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Applied Statistics and the SAS Programming Language

Applied Statistics and the SAS Programming Language - 4th edition

Applied Statistics and the SAS Programming Language - 4th edition

ISBN13: 9780137436422

ISBN10: 0137436424

Applied Statistics and the SAS Programming Language by Ronald P. Cody and Jeffrey K. Smith - ISBN 9780137436422
Edition: 4TH 97
Copyright: 1997
Publisher: Prentice Hall, Inc.
Published: 1997
International: No
Applied Statistics and the SAS Programming Language by Ronald P. Cody and Jeffrey K. Smith - ISBN 9780137436422

ISBN13: 9780137436422

ISBN10: 0137436424

Edition: 4TH 97


Applied Statistics and the SAS Programming Language is intended to provide the applied researcher with the capacity to perform statistical analyses with SAS software without wading through pages of technical documentation.

The researcher is provided with the necessary SAS statements to run programs for most of the commonly used statistics, explanations of the computer output, interpretations of results, and examples of how to construct tables and write up results for reports and journal articles.

Examples have been selected from business, medicine, education, psychology, and other disciplines.

Table of Contents

Table of Contents


1. A SAS Tutorial.

Introduction. Computing with SAS Software: An Illustrative Example. Enhancing the Program. SAS Procedures. Overview of the SAS Data Step. Syntax of SAS Procedures. Comment Statements. References.

2. Describing Data.

Introduction. Describing Data. More Descriptive Statistics. Descriptive Statistics Broken Down by Subgroups. Frequency Distributions. Bar Graphs. Plotting Data. Creating Summary Data Sets with PROC MEANS an PROC UNIVARIATE. Outputting Statistics Other than Means. Creating a Summary Data Set to Contain a Median.

3. Analyzing Categorical Data.

Introduction. Questionnaire Design and Analysis. Adding Variable Labels. Adding "Value Labels" (Formats). Recoding Data. Using a Format to Recode a Variable. Two-way Frequency Tables. A Shortcut Way of Requesting Multiple Tables. Computing Chi-square From Frequency Counts. A Useful Program for Multiple Chi-square Tables. McNemar's Test for Paired Data. Odds Ratios. Relative Risk. Chi-square Test for Trend. Mantel-Haenszel Chi-square for Stratified Tables and Meta Analysis. "Check all that Apply" Questions.

4. Working with Date and Longitudinal Data.

Introduction Processing Date Variables. Longitudinal Data. Most Recent (or Last) Visit per Patient. Computing Frequencies on Longitudinal Data Sets.

5. Correlation and Regression.

Introduction. Correlation. Significance of a Correlation Coefficient. How to Interpret a Correlation Coefficient. Partial Correlations. Linear Regression. Partitioning the Total Sum of Squares. Plotting the Points on the Regression Line. Plotting Residuals and Confidence Limits. Adding a Quadratic Term to the Regression Equation. Transforming Data. Computing Within-subject Slopes.

6. T-Tests and Nonparametric Comparisons.

Introduction. T-Test: Testing Differences Between Two Means. Random Assignment of Subjects. Two Independent Samples: Distribution Free Tests. One-Tailed versus Two-Tailed Tests. Paired T-Tests (Related Samples).

7. Analysis of Variance.

Introduction. One-Way Analysis of Variance. Computing Contrasts. Analysis of Variance: Two Independent Variables. Interpreting Significant Interactions. N-Way Factorial Designs. Unbalanced Designs: PROC GLM. Analysis of Covariance.

8. Repeated Measures Designs.

Introduction. One-Factor Experiments. Using the REPEATED Statement of PROC ANOVA. Two-Factor Experiments with a Repeated Measure on One Factor. Two-Factor Experiments with Repeated Measures on Both Factors. Three-Factor Experiments with a Repeated Measure on the Last Factor. Three-Factor Experiments with Repeated Measures on Two Factors.

