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Applied Statistics and the SAS Programming Language 4th ed

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Item specifics

Condition
Very good
A book that does not look new and has been read but is in excellent condition. No obvious damage to the cover, with the dust jacket (if applicable) included for hard covers. No missing or damaged pages, no creases or tears, and no underlining/highlighting of text or writing in the margins. May be very minimal identifying marks on the inside cover. Very minimal wear and tear. See the seller’s listing for full details and description of any imperfections. See all condition definitionsopens in a new window or tab
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“Signs of light use. No visible markings in text.”
Publication Name
Prentice Hall
Type
Textbook
Author
Ronald P. Cody, Jeffrey K. Smith
Subject
Statistics, SAS, Programming, statistical analysis, data analysis
ISBN
9780137436422

About this product

Product Identifiers

Publisher
Prentice Hall PTR
ISBN-10
0137436424
ISBN-13
9780137436422
eBay Product ID (ePID)
790094

Product Key Features

Number of Pages
445 Pages
Publication Name
Applied Statistics and the Sas Programming Language
Language
English
Publication Year
1997
Subject
Mathematical & Statistical Software, Probability & Statistics / General, Statistics
Type
Textbook
Author
Ronald P. Cody, Jeffrey K. Smith
Subject Area
Mathematics, Computers, Business & Economics
Format
Trade Paperback

Dimensions

Item Height
0.6 in
Item Weight
22.9 Oz
Item Length
9.2 in
Item Width
6.9 in

Additional Product Features

Edition Number
4
Intended Audience
College Audience
LCCN
97-001737
Dewey Edition
20
Dewey Decimal
519.5/028/55369
Table Of Content
I. APPLIED STATISTICS AND SAS SOFTWARE. 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. II. SAS PROGRAMMING. 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 U
Synopsis
For students and researchers with no previous knowledge of it, explains the language and how to use it to run programs for the mostly commonly used statistics. Also explains the computer output, interpreting the results, constructing tables, and writing up results for reports and journal articles. E, 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. 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.
LC Classification Number
QA276.4.C53 1997

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