List of Outputs

Chapter 51: The NLMIXED Procedure

Output 51.5.1: Analysis Results for Failure Time Model

Output 51.5.2: Estimated Cumulative Distribution Function

Output 51.5.3: Analysis Results for Frailty Model

Output 51.5.4: Patient-Specific CDFs and Predicted Values. Pain Reliever 1: Solid Line, Closed Circles. Pain Reliever 2: Dashed Lines, Open Circles.

Chapter 52: The NPAR1WAY Procedure

Output 52.1.1: Wilcoxon Two-Sample Test

Output 52.1.2: Median Two-Sample Test

Output 52.1.3: Empirical Distribution Function Statistics

Output 52.2.1: Wilcoxon Two-Sample Test

Output 52.3.1: Savage Multisample Test

Chapter 53: The ORTHOREG Procedure

Output 53.1.1: PROC ORTHOREG Results for Atomic Weight Example

Output 53.2.1: Wampler data: Deviations from Certified Values

Chapter 54: The PHREG Procedure

Output 54.1.1: Individual Score Test Results for All Variables

Output 54.1.2: First Model in the Stepwise Selection Process

Output 54.1.3: Score Tests Adjusted for the Variable LogBUN

Output 54.1.4: Second Model in the Stepwise Selection Process

Output 54.1.5: Third Model in the Stepwise Regression

Output 54.1.6: Final Model in the Stepwise Regression

Output 54.1.7: Model Selection Summary

Output 54.2.1: Best Variable Combinations

Output 54.3.1: Summary of Number of Case and Controls

Output 54.3.2: Conditional Logistic Regression Analysis for the Low Birth-Weight Study

Output 54.4.1: Heart Transplant Study Analysis I

Output 54.4.2: Heart Transplant Study Analysis II

Output 54.5.1: Cox Regression Analysis on the Survival of Rodents

Output 54.5.2: Plot of DFBETA Statistic for DOSE versus Subject Number

Output 54.5.3: Plot of DFBETA Statistic for NPAP versus Subject Number

Output 54.6.1: Survivor Function Estimates for LogBUN=1.0 and HGB=10.0

Output 54.6.2: Survival Curves for Specific Covariate Patterns

Output 54.7.1: Martingale Residual Plot

Output 54.7.2: Deviance Residual Plot

Output 54.8.1: Analysis of the Intensity Model

Output 54.8.2: Analysis of the Proportional Means Model

Output 54.8.3: Analysis of the PWP Total Time Model with Noncommon Effects

Output 54.8.4: Analysis of the PWP Gap Time Model with Noncommon Effects

Output 54.8.5: Summary of Bladder Tumor Recurrences in 86 Patients

Output 54.8.6: Analysis of Marginal Cox Models

Output 54.8.7: Tests of Treatment Effects

Output 54.9.1: Breakdown of Blindness in the Control and Treated Groups

Output 54.9.2: Inference Based on the Robust Sandwich Covariance

Output 54.10.1: Cox Model with Bilirubin as a Covariate

Output 54.10.2: Cumulative Martingale Residuals vs Bilirubin (Experimental)

Output 54.10.3: Typical Cumulative Residual Plot Patterns

Output 54.10.4: Model with log(Bilirubin) as a Covariate

Output 54.10.5: Panel Plot of Cumulative Martingale Residuals vs log(Bilirubin) (Experimental)

Output 54.10.6: Cumulative Martingale Residuals vs log(Bilirubin) (Experimental)

Output 54.10.7: Standardized Score Process for log(Bilirubin) (Experimental)

Output 54.10.8: Standardized Score Process for log(Protime) (Experimental)

Output 54.10.9: Kolmogorov-type Supremum Tests for Proportional Hazards Assumption

