List of Outputs

Chapter 22: The CATMOD Procedure

Output 22.1.1: Detergent Preference StudyLinear Model Analysis

Output 22.1.2: Population Profiles

Output 22.1.3: Response Profiles, Frequencies, and Probabilities

Output 22.1.4: Analysis of Variance and WLS Estimates

Output 22.1.5: Main-Effects Design Matrix

Output 22.1.6: ANOVA Table for the Main-Effects Model

Output 22.1.7: WLS Estimates for the Main-Effects Model

Output 22.2.1: Surgical DataAnalysis of Mean Scores

Output 22.2.2: Population Sizes

Output 22.2.3: Response Frequencies

Output 22.2.4: Design Matrix

Output 22.2.5: ANOVA Table

Output 22.2.6: Parameter Estimates

Output 22.3.1: Maximum Likelihood Logistic Regression

Output 22.3.2: Response Summaries

Output 22.3.3: Design Matrix

Output 22.3.4: Iteration History

Output 22.3.5: Analysis of Variance Table

Output 22.3.6: Maximum Likelihood Estimates

Output 22.3.7: Covariance Matrix

Output 22.3.8: Correlation Matrix

Output 22.4.1: Analysis of Bartletts DataLog-Linear Model

Output 22.4.2: Response Profiles

Output 22.4.3: Analysis of Variance Table

Output 22.4.4: Response Function Predicted Values

Output 22.4.5: Predicted Frequencies

Output 22.5.1: Log-Linear Model Analysis with Zero Frequencies

Output 22.5.2: Output from the ONEWAY option

Output 22.5.3: Profiles

Output 22.5.4: Frequency of Response by Response Number

Output 22.5.5: Analysis of Variance Table

Output 22.5.6: Contrasts between Monkeys ˜u and ˜v

Output 22.5.7: Response Function Predicted Values

Output 22.5.8: Predicted Frequencies

Output 22.6.1: Analysis of Multiple-Population Repeated Measures

Output 22.6.2: Response Profiles

Output 22.6.3: Response Frequencies

Output 22.6.4: Analysis of Variance Table

Output 22.6.5: Parameter Estimates

Output 22.6.6: Final ModelDesign Matrix

Output 22.6.7: ANOVA Table

Output 22.7.1: Vision StudyAnalysis of Marginal Homogeneity

Output 22.7.2: Response Profiles

Output 22.7.3: Response Frequencies

Output 22.7.4: Design Matrix

Output 22.7.5: ANOVA Table

Output 22.7.6: Parameter Estimates

Output 22.8.1: Logistic Analysis of Growth Curve

Output 22.8.2: Population and Response Profiles

Output 22.8.3: Response Frequencies

Output 22.8.4: Design Matrix

Output 22.8.5: Analysis of Variance

Output 22.8.6: Parameter Estimates

Output 22.8.7: Contrasts

Output 22.9.1: Diagnosis DataTwo Repeated Measurement Factors

Output 22.9.2: Response Profiles

Output 22.9.3: Response Frequencies

Output 22.9.4: Design Matrix

Output 22.9.5: ANOVA Table

Output 22.9.6: Diagnosis DataReduced Model

Output 22.9.7: Design Matrix

Output 22.9.8: ANOVA Table

Output 22.9.9: Parameter Estimates

Output 22.9.10: Correlation Matrix

Output 22.9.11: Diagnosis DataSensitivity and Specificity Analysis

Output 22.9.12: Design Matrix

Output 22.9.13: ANOVA Table

Output 22.9.14: Parameter Estimates

Output 22.9.15: Covariance Matrix

Output 22.10.1: Health Survey DataUsing Direct Input

Output 22.10.2: ANOVA Table

Output 22.10.3: Parameter Estimates

Output 22.10.4: Age<65 Contrast

Output 22.10.5: Design Matrix

Output 22.10.6: ANOVA Table

Output 22.10.7: Parameter Estimates

Output 22.11.1: Marketing Research DataObtaining Predicted Probabilities

Output 22.11.2: Profiles and Design Matrix

Output 22.11.3: ANOVA Table and Parameter Estimates

Output 22.11.4: Predicted Values and Residuals

Output 22.11.5: Predicted Probabilities Data Set

Chapter 23: The CLUSTER Procedure

Output 23.1.1: Statistics and Tree Diagrams for Six Different Clustering Methods

Output 23.2.1: Clusters for Birth and Death RatesMETHOD=AVERAGE

Output 23.2.2: Plot of Three Clusters, METHOD=AVERAGE

Output 23.2.3: Plot of Eight Clusters, METHOD=AVERAGE

