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SAS Statistics by Example

Book Description

Offers a cookbook approach for doing statistics with SAS. Structured specifically around the most commonly used statistical tasks or techniques--for example, comparing two means, ANOVA, and regression--it provides an easy-to-follow, how-to approach to statistical analysis not found in other books.

Table of Contents

  1. Cover Page
  2. Title Page
  3. Copyright
  4. Contents
  5. List of Programs
  6. Acknowledgments
  7. Chapter 1 An Introduction to SAS
    1. Introduction
    2. What is SAS
    3. Statistical Tasks Performed by SAS
    4. The Structure of SAS Programs
    5. SAS Data Sets
    6. SAS Display Manager
    7. Excel Workbooks
    8. Variable Types in SAS Data Sets
    9. Temporary versus Permanent SAS Data Sets
    10. Creating a SAS Data Set from Raw Data
      1. Data Values Separated by Delimiters
      2. Reading CSV Files
      3. Data Values in Fixed Columns
    11. Excel Files with Invalid SAS Variable Names
    12. Other Sources of Data
    13. Conclusions
  8. Chapter 2 Descriptive Statistics – Continuous Variables
    1. Introduction
    2. Computing Descriptive Statistics Using PROC MEANS
    3. Descriptive Statistics Broken Down by a Classification Variable
    4. Computing a 95% Confidence Interval and the Standard Error
    5. Producing Descriptive Statistics, Histograms, and Probability Plots
    6. Changing the Midpoint Values on the Histogram
    7. Generating a Variety of Graphical Displays of Your Data
    8. Displaying Multiple Box Plots for Each Value of a Categorical Variable
    9. Conclusions
  9. Chapter 3 Descriptive Statistics – Categorical Variables
    1. Introduction
    2. Computing Frequency Counts and Percentages
    3. Computing Frequencies on a Continuous Variable
    4. Using Formats to Group Observations
    5. Histograms and Bar Charts
    6. Creating a Bar Chart Using PROC SGPLOT
    7. Using ODS to Send Output to Alternate Destinations
    8. Creating a Cross-Tabulation Table
    9. Changing the Order of Values in a Frequency Table
    10. Conclusions
  10. Chapter 4 Descriptive Statistics – Bivariate Associations
    1. Introduction
    2. Producing a Simple Scatter Plot Using PROG GPLOT
    3. Producing a Scatter Plot Using PROC SGPLOT
    4. Creating Multiple Scatter Plots on a Single Page Using PROC SGSCATTER
      1. The PLOT Statement: Generating More than One Plot on a Page
      2. The COMPARE Statement: Plotting One Variable versus Several Variables on a Single Page
      3. The MATRIX Statement: Creating a Scatter Plot Matrix
    5. Conclusions
  11. Chapter 5 Inferential Statistics – One-Sample Tests
    1. Introduction
    2. Conducting a One-Sample t-test Using PROC TTEST
    3. Running PROC TTEST with ODS Graphics Turned On
    4. Conducting a One-Sample t-test Using PROC UNIVARIATE
    5. Testing Whether a Distribution is Normally Distributed
    6. Tests for Other Distributions
    7. Conclusions
  12. Chapter 6 Inferential Statistics – Two-Sample Tests
    1. Introduction
    2. Conducting a Two-Sample t-test
    3. Testing the Assumptions for a t-test
    4. Customizing the Output from ODS Statistical Graphics
    5. Conducting a Paired t-test
    6. Assumption Violations
    7. Conclusions
  13. Chapter 7 Inferential Statistics – Comparing More than Two Means
    1. Introduction
    2. A Simple One-way Design
    3. Conducting Multiple Comparison Tests
    4. Using ODS Graphics to Produce a Diffogram
    5. Two-way Factorial Designs
    6. Analyzing Factorial Models with Significant Interactions
    7. Analyzing a Randomized Block Design
    8. Conclusions
  14. Chapter 8 Correlation and Regression
    1. Introduction
    2. Producing Pearson Correlations
    3. Generating a Correlation Matrix
    4. Creating HTML Output with Data Tips
    5. Generating Spearman Nonparametric Correlations
    6. Running a Simple Linear Regression Model
    7. Using ODS Statistical Graphics to Investigate Influential Observations
    8. Using the Regression Equation to Do Prediction
    9. A More Efficient Way to Compute Predicted Values
    10. Conclusions
  15. Chapter 9 Multiple Regression
    1. Introduction
    2. Fitting Multiple Regression Models
    3. Running All Possible Regressions with n Variables
    4. Producing Separate Plots Instead of a Panel
    5. Choosing the Best Model (Cp and Hocking's Criteria)
    6. Forward, Backward, and Stepwise Selection Methods
    7. Influential Observations in Multiple Regression Models
    8. Conclusions
  16. Chapter 10 Categorical Data
    1. Introduction
    2. Comparing Proportions
    3. Rearranging Rows and Columns in a Table
    4. Tables with Expected Values Less Than 5 (Fisher's Exact Test)
    5. Computing Chi-Square from Frequency Data
    6. Using a Chi-Square Macro
    7. A Short-Cut Method for Requesting Multiple Tables
    8. Computing Coefficient Kappa—A Test of Agreement
    9. Computing Tests for Trends
    10. Computing Chi-Square for One-Way Tables
    11. Conclusions
  17. Chapter 11 Binary Logistic Regression
    1. Introduction
    2. Running a Logistic Regression Model with One Categorical Predictor Variable
    3. Running a Logistic Regression Model with One Continuous Predictor Variable
    4. Using a Format to Create a Categorical Variable from a Continuous Variable
    5. Using a Combination of Categorical and Continuous Variables in a Logistic Regression Model
    6. Running a Logistic Regression with Interactions
    7. Conclusions
  18. Chapter 12 Nonparametric Tests
    1. Introduction
    2. Performing a Wilcoxon Rank-Sum Test
    3. Performing a Wilcoxon Signed-Rank Test (for Paired Data)
    4. Performing a Kruskal-Wallis One-Way ANOVA
    5. Comparing Spread: The Ansari-Bradley Test
    6. Converting Data Values into Ranks
    7. Using PROC RANK to Group Your Data Values
    8. Conclusions
  19. Chapter 13 Power and Sample Size
    1. Introduction
    2. Computing the Sample Size for an Unpaired t-Test
    3. Computing the Power of an Unpaired t-Test
    4. Computing Sample Size for an ANOVA Design
    5. Computing Sample Sizes (or Power) for a Difference in Two Proportions
    6. Using the SAS Power and Sample Size Interactive Application
    7. Conclusions
  20. Chapter 14 Selecting Random Samples
    1. Introduction
    2. Taking a Simple Random Sample
    3. Taking a Random Sample with Replacement
    4. Creating Replicate Samples using PROC SURVEYSELECT
    5. Conclusions
  21. Index
    1. A
    2. B
    3. C
    4. D
    5. E
    6. F
    7. G
    8. H
    9. I
    10. J
    11. K
    12. L
    13. M
    14. N
    15. O
    16. P
    17. Q
    18. R
    19. S
    20. T
    21. U
    22. V
    23. W
    24. Y
    25. Symbols and Numbers
  22. References
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