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Chapter 2: Introduction to Regression Procedures
Figure 2.1: Regression for Weight and Height Data
Figure 2.2: Regression for Weight and Height Data
Figure 2.3: Default Multivariate Tests
Figure 2.4: Multivariate Tests with MSTAT=EXACT
Chapter 6: Introduction to Discriminant Procedures
Figure 6.1: Groups for Contrasting Univariate and Multivariate Analyses
Figure 6.2: Contrasting Univariate and Multivariate Analyses
Chapter 7: Introduction to Clustering Procedures
Figure 7.1: Data Containing Well-Separated, Compact Clusters: PROC CLUSTER with METHOD=SINGLE and PROC GPLOT
Figure 7.2: Data Containing Poorly Separated, Compact Clusters: Plot of True Clusters
Figure 7.3: Data Containing Poorly Separated, Compact Clusters: PROC FASTCLUS
Figure 7.4: Data Containing Poorly Separated, Compact Clusters: PROC CLUSTER with METHOD=WARD
Figure 7.5: Data Containing Poorly Separated, Compact Clusters: PROC CLUSTER with METHOD=AVERAGE
Figure 7.6: Data Containing Poorly Separated, Compact Clusters: PROC CLUSTER with METHOD=CENTROID
Figure 7.7: Data Containing Poorly Separated, Compact Clusters: PROC CLUSTER with METHOD=TWOSTAGE
Figure 7.8: Data Containing Poorly Separated, Compact Clusters: PROC CLUSTER with METHOD=SINGLE
Figure 7.9: Data Containing Generated Clusters of Unequal Size
Figure 7.10: Data Containing Compact Clusters of Unequal Size: PROC FASTCLUS
Figure 7.11: Data Containing Compact Clusters of Unequal Size: PROC CLUSTER with METHOD=WARD
Figure 7.12: Data Containing Compact Clusters of Unequal Size: PROC CLUSTER with METHOD=AVERAGE
Figure 7.13: Data Containing Compact Clusters of Unequal Size: PROC CLUSTER with METHOD=CENTROID
Figure 7.14: Data Containing Compact Clusters of Unequal Size: PROC CLUSTER with METHOD=TWOSTAGE
Figure 7.15: Data Containing Compact Clusters of Unequal Size: PROC CLUSTER with METHOD=SINGLE
Figure 7.16: Data Containing Parallel Elongated Clusters: PROC FASTCLUS
Figure 7.17: Data Containing Parallel Elongated Clusters: PROC CLUSTER with METHOD=AVERAGE
Figure 7.18: Data Containing Parallel Elongated Clusters: PROC CLUSTER with METHOD=TWOSTAGE
Figure 7.19: Data Containing Parallel Elongated Clusters: PROC ACECLUS
Figure 7.20: Data Containing Parallel Elongated Clusters After Transformation by PROC ACECLUS
Figure 7.21: Transformed Data Containing Parallel Elongated Clusters: PROC CLUSTER with METHOD=WARD
Figure 7.22: Data Containing Nonconvex Clusters: PROC FASTCLUS
Figure 7.23: Data Containing Nonconvex Clusters: PROC CLUSTER with METHOD=CENTROID
Figure 7.24: Data Containing Nonconvex Clusters: PROC CLUSTER with METHOD=TWOSTAGE
Chapter 13: Introduction to Structural Equation Modeling
Figure 13.1: Measurement Error Model for Corn Data
Figure 13.2: Spleen Data: Parameter Estimates for Overidentified Model
Figure 13.3: Spleen Data: Fit Statistics for Overidentified Model
Figure 13.4: Spleen Data: Parameter Estimated for Just Identified Model
Figure 13.5: Path Diagram: Spleen
Figure 13.6: Path Diagram: Spleen
Figure 13.7: Spleen Data: RAM Model
Figure 13.8: Spleen Data: RAM Model with Names for Latent Variables
Figure 13.9: Spleen Data: OUTRAM= Data Set with Final Parameter Estimates
Figure 13.10: Spleen Data: RAM Model with INRAM= Data Set
Figure 13.11: Path Diagram: Lord
Figure 13.12: Lord Data: Major Results for RAM Model, Hypothesis H4
Figure 13.13: Lord Data: Using LINEQS Statement for RAM Model, Hypothesis H4
Figure 13.14: Lord Data: Major Results for Hypothesis H3
Figure 13.15: Lord Data: Major Results for Hypothesis H2
Figure 13.16: Lord Data: Major Results for Hypothesis H1
Figure 13.17: Path Diagram: Career Aspiration Jreskog and Srbom
Figure 13.18: Career Aspiration Data: J&S Analysis 1
Figure 13.19: Career Aspiration Data: J&S Analysis 2
Figure 13.20: Path Diagram: Career Aspiration Loehlin
Figure 13.21: Career Aspiration Data: Loehlin Model 1
Figure 13.22: Career Aspiration Data: Loehlin Model 2
Figure 13.23: Career Aspiration Data: Loehlin Model 3
Figure 13.24: Career Aspiration Data: Loehlin Model 4
Figure 13.25: Career Aspiration Data: Loehlin Model 5
Figure 13.26: Career Aspiration Data: Loehlin Model 7
Figure 13.27: Career Aspiration Data: Loehlin Model 6
Figure 13.28: Career Aspiration Data: Model Comparisons
Chapter 14: Using the Output Delivery System
Figure 14.1: Partial Contents of the SAS Log: Result of the ODS TRACE ON Statement
Figure 14.2: The Results Window from the SAS Explorer
Chapter 15: Statistical Graphics Using ODS (Experimental)
Figure 15.1: Fit Diagnostics Panel
Figure 15.2: Residual Plot
Figure 15.3: Fit Plot
Figure 15.4: Contour Plot of Estimated Density
Figure 15.5: Surface Plot of Estimated Density
Figure 15.6: Current Folder (Right Bottom)
