Читать книгу SAS Statistics by Example - Ron Cody EdD - Страница 4
ОглавлениеContents
Chapter 1 An Introduction to SAS
Statistical Tasks Performed by SAS
Variable Types in SAS Data Sets
Temporary versus Permanent SAS Data Sets
Creating a SAS Data Set from Raw Data
Data Values Separated by Delimiters
Excel Files with Invalid SAS Variable Names
Chapter 2 Descriptive Statistics – Continuous Variables
Computing Descriptive Statistics Using PROC MEANS
Descriptive Statistics Broken Down by a Classification Variable
Computing a 95% Confidence Interval and the Standard Error
Producing Descriptive Statistics, Histograms, and Probability Plots
Changing the Midpoint Values on the Histogram
Generating a Variety of Graphical Displays of Your Data
Displaying Multiple Box Plots for Each Value of a Categorical Variable
Chapter 3 Descriptive Statistics – Categorical Variables
Introduction
Computing Frequency Counts and Percentages
Computing Frequencies on a Continuous Variable
Using Formats to Group Observations
Histograms and Bar Charts
Creating a Bar Chart Using PROC SGPLOT
Using ODS to Send Output to Alternate Destinations
Creating a Cross-Tabulation Table
Changing the Order of Values in a Frequency Table
Conclusions
Chapter 4 Descriptive Statistics – Bivariate Associations
Introduction
Producing a Simple Scatter Plot Using PROG GPLOT
Producing a Scatter Plot Using PROC SGPLOT
Creating Multiple Scatter Plots on a Single Page Using PROC SGSCATTER
Conclusions
Chapter 5 Inferential Statistics – One-Sample Tests
Introduction
Conducting a One-Sample t-test Using PROC TTEST
Running PROC TTEST with ODS Graphics Turned On
Conducting a One-Sample t-test Using PROC UNIVARIATE
Testing Whether a Distribution is Normally Distributed
Tests for Other Distributions
Conclusions
Chapter 6 Inferential Statistics – Two-Sample Tests
Introduction
Conducting a Two-Sample t-test
Testing the Assumptions for a t-test
Customizing the Output from ODS Statistical Graphics
Conducting a Paired t-test
Assumption Violations
Conclusions
Chapter 7 Inferential Statistics – Comparing More than Two Means
Introduction
A Simple One-way Design
Conducting Multiple Comparison Tests
Using ODS Graphics to Produce a Diffogram
Two-way Factorial Designs
Analyzing Factorial Models with Significant Interactions
Analyzing a Randomized Block Design
Conclusions
Chapter 8 Correlation and Regression
Introduction
Producing Pearson Correlations
Generating a Correlation Matrix
Creating HTML Output with Data Tips
Generating Spearman Nonparametric Correlations
Running a Simple Linear Regression Model
Using ODS Statistical Graphics to Investigate Influential Observations
Using the Regression Equation to Do Prediction
A More Efficient Way to Compute Predicted Values
Conclusions
Introduction
Fitting Multiple Regression Models
Running All Possible Regressions with n Variables
Producing Separate Plots Instead of a Panel
Choosing the Best Model (Cp and Hocking’s Criteria)
Forward, Backward, and Stepwise Selection Methods
Forcing Selected Variables into a Model
Creating Dummy (Design) Variables for Regression
Detecting Collinearity
Influential Observations in Multiple Regression Models
Conclusions
Introduction
Comparing Proportions
Rearranging Rows and Columns in a Table
Tables with Expected Values Less Than 5 (Fisher’s Exact Test)
Computing Chi-Square from Frequency Data
Using a Chi-Square Macro
A Short-Cut Method for Requesting Multiple Tables
Computing Coefficient Kappa—A Test of Agreement
Computing Tests for Trends
Computing Chi-Square for One-Way Tables
Conclusions
Chapter 11 Binary Logistic Regression
Introduction
Running a Logistic Regression Model with One Categorical Predictor Variable
Running a Logistic Regression Model with One Continuous Predictor Variable
Using a Format to Create a Categorical Variable from a Continuous Variable
Using a Combination of Categorical and Continuous Variables in a Logistic Regression Model
Running a Logistic Regression with Interactions
Conclusions
Chapter 12 Nonparametric Tests
Introduction
Performing a Wilcoxon Rank-Sum Test
Performing a Wilcoxon Signed-Rank Test (for Paired Data)
Performing a Kruskal-Wallis One-Way ANOVA
Comparing Spread: The Ansari-Bradley Test
Converting Data Values into Ranks
Using PROC RANK to Group Your Data Values
Conclusions
Chapter 13 Power and Sample Size
Introduction
Computing the Sample Size for an Unpaired t-Test
Computing the Power of an Unpaired t-Test
Computing Sample Size for an ANOVA Design
Computing Sample Sizes (or Power) for a Difference in Two Proportions
Using the SAS Power and Sample Size Interactive Application
Conclusions
Chapter 14 Selecting Random Samples
Introduction
Taking a Simple Random Sample
Taking a Random Sample with Replacement
Creating Replicate Samples using PROC SURVEYSELECT
Conclusions
