How to Do Non-Parametric Analysis Using SAS

How to Do Non How to Do Non- - Parametric Parametric Analysis Using Analysis Using SAS SAS An Academic Assignment Help Guide

Introduction to Non Introduction to Non- - Parametric Analysis Parametric Analysis • Non Non- -parametric methods are statistical techniques that do parametric methods are statistical techniques that do not assume a specific distribution for the data. not assume a specific distribution for the data. • These methods are useful when data doesn't meet the These methods are useful when data doesn't meet the assumptions of parametric tests (e.g., normal distribution). assumptions of parametric tests (e.g., normal distribution). • Examples include the Wilcoxon Rank Examples include the Wilcoxon Rank- -Sum Test, Kruskal Wallis Test, and Spearman's Rank Correlation. Wallis Test, and Spearman's Rank Correlation. Sum Test, Kruskal- -

Why Use SAS for Non Why Use SAS for Non- - Parametric Analysis? Parametric Analysis? • SAS is a powerful statistical software that offers a wide SAS is a powerful statistical software that offers a wide range of non range of non- -parametric tests. parametric tests. • It provides flexibility and efficiency in handling large datasets It provides flexibility and efficiency in handling large datasets and performing complex analyses. and performing complex analyses. • SAS’s robust output and graphical capabilities make SAS’s robust output and graphical capabilities make interpretation easier. interpretation easier.

Types of Non Types of Non- - Parametric Parametric Tests in SAS Tests in SAS Wilcoxon Rank Wilcoxon Rank- -Sum Test Kruskal Kruskal- -Wallis Test Wallis Test Sign Test Sign Test Friedman Test Friedman Test Spearman’s Rank Correlation Spearman’s Rank Correlation Sum Test • • • • •

Step Step- -by Guide Guide by- -Step Step

Wilcoxon Rank Wilcoxon Rank- -Sum Test Sum Test The Wilcoxon Rank The Wilcoxon Rank- -Sum Test is used to compare two independent samples. Sum Test is used to compare two independent samples. SAS Code: proc npar1way data=dataset_name wilcoxon; class group_variable; var test_variable; run;

Kruskal Kruskal- -Wallis Test Wallis Test The Kruskal The Kruskal- -Wallis Test is an extension of the Wilcoxon Rank Wallis Test is an extension of the Wilcoxon Rank- -Sum Test for more than two groups. Sum Test for more than two groups. SAS Code: proc npar1way data=dataset_name wilcoxon dscf; class group_variable; var test_variable; run;

Sign Test Sign Test The Sign Test is used to test for the median of a single sample or the differences between paired The Sign Test is used to test for the median of a single sample or the differences between paired samples. samples. SAS Code: proc freq data=dataset_name; tables test_variable / binomial; where condition; run;

Friedman Test Friedman Test The Friedman Test is a non The Friedman Test is a non- -parametric test for detecting differences in treatments across multiple parametric test for detecting differences in treatments across multiple test attempts. test attempts. SAS Code: proc freq data=dataset_name; tables test_variable; exact binomial; run;

Spearman’s Rank Correlation Test Spearman’s Rank Correlation Test Spearman’s Rank Correlation measures the strength and direction of association between two Spearman’s Rank Correlation measures the strength and direction of association between two ranked variables. ranked variables. SAS Code: proc freq data=dataset_name; tables test_variable; exact binomial; run;

Handling Ties in Non Handling Ties in Non- -Parametric Tests Parametric Tests SAS handles ties by assigning average ranks to tied values. SAS handles ties by assigning average ranks to tied values. • This can affect the interpretation of test statistics, so it’s important to be aware of how This can affect the interpretation of test statistics, so it’s important to be aware of how ties are treated. ties are treated. •

Data Preparation for Non Data Preparation for Non- -Parametric Tests Tests Parametric Proper data preparation is crucial for accurate results in non Proper data preparation is crucial for accurate results in non- -parametric tests. parametric tests. • This includes ensuring data is clean, handling missing values, and coding categorical This includes ensuring data is clean, handling missing values, and coding categorical variables appropriately. variables appropriately. •

Interpreting SAS Output for Non Interpreting SAS Output for Non- - Parametric Tests Parametric Tests Understanding SAS output is key to interpreting non Understanding SAS output is key to interpreting non- -parametric test results. parametric test results. • Focus on key metrics such as p Focus on key metrics such as p- -values, test statistics, and confidence intervals. values, test statistics, and confidence intervals. •

Customizing Output in SAS Customizing Output in SAS SAS allows for customization of output to enhance clarity and presentation. SAS allows for customization of output to enhance clarity and presentation. SAS Code: ods select Plots Statistics; proc npar1way data=dataset_name wilcoxon; class group_variable; var test_variable; run;

Common Mistakes and Common Mistakes and Troubleshooting Troubleshooting • Common errors include incorrect data formatting, Common errors include incorrect data formatting, misinterpretation of results, and overlooking assumptions. misinterpretation of results, and overlooking assumptions. • Always validate your results by checking SAS output and Always validate your results by checking SAS output and reviewing assumptions. reviewing assumptions.

Advantages and Disadvantages of Using Advantages and Disadvantages of Using SAS SAS Advantages: Advantages: Disadvantages: Disadvantages: Robust statistical tools Robust statistical tools Steep learning curve Steep learning curve • • flexible data handling flexible data handling Cost Cost • • excellent graphical capabilities. excellent graphical capabilities. complexity in customizing outputs. complexity in customizing outputs. • •

Real Real- -World Applications of Non World Applications of Non- - Parametric Analysis Parametric Analysis Non Non- -parametric analysis in SAS is widely used in fields such as medicine, finance, and parametric analysis in SAS is widely used in fields such as medicine, finance, and social sciences. social sciences. • Examples include analyzing clinical trial data, assessing financial market trends, and Examples include analyzing clinical trial data, assessing financial market trends, and social survey analysis. social survey analysis. •

Challenges in Conducting Non Challenges in Conducting Non- - Parametric Analysis in SAS Parametric Analysis in SAS Challenges include complexity in data preparation, understanding SAS syntax, and Challenges include complexity in data preparation, understanding SAS syntax, and interpreting outputs. interpreting outputs. • These challenges can be mitigated with proper guidance and support. These challenges can be mitigated with proper guidance and support. •

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How to Do Non-Parametric Analysis Using SAS