Output 23.2.4: Clusters for Birth and Death RatesMETHOD=COMPLETE

Output 23.2.5: Clusters for Birth and Death RatesMETHOD=SINGLE

Output 23.2.6: Clusters for Birth and Death RatesMETHOD=TWOSTAGE, K=10

Output 23.2.7: Clusters for Birth and Death RatesMETHOD=TWOSTAGE, K=18

Output 23.3.1: Cluster Analysis of Fisher Iris DataCLUSTER with METHOD=WARD

Output 23.3.2: Cluster Analysis of Fisher Iris DataCLUSTER with METHOD=TWOSTAGE

Output 23.3.3: Preliminary Analysis of Fisher Iris Data

Output 23.3.5: Clustering Clusters: PROC CLUSTER with Wongs Hybrid Method

Output 23.4.1: Average Linkage Analysis of Mammals Teeth Data: Raw Data

Output 23.4.2: Average Linkage Analysis of Mammals Teeth Data: Standardized Data

Output 23.4.3: Analysis of Ten Random Permutations of Raw Mammals Teeth Data: Indeterminacy at the 4-Cluster Level

Output 23.4.4: Analysis of Ten Random Permutations of Standardized Mammals Teeth Data: No Indeterminacy at the 4-Cluster Level

Output 23.6.1: Analysis of Standardized Data

Output 23.6.2: Analysis of Standardized Row-Centered Logarithms

Output 23.6.3: Analysis of Standardized Row-Standardized Logarithms

Output 23.6.4: Analysis of Standardized Row-Standardized Logarithms and Color

Chapter 24: The CORRESP Procedure

Output 24.1.1: Simple Correspondence Analysis of a Contingency Table

Output 24.2.1: Multiple Correspondence Analysis of a Burt Table

Output 24.2.2: Plot of Multiple Correspondence Analysis of a Burt Table

Output 24.2.3: Correspondence Analysis of a Binary Table

Output 24.3.1: Simple Correspondence Analysis (Experimental)

Output 24.3.2: Multiple Correspondence Analysis (Experimental)

Output 24.4.1: Supplementary Observations Example

Output 24.4.2: Supplementary Observations Example

Chapter 25: The DISCRIM Procedure Chapter Contents

Output 25.1.1: Sample Distribution of Petal Width in Three Species

Output 25.1.2: Normal Density Estimates with Equal Variance

Output 25.1.3: Normal Density Estimates with Unequal Variance

Output 25.1.4: Kernel Density Estimates with Equal Bandwidth

Output 25.1.5: Kernel Density Estimates with Unequal Bandwidth

Output 25.2.1: Joint Sample Distribution of Petal Width and Petal Length in Three Species

Output 25.2.2: Normal Density Estimates with Equal Variance

Output 25.2.3: Normal Density Estimates with Unequal Variance

Output 25.2.4: Kernel Density Estimates with Equal Bandwidth

Output 25.2.5: Kernel Density Estimates with Unequal Bandwidth

Output 25.3.1: Quadratic Discriminant Analysis of Iris Data

Output 25.3.2: Covariance Matrices

Output 25.3.3: Homogeneity Test

Output 25.3.4: Squared Distances

Output 25.3.5: Tests of Equal Class Means

Output 25.3.6: Misclassified Observations: Resubstitution

Output 25.3.7: Misclassified Observations: Cross validation

Output 25.3.8: Output Statistics from Iris Data

Output 25.4.1: Linear Discriminant Function on Crop Data

Output 25.4.2: Misclassified Observations: Resubstitution

Output 25.4.3: Misclassified Observations: Cross Validation

Output 25.4.4: Classification of Test Data

Output 25.4.5: Output Data Set of the Classification Results for Test Data

Output 25.5.1: Quadratic Discriminant Function on Crop Data

Chapter 26: The DISTANCE Procedure

Output 26.1.1: Distance Matrix Based on the Jaccard Coefficient

Output 26.1.2: Clustering History

Output 26.1.3: Cluster Membership

Output 26.2.1: Distance Matrix Based on the DCORR Coefficient

Output 26.2.2: Pseudo F versus Number of Clusters when METHOD= WARD

Output 26.2.3: Pseudo F versus Number of Clusters when METHOD= AVERAGE

Output 26.2.4: Tree Diagram of Clusters versus Semi-Partial R-Square Values when METHOD= WARD

Output 26.2.5: Tree Diagram of Clusters versus Average Distance Between Clusters when METHOD= AVERAGE

