output from PROC TPSPLINE without the D= Option
Output 74.4.2: Output from PROC TPSPLINE with the D= Option
Output 74.4.3: Comparison of Two Fits with and without the D= Option
Output 74.5.1: Output from PROC TPSPLINE for the Melanoma Data Set
Output 74.5.2: TPSPLINE Estimate and 90% Confidence Interval of Melanoma Data
Output 74.5.3: Output from PROC TPSPLINE for the Melanoma Data Set
Output 74.5.4: Comparison of Bayesian and Bootstrap Confidence Interval for Melanoma Data
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Table of content
SAS.STAT 9.1 Users Guide (Vol. 6)
ISBN: N/A
EAN: N/A
Year: 2004
Pages: 127
BUY ON AMAZON
Absolute Beginner[ap]s Guide to Project Management
What Is the Value of Project Management?
One Title, Many Roles
Principles of Managing Project Quality
Proven Techniques for Leading Cross-Functional Projects
Methods for Ending a Contract or a Project
Software Configuration Management
Introduction to Software Configuration Management
A Practical Approach to Documentation and Configuration Status Accounting
Configuration Management and Data Management
Configuration Management and Software Engineering Standards Reference
Appendix S Sample Maintenance Plan
Oracle Developer Forms Techniques
Tips for Standard GUI Practices and Forms Development
Summary
Form Management in an OPEN_FORM Configuration
Populating a PL/SQL Table from a Block
Creating a Tree Item
Developing Tablet PC Applications (Charles River Media Programming)
Working with VB .NET
Using Gestures to Control Tablet Media Player
Getting Started with Microsoft Agent
3D Rendering with OpenGL and DirectX 9
Using Third-Party Engines
Introducing Microsoft ASP.NET AJAX (Pro - Developer)
The AJAX Revolution
The Pulsing Heart of ASP.NET AJAX
Partial Page Rendering
Built-in Application Services
Remote Method Calls with ASP.NET AJAX
An Introduction to Design Patterns in C++ with Qt 4
Iterators
Returning References from Functions
QMetaObject: The MetaObject Pattern
Declarations and Definitions
Point of Departure
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