9. Multiple Regression Analysis.

Introduction. Designed Regression. Nonexperimental Regression. Stepwise Regressions. Creating and Using Dummy Variables. Logistic Regression.

10. Factor Analysis.

Introduction. Types of Factor Analysis. Principle Components Analysis. Oblique Rotations. Using Communalities Other than One. How to Reverse Item Scores.

11. Psychometrics.

Introduction. Using SAS Software to Score a Test. Generalizing the Program for a Variable Number of Questions. Creating a Better Looking Table Using PROC TABULATE. A Complete Test Scoring and Item Analysis Program. Test Reliability. Interrater Reliability.


12. The SAS INPUT Statement.

Introduction. List Directed Input: Data Values Separated By Spaces. Reading Comma Delimited Data. Using INFORMATS with List Directed Data. Column Input. Pointers and Informats. Reading More than One Line per Subject. Changing the Order and Reading a Column More Than Once. Informat Lists. "Holding the Line" - Single and Double Trailing @'s. Suppressing the Error Messages for Invalid Data. Reading "Unstructured" Data.

13. External Files: Reading and Writing Raw and System Files.

Introduction. Data in the Program Itself. Reading ASCII Data from and External File. INFILE Options. Writing ASCII or "Raw Data" to an External File. Creating a Permanent SAS Data Set. Reading Permanent SAS Data Sets. How to Determine the Contents of a SAS Data Set. Permanent SAS Data Sets with Formats. Working with Large Data Sets.

14. Data Set Subsetting, Concatenating, Merging, and Updating.

Introduction. Subsetting. Combining Similar Data from Multiple SAS Data Sets. Combining Different Data from Multiple SAS Data Sets. Table Look Up. Updating a Master Data Set from an Update Data Set.

15. Working with Arrays.

Introduction. Substituting One Value for Another for a Series of Variables. Extending Example 1 to Convert All Numeric Values o 999 to Missing. Converting the Value of N/A (not applicable) to a Character Missing Value. Converting Heights and Weights from English to Metric Units. Temporary Arrays. Using a Temporary Array to Score a Test. Specifying Array Bounds. Temporary Arrays and Array Bounds. Implicitly Subscripted Arrays.

16. Restructuring SAS Data Sets Using Arrays.

Introduction. Creating a New Data Set With Several Observations per Subject from a Data Set with One Observation per Subject. Another Example of Creating Multiple Observations from a Single Observation. Going from One Observation per Subject to Many Observations per Subject Using Multidimensional Arrays. Creating a Data Set with One Observation per Subject from a Data Set with Multiple Observations per Subject. Creating a Data Set with One Observation per Subject from a Data Set with Multiple Observations per Subject Using a Multidimensional Array.

17. A Review of SAS Functions - Part I (Functions other than Character Functions).

Introduction. Arithmetic and Mathematical Functions. Random Number Functions. Time and Date Functions. The INPUT and PUT Functions: Converting Numeric to Character and Character to Numeric variables. The LAG and DIF Functions.

18. A Review of SAS Functions - Part II (Character Functions).

Introduction. How Lengths of Character Variables are Set in a SAS DATA Step. Working with Blanks. How to Remove Characters from a String. Character Data Verification. Substring Example. Using the SUBSTR Function on the Left-Hand Side of the Equal Sign. Doing the Previous Example Another Way. Unpacking a String. Parsing a String. Locating the Position of One String Within Another String. Changing Lower Case to Upper Case and Vice Versa. Substituting One Character for Another. Substituting One Word for Another in a String. Concatenating (Joining) Strings. Soundex Conversion.

19. Selected Programming Examples.

Introduction. Expressing Data Values as a Percent of the Grand Mean. Expressing a Value as a Percent of a Group Mean. Plotting Means with Error Bars. Using a Macro Variable to Save Coding Time. Computing Relative Frequencies. Computing Combined Frequencies on Different Variables. Computing a Moving Average. Sorting Within an Observation. Computing Coefficient Alphs (or KR-20) in a DATA Step.

20. Syntax Examples.


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