Applied Statistics and the SAS Programming Language

ISBN-10: 0131465325
ISBN-13: 9780131465329
Edition: 5th 2006
List price: $117.80
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Description: Suitable for use by departments ranging from statistics and Engineering to Psychology and Education when the objective of the course is to learn to use the SAS programming language to perform statistical analysis. As the SAS programming language  More...

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Book details

List price: $117.80
Edition: 5th
Copyright year: 2006
Publisher: Prentice Hall PTR
Publication date: 3/30/2005
Binding: Paperback
Pages: 592
Size: 7.00" wide x 9.25" long x 1.00" tall
Weight: 2.244

Suitable for use by departments ranging from statistics and Engineering to Psychology and Education when the objective of the course is to learn to use the SAS programming language to perform statistical analysis. As the SAS programming language continues to evolve, this new edition follows suit with up-to-date coverage of this combination statistical package, database management system, and high-level programming language. Using examples from business, medicine, education, psychology, and other disciplines,Applied Statistics and the SAS Programming Language is an invaluable resource for both students and applied researchers, giving them the capacity to perform statistical analyses with SAS without wading through pages of technical documentation.

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

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