Step-By-Step Basic Statistics Using SAS Student Guide

Name: Step-By-Step Basic Statistics Using SAS Student Guide
Price: 107.79 USD
Availability: InStock
ISBN: 9781590471487

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