Step-By-Step Basic Statistics Using SAS Student Guide

ISBN-10: 1590471482
ISBN-13: 9781590471487
Edition: 2003
Authors: Larry Hatcher
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Book details

List price: $79.95
Copyright year: 2003
Publisher: SAS Institute
Publication date: 4/1/2003
Binding: Paperback
Pages: 710
Size: 8.50" wide x 11.20" long x 1.25" tall
Weight: 3.586
Language: English

Acknowledgments
Using This Student Guide
Introduction
Introduction to the SAS System
Contents of This Student Guide
Conclusion
Terms and Concepts Used in This Guide
Introduction
Research Hypotheses and Statistical Hypotheses
Data, Variables, Values, and Observations
Classifying Variables According to Their Scales of Measurement
Classifying Variables According to the Number of Values They Display
Basic Approaches to Research
Using Type-of-Variable Figures to Represent Dependent and Independent Variables
The Three Types of SAS Files
Conclusion
Tutorial: Writing and Submitting SAS Programs
Introduction
Basics of Using the SAS Windowing Environment
Opening and Editing an Existing SAS Program
Submitting a Program with an Error
Practicing What You Have Learned
Summary of Steps for Frequently Performed Activities
Controlling the Size of the Output Page with the Options Statement
For More Information
Conclusion
Data Input
Introduction
Creating a Simple SAS Data Set
A More Complex Data Set
Using PROC Means and PROC FREQ to Identify Obvious Problems with the Data Set
Using PROC Print to Create a Printout of Raw Data
The Complete SAS Program
Conclusion
Creating Frequency Tables
Introduction
A Political Donation Study
Using PROC FREQ to Create a Frequency Table
Examples of Questions That Can Be Answered by Interpreting a Frequency Table
Conclusion
Creating Graphs
Introduction
Reprise of Example 5.1: the Political Donation Study
Using PROC Chart to Create a Frequency Bar Chart
Using PROC Chart to Plot Means for Subgroups
Conclusion
Measures of Central Tendency and Variability
Introduction
Reprise of Example 5.1: The Political Donation Study
Measures of Central Tendency: The Mode, Median, and Mean
Interpreting a Stem-and-Leaf Plot Created by PROC Univariate
Using PROC Univariate to Determine the Shape of Distributions
Simple Measures of Variability: The Range, the Interquartile Range, and the Semi-Interquartile Range
More Complex Measures of Central Tendency: The Variance and Standard Deviation
Variance and Standard Deviation: Three Formulas
Using PROC Means to Compute the Variance and Standard Deviation
Conclusion
Creating and Modifying Variables and Data Sets
Introduction
An Achievement Motivation Study
Using PROC Print to Create a Printout of Raw Data
Where to Place Data Manipulation and Data Subsetting Statements
Basic Data Manipulation
Recoding a Reversed Item and Creating a New Variable for the Achievement Motivation Study
Using If-Then Control Statements
Data Subsetting
Combining a Large Number of Data Manipulation and Data Subsetting Statements in a Single Program
Conclusion
z Scores
Introduction
Comparing Mid-Term Test Scores for Two Courses
Converting a Single Raw-Score Variable into a z-Score Variable
Converting Two Raw-Score Variables into z-Score Variables
Standardizing Variables with PROC Standard
Conclusion
Bivariate Correlation
Introduction
Situations Appropriate for the Pearson Correlation Coefficient
Interpreting the Sign and Size of a Correlation Coefficient
Interpreting the Statistical Significance of a Correlation Coefficient
Problems with Using Correlations to Investigate Causal Relationships
Correlating Weight Loss with a Variety of Predictor Variables
Using PROC PLOT to Create a Scattergram
Using PROC CORR to Compute the Pearson Correlation between Two Variables
Using PROC CORR to Compute All Possible Correlations for a Group of Variables
Summarizing Results Involving a Nonsignificant Correlation
Using the VAR and WITH Statements to Suppress the Printing of Some Correlations
Computing the Spearman Rank-Order Correlation Coefficient for Ordinal-Level Variables
Some Options Available with PROC CORR
Problems with Seeking Significant Results
Conclusion
Bivariate Regression
Introduction
Choosing between the Terms Predictor Variable, Criterion Variable, Independent Variable, and Dependent Variable
Situations Appropriate for Bivariate Linear Regression
Predicting Weight Loss from a Variety of Predictor Variables
Using PROC REG: Example with a Significant Positive Regression Coefficient
Using PROC REG: Example with a Significant Negative Regression Coefficient
Using PROC REG: Example with a Nonsignificant Regression Coefficient
Conclusion
Single-Sample t Test
Introduction
Situations Appropriate for the Single-Sample t Test
Results Produced in a Single-Sample t Test
Assessing Spatial Recall in a Reading Comprehension Task (Significant Results)
One-Tailed Tests versus Two-Tailed Tests
An Illustration of Nonsignificant Results
Conclusion
Independent-Samples t Test
Introduction
Situations Appropriate for the Independent-Samples t Test
Results Produced in an Independent-Samples t Test
Observed Consequences for Modeled Aggression: Effects on Subsequent Subject Aggression (Significant Differences)
An Illustration of Results Showing Nonsignificant Differences
Conclusion
Paired-Samples t Test
Introduction
Situations Appropriate for the Paired-Samples t Test
Similarities between the Paired-Samples t Test and the Single-Sample t Test
Results Produced in a Paired-Samples t Test
Women's Responses to Emotional versus Sexual Infidelity
An Illustration of Results Showing Nonsignificant Differences
Conclusion
One-Way ANOVA with One Between-Subjects Factor
Introduction
Situations Appropriate for One-Way ANOVA with One Between-Subjects Factor
A Study Investigating Aggression
Treatment Effects, Multiple Comparison Procedures, and a New Index of Effect Size
Some Possible Results from a One-Way ANOVA
One-Way ANOVA Revealing a Significant Treatment Effect
One-Way ANOVA Revealing a Nonsignificant Treatment Effect
Conclusion
Factorial ANOVA with Two Between-Subjects Factors
Introduction
Situations Appropriate for Factorial ANOVA with Two Between-Subjects Factors
Using Factorial Designs in Research
A Different Study Investigating Aggression
Understanding Figures That Illustrate the Results of a Factorial ANOVA
Some Possible Results from a Factorial ANOVA
Example of a Factorial ANOVA Revealing Two Significant Main Effects and a Nonsignificant Interaction
Example of a Factorial ANOVA Revealing Nonsignificant Main Effects and a Nonsignificant Interaction
Example of a Factorial ANOVA Revealing a Significant Interaction
Using the LSMEANS Statement to Analyze Data from Unbalanced Designs
Learning More about Using SAS for Factorial ANOVA
Conclusion
Chi-Square Test of Independence
Introduction
Situations That Are Appropriate for the Chi-Square Test of Independence
Using Two-Way Classification Tables
Results Produced in a Chi-Square Test of Independence
A Study Investigating Computer Preferences
Computing Chi-Square from Raw Data versus Tabular Data
Example of a Chi-Square Test That Reveals a Significant Relationship
Example of a Chi-Square Test That Reveals a Nonsignificant Relationship
Computing Chi-Square from Raw Data
Conclusion
References
Index

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