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| Note: All chapters open with an Introduction | |
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| A SAS Tutorial Computing With SAS: An Illustrative Example | |
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| Enhancing the Program | |
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| SAS Procedures | |
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| Overview of the SAS DATA Step | |
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| Syntax of SAS Procedures | |
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| Comment Statements | |
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| References | |
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| Describing Data Describing Data | |
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| More Descriptive Statistics | |
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| Histograms, QQ Plots, and Probability Plots | |
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| Descriptive Statistics Broken Down by Subgroups | |
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| Frequency Distributions | |
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| Bar Graphs | |
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| Plotting Data | |
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| Analyzing Categorical Data Questionnaire Design and Analysis | |
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| Adding Variable Labels | |
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| Adding Value Labels (Formats). Recoding Data | |
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| Using a Format to Recode a Variable | |
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| Two-way Frequency Tables | |
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| A Short-cut Way to Request Multiple Tables | |
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| Computing Chi-square from Frequency Counts | |
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| A Useful Program for Multiple Chi-square Tables | |
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| A Useful Macro for Computing Chi-square from Frequency Counts | |
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| McNemars Test for Paired Data | |
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| Computing the Kappa Statistics (Coefficient of Agreement) | |
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| Odds Ratios | |
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| Relative Risk | |
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| Chi-square Test for Trend | |
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| Mantel-Haenszel Chi-square for Stratified Tables and Meta Analysis | |
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| Check All That Apply Questions | |
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| Working with Date and Longitudinal Data Processing Date Variables | |
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| Working with Two-digit | |
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| Year Values (The Y2K Problem | |
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| Longitudinal Data | |
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| Selecting the First or Last Visit per Patient | |
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| Computing Differences between Observations in a Longitudinal Data Set | |
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| Computing the Difference between the First and Last Observation for each Subject | |
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| Computing Frequencies on Longitudinal Data Sets | |
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| Creating Summary Data Sets with PROC MEANS or PROC SUMMARY | |
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| Outputting Statistics Other Than Means | |
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| Correlation and Simple Regression Correlation | |
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| Significance of a Correlation Coefficient | |
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| How to Interpret a Correlation Coefficient | |
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| Partial Correlations | |
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| Linear Regression | |
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| Partitioning the Total Sum of Squares | |
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| Producing a Scatter Plot and the Regression Line | |
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| Adding a Quadratic Term to the Regression Equation | |
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| Transforming Data | |
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| T-tests and Nonparametric Comparisons T-test: Testing Differences between Two Means | |
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| Random Assignment of Subjects | |
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| Two Independent Samples: Distribution Free Tests | |
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| One-tailed versus Two-tailed Tests | |
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| Paired T-tests (Related Samples) | |
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| Analysis of Variance One-way Analysis of Variance | |
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| Computing Contrasts | |
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| Analysis of Variance: Two Independent Variables | |
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| Interpreting Significant Interactions | |
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| N-way Factorial Designs | |
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| Unbalanced Designs: PROC GLM | |
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| Analysis of Covariance | |
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| Repeated Measures Designs One-factor Experiments | |
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| Using the REPEATED Statement of PROC ANOVA | |
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| Using PROC MIXED to Compute a Mixed (random effects) Model | |
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| Two-factor Experiments with a Repeated Measure on One Factor | |
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| Two-factor Experiments with Repeated Measures on Both Factors | |
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| Three-factor Experiments with a Repeated Measure on the Last Factor | |
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| Three-factor Experiments with Repeated Measures on Two Factors | |
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| Multiple Regression Analysis Designed Regression | |
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| Nonexperimental Regression | |
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| Stepwise and Other Variable Selection Methods | |
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| Creating and Using Dummy Variables | |
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| Using the Variance Inflation Factor to Look for Multicollinearity | |
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| Logistic Regression | |
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| Automatic Creation of Dummy Variables with PROC LOGISTIC | |
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| Factor Analysis Types of Factor Analysis | |
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| Principal Components Analysis | |
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| Oblique Rotations | |
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| Using Communalities Other Than One | |
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| How to Reverse Item Scores | |
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| Chapter | |