Figure 15.7: Disabling View of Results as Generated
Figure 15.8: Selecting an External Browser
Figure 15.9: Changing the Default External Browser
Figure 15.10: ODS Trace Record in SAS Log
Figure 15.11: HTML Output with Default Style
Figure 15.12: HTML Output with Journal Style
Figure 15.13: Requesting the Templates Window in the Command Line
Figure 15.14: Result of ODS PATH SHOW Statement
Figure 15.15: SAS Registry Editor
Figure 15.16: Selecting a Default Style for HTML Destination
Figure 15.17: Label Collision Avoidance
Chapter 16: The ACECLUS Procedure
Figure 16.1: Scatter Plot of Original Poverty Data: Birth Rate versus Death Rate
Figure 16.2: Means, Standard Deviations, and Covariance Matrix from the ACECLUS Procedure
Figure 16.3: Table of Iteration History from the ACECLUS Procedure
Figure 16.4: Approximate WithinCluster Covariance Estimates
Figure 16.5: Raw and Standardized Canonical Coefficients from the ACECLUS Procedure
Figure 16.6: Scatter Plot of Poverty Data, Identified by Cluster
Figure 16.7: Scatter Plot of Canonical Variables
Chapter 17: The ANOVA Procedure
Figure 17.1: Class Level Information
Figure 17.2: ANOVA Table
Figure 17.3: Tukeys Multiple Comparisons Procedure
Figure 17.4: Box Plot of Nitrogen Content for each Treatment (Experimental)
Figure 17.5: Class Level Information
Figure 17.6: Overall ANOVA Table for Yield
Figure 17.7: Tests of Effects for Yield
Figure 17.8: ANOVA Table for Worth
Figure 17.9: Means of Yield and Worth
Chapter 18: The BOXPLOT Procedure
Figure 18.1: Box Plot for Power Output Data
Figure 18.2: Box Plot with Insets
Figure 18.3: Skeletal Box-and-Whisker Plot
Figure 18.4: Box Plot: the NOTCHES Option
Figure 18.5: BOXSTYLE= SCHEMATIC
Figure 18.6: Box Plot with Discrete Group Variable
Figure 18.7: Box Plot with Continuous Group Variable
Figure 18.8: Insets Positioned Using Compass Points
Figure 18.9: Positioning Insets in the Margins
Figure 18.10: Inset Positioned Using Data Unit Coordinates
Figure 18.11: Inset Positioned Using Axis Percent Unit Coordinates
Figure 18.12: Box Plot Using a Block Variable
Figure 18.13: Compressed Box Plots
Figure 18.14: Box Plot with Clip Factor of 1.5
Figure 18.15: Box Plot Using Clipping Options
Chapter 19: The CALIS Procedure
Figure 19.1: Path Diagram of Stability and Alienation Example
Figure 19.2: Path Diagram of Second-Order Factor Analysis Model
Figure 19.3: Examples of RAM Nomography
Figure 19.4: Within-List and Between-List Covariances
Figure 19.5: Exogenous and Endogenous Variables
Chapter 20: The CANCORR Procedure
Figure 20.1: Canonical Correlations, Eigenvalues, and Likelihood Tests
Figure 20.2: Multivariate Statistics and Approximate F Tests
Figure 20.3: Standardized Canonical Coefficients from the CANCORR Procedure
Figure 20.4: Canonical Structure Correlations from the CANCORR Procedure
Chapter 21: The CANDISC Procedure
Figure 21.1: Summary Information
Figure 21.2: MANOVA and Multivariate Tests
Figure 21.3: Canonical Correlations
Figure 21.4: Likelihood Ratio Test
Figure 21.5: Raw Canonical Coefficients
Figure 21.6: Class Means for Canonical Variables
Figure 21.7: Plot of First Two Canonical Variables
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SAS/STAT 9.1 Users Guide, Volumes 1-7
ISBN: 1590472438
EAN: 2147483647
Year: 2004
Pages: 156
Authors:
SAS Publishing
BUY ON AMAZON
Cisco IP Telephony (CIPT) (Authorized Self-Study) (2nd Edition)
Review Questions
Review Questions
Dropping Conference Calls
PKI Revocation and Key Storage
Summary
Competency-Based Human Resource Management
An Overview of Competency-Based HR Management Practices
Competency-Based Performance Management
Competency-Based Employee Development
Appendix B Further Suggestions on Employee Development
Appendix D Employee Development and Succession Management
Google Maps Hacks: Tips & Tools for Geographic Searching and Remixing
Hack 9. Use del.icio.us to Keep Up with Google Maps
Hack 14. Create Custom Map Markers
Hacks 2941: Introduction
Hack 37. View Your GPS Tracklogs in Google Maps
Hacks 4250: Introduction
Visual Studio Tools for Office(c) Using C# with Excel, Word, Outlook, and InfoPath
Conclusion
Events in Visual Studio Tools for Office
Conclusion
An End-to-End Scenario
Writing a COM Add-In Using Visual Studio
HTI+ Home Technology Integrator & CEDIA Installer I All-In-One Exam Guide
Computer Network Hardware
Troubleshooting and Maintaining Lighting Control Systems
Designing a Home Security System
Troubleshooting and Maintaining a Home Security System
Home Automation Controllers
Microsoft Office Visio 2007 Step by Step (Step By Step (Microsoft))
Modifying Shape Connections
Key Points
Exporting Timelines to Create Gantt Charts
Creating Scaled Office Spaces
Creating Network Reports
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