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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 | |