Chapter 27: The FACTOR Procedure

Output 27.1.1: Principal Component Analysis

Output 27.2.16: Output Data Set

Output 27.2.1: Principal Factor Analysis

Output 27.2.2: Scree Plot

Output 27.2.3: Factor Pattern Matrix and Communalities

Output 27.2.4: Residual and Partial Correlations

Output 27.2.5: Root Mean Square Off-Diagonal Partials

Output 27.2.6: Unrotated Factor Pattern Plot

Output 27.2.7: Varimax RotationTransform Matrix and Rotated Pattern

Output 27.2.8: Varimax RotationVariance Explained and Communalities

Output 27.2.9: Varimax Rotated Factor Pattern Plot

Output 27.2.10: Promax RotationProcrustean Target and Transform Matrix

Output 27.2.11: Promax RotationOblique Transform Matrix and Correlation

Output 27.2.12: Promax RotationRotated Factor Pattern and Correlations

Output 27.2.13: Promax RotationVariance Explained and Factor Structure

Output 27.2.14: Promax RotationVariance Explained and Final Communalities

Output 27.2.15: Promax Rotated Factor Pattern Plot

Output 27.2.17: Harris-Kaiser Rotation

Output 27.3.1: Maximum Likelihood Factor Analysis

Output 27.3.2: Maximum Likelihood Factor AnalysisTwo Factors

Output 27.3.3: Maximum Likelihood Factor AnalysisThree Factors

Output 27.4.1: QuartiminRotated Factor Solution with Standard Errors

Output 27.4.2: Interpretations of Factors Using Rotated Factor Pattern

Output 27.4.3: Interpretations of Factors Using Factor Structure

Chapter 28: The FASTCLUS Procedure

Output 28.1.1: Fishers Iris DataPROC FASTCLUS with MAXC=2 and PROC FREQ

Output 28.1.2: Fishers Iris DataPROC FASTCLUS with MAXC=3 and PROC FREQ

Output 28.1.3: Fishers Iris DataPROC CANDISC and PROC GPLOT

Output 28.2.1: Preliminary Analysis of Data with OutliersPROC FASTCLUS and PROC GPLOT

Output 28.2.2: Analysis of Data with Outliers using the LEAST= Option

Output 28.2.3: Cluster Analysis with Outliers OmittedPROC FASTCLUS and PROC GPLOT

Output 28.2.4: Final Analysis with Outliers Assigned to ClustersPROC FASTCLUS and PROC GPLOT

Chapter 29: The FREQ Procedure

Output 29.1.1: Frequency Tables

Output 29.1.2: Crosstabulation Table

Output 29.1.3: OUT= Data Set

Output 29.2.1: One-Way Frequency Table with BY Groups

Output 29.3.1: Binomial Proportion for Eye Color

Output 29.3.2: Binomial Proportion for Hair Color

Output 29.4.1: Contingency Table

Output 29.4.2: Chi-Square Statistics

Output 29.4.3: Relative Risk

Output 29.5.1: Contingency Table

Output 29.5.2: Chi-Square Statistics

Output 29.5.3: Output Data Set

Output 29.6.1: Cochran-Mantel-Haenszel Statistics

Output 29.6.2: CMH OptionRelative Risks

Output 29.6.3: CMH OptionBreslow-Day Test

Output 29.7.1: Contingency Table

Output 29.7.2: Measures of Association

Output 29.7.3: Trend Test

Output 29.8.1: CMH StatisticsStratifying by Subject

Output 29.8.2: CMH StatisticsNo Stratification

Output 29.9.1: One-Way Frequency Tables

Output 29.9.2: Measures of Agreement

Output 29.9.3: Cochrans Q

Chapter 30: The GAM Procedure (Experimental)

Output 30.1.1: GENMOD Analysis: Partial Output

Output 30.1.2: Summary Statistics

Output 30.1.3: Model Fit Statistics

Output 30.1.4: Partial Prediction for Each Predictor (Experimental)

Output 30.1.5: Analysis After Removing NumVert=14

Output 30.1.6: Partial Prediction After Removing NumVert=14 (Experimental)

Output 30.1.7: Joint Linear and Quadratic Tests

Output 30.2.1: PROC GENMOD Listing for Type III Analysis

Output 30.2.2: Predicted Seasonal Trend from a Parametric Model Fit Using a CLASS Statement

Output 30.2.3: PROC GAM Listing for Cubic Spline Regression Using the METHOD=GCV Option

Output 30.2.4: Model Fit Statistics

Output 30.2.5: Predicted Seasonal Trend from a Cubic Spline Model (Experimental)

Output 30.2.6: Estimated Nonparametric Factor of Seasonal Trend, Along with 95% Confidence Bounds (Experimental)

Output 30.3.1: Surface Plot of Yield by Temperature and Amount of Catalyst

Output 30.3.2: Fitted Regression Surfaces

Output 30.3.3: Cross sections of Fitted Regression Surfaces

Output 30.3.4: Raw Data from Experiment B

Output 30.3.5: Fitted Regression Surfaces

Output 30.3.6: Scatterplots of Yield by Catalyst