Chapter 55: The PLAN Procedure

Output 55.1.1: A Split-Plot Design

Output 55.2.1: A Hierarchical Design

Output 55.3.1: A Generalized Cyclic Block Design

Output 55.3.2: A Generalized Cyclic Block Design

Output 55.4.1: A Randomized Latin Square Design

Output 55.5.1: A Generalized Cyclic Incomplete Block Design

Output 55.6.1: List of Permutations

Output 55.6.2: List of Permutations

Output 55.6.3: Randomized Permutations

Output 55.6.4: List of Combinations

Output 55.6.5: Combinations Data Set Created by ODS

Chapter 56: The PLS Procedure

Output 56.1.1: Amount of Training Set Variation Explained

Output 56.1.2: First X- and Y-scores for Penta-Peptide Model 1

Output 56.1.3: Second X- and Y-scores for Penta-Peptide Model 1

Output 56.1.4: First and Second X-scores for Penta-Peptide Model 1

Output 56.1.5: First and Second X-weights for Penta-Peptide Model 1

Output 56.1.6: Estimated PLS Regression Coefficients and VIP (Model 1)

Output 56.2.1: Distances from the X-variables to the Model (Training Set)

Output 56.2.2: Distances from the Y-variables to the Model (Training Set)

Output 56.3.1: Spectra for Three Samples of Tyrosine and Tryptophan

Output 56.3.2: Amount of Training Set Variation Explained

Output 56.3.3: Test Set Validation for the Number of PLS Factors

Output 56.3.4: Predictor Loadings Across Frequencies

Chapter 57: The POWER Procedure

Output 57.1.1: Sample Sizes for One-Way ANOVA Contrasts

Output 57.1.2: Plot of Sample Size versus Power for One-Way ANOVA Contrasts

Output 57.1.3: Plot of Power versus Sample Size for One-Way ANOVA Contrasts

Output 57.2.1: Approximate Sample Size for z Test of a Proportion

Output 57.2.2: Approximate Sample Size for z Test with Continuity Correction

Output 57.2.3: Plot of Power versus Sample Size for Exact Binomial Test

Output 57.2.4: Plot for Assessing Sensitivity to True Proportion Value

Output 57.2.5: Numerical Content of Plot

Output 57.2.6: Plot of Power versus Sample Size for Another 1-Sided Test

Output 57.2.7: Plot of Power versus Sample Size for a 2-Sided Test

Output 57.3.1: Power for Paired t Analysis of Crossover Design

Output 57.3.2: Plot of Power versus Sample Size for Paired t Analysis of Crossover Design

