SAS Statistics by Example. Ron Cody, EdD. Читать онлайн. Newlib. NEWLIB.NET

Автор: Ron Cody, EdD
Издательство: Ingram
Серия:
Жанр произведения: Программы
Год издания: 0
isbn: 9781612900124
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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

       Chapter 9 Multiple Regression

       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

       Chapter 10 Categorical Data

       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