Output 57.3.3: Power for Paired Equivalence Test for Crossover Design

Output 57.4.1: Power for Noninferiority Test of Ratio

Output 57.4.2: Plot of Power versus Mean Ratio for Noninferiority Test

Output 57.5.1: Power Analysis for Multiple Regression

Output 57.5.2: Plot of Power versus Sample Size for Multiple Regression

Output 57.5.3: Power Analysis for Fishers z Test

Output 57.5.4: Sample Size Determination for Fishers z Test

Output 57.6.1: Survival Curves

Output 57.6.2: Sample Size Determination for Log-Rank Test

Output 57.7.1: Sample Size Determination for Confidence Interval Precision

Output 57.7.2: Plot of Sample Size vs. Confidence Interval Half-Width

Output 57.8.1: Computed Sample Sizes

Output 57.8.2: Plot of Sample Size versus Power

Output 57.8.3: Plot of Power versus Sample Size using First Strategy

Output 57.8.4: Computed Sample Sizes

Output 57.8.5: Plot of Power versus Sample Size Using Second Strategy

Output 57.8.6: Plot of Sample Size versus Mean Difference

Output 57.8.7: Plot with Overlapping Points

Output 57.8.8: Sample Sizes

Output 57.8.9: Plot with Unequally Spaced Points

Output 57.8.10: Plot with Fractional Sample Sizes

Output 57.8.11: Plot with Simple Reference Lines on Y-Axis

Output 57.8.12: Plot with CROSSREF=YES Style Reference Lines from Y-Axis

Output 57.8.13: Plot with CROSSREF=YES Style Reference Lines from X-Axis

Output 57.8.14: Plot with Default VARY SettingsPanel 1 of 2

Output 57.8.15: Plot with Default VARY SettingsPanel 2 of 2

Output 57.8.16: Plot with Varying Color Instead of Panel

Output 57.8.17: Plot with Features Explicitly Linked to ParametersPanel 1 of 2

Output 57.8.18: Plot with Features Explicitly Linked to ParametersPanel 2 of 2

Output 57.8.19: Plot with a By-Feature Key Inside the Plotting Region

Output 57.8.20: Plot with a Numbered By-Curve Key

Output 57.8.21: Plot with a Nonnumbered By-Curve Key

Output 57.8.22: Plot with Directly Labeled Curves

Output 57.8.23: Plot with MARKERS=ANALYSIS

Output 57.8.24: Plot with MARKERS=NICE

Chapter 58: The PRINCOMP Procedure

Output 58.1.1: Plot of Raw Data

Output 58.1.2: Results of Principal Component Analysis

Output 58.1.3: Plot of Principal Components

Output 58.2.1: Results of Principal Component AnalysisPROC PRINCOMP

Output 58.2.2: OUT= Data Set Sorted by First Principal Component

Output 58.2.3: OUT= Data Set Sorted by Second Principal Component

Output 58.2.4: Plot of the First Two Principal Components

Output 58.2.5: Plot of the First and Third Principal Components

Output 58.3.1: Summary Statistics for Basketball Rankings Using PROC MEANS

Output 58.3.2: Principal Components Analysis of Basketball Rankings Using PROC PRINCOMP

Output 58.3.3: Basketball Rankings Using PROC PRINCOMP

Output 58.4.1: Eigenvalue Scatter Plot (Experimental)

Output 58.4.2: Component Scores Matrix Plot (Experimental)

Output 58.4.3: Component Pattern Plot (Experimental)

Output 58.4.4: Component Scores Plot1st versus 2nd (Experimental)

Output 58.4.5: Component Scores Plot1st versus 3rd (Experimental)

Output 58.4.6: Painted Components Scores Plot2nd versus 3rd, Painted by 1st (Experimental)

Chapter 59: The PRINQUAL Procedure

Output 59.1.1: Principal Component Analysis of Original Data

Output 59.1.2: Transformation of Automobile Preference Data

Output 59.1.3: Principal Components of Transformed Data

Output 59.1.4: Preference Ratings for Automobiles Manufactured in 1980

Output 59.2.1: Multidimensional Preference Analysis (Experimental)

Output 59.3.1: Transformation of Basketball Team Rankings

Output 59.3.2: Alternative Approach for Analyzing Basketball Rankings

Output 59.3.3: Monotonic Transformation for Each News Service

Chapter 60: The PROBIT Procedure

Example 11.1: Plot of Observed and Fitted Probabilities

Example 11.2: Dosage Levels: PROC PROBIT

Example 11.3: Multilevel Response: PROC PROBIT

Output 60.2.2: Plot of Predicted Probilities for the Test Preparation Group

Output 60.2.3: Plot of Predicted Probabilities for the Standard Preparation Group

Output 60.3.1: Logistic Regression: PROC PROBIT

Output 60.4.1: Class Level Information

Output 60.4.2: Parameter Information

Output 60.4.3: Model Information

Output 60.4.4: Goodness-of-Fit Tests and Response-Covariate Profile

Output 60.4.5: Type III Tests

Output 60.4.6: Analysis of Parameter Estimates

Output 60.4.7: Estimated Covariance Matrix

Output 60.4.8: Estimated Correlation Matrix

Output 60.4.9: Probit Analysis on Dose

Output 60.4.10: Outest Data Set for Epidemiology Study

Output 60.4.11: Predicted Probability Plot

Output 60.4.12: Inverse Predicted Probability Plot

Output 60.4.13: Linear Predictor Plot

Output 60.4.14: Out2

Output 60.4.15: Out3

Output 60.4.16: Out4

Output 60.4.17: Goodness-of-Fit Table