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Related Concept Videos

Statistical Analysis System (SAS)01:14

Statistical Analysis System (SAS)

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SAS, short for Statistical Analysis System, is a powerful data analysis, management, and visualization tool. Developed by the SAS Institute in the early 1970s, SAS has evolved into a comprehensive software suite used across various industries for statistical analysis, business intelligence, and predictive modeling.
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Analysis of Variance, or ANOVA, is a powerful statistical technique used to analyze parametric data, primarily in research and experimental studies. It's designed to compare the means of two or more groups, assisting researchers in identifying any significant differences between these group means. There are two main types of ANOVA based on the complexity of the analysis: one-way and two-way.
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One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
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One-way ANOVA analyzes more than three samples categorized by one factor. For example, it can compare the average mileage of sports bikes. Here, the data is categorized by one factor - the company. However, one-way ANOVA cannot be used to simultaneously compare the sample mean of three or more samples categorized by two factors. An example of two factors would be sports bikes from different companies driven in different terrains, such as a desert or snowy landscape. Here, two-way ANOVA is used...
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The two-way ANOVA is an extension of the one-way ANOVA. It is a statistical test performed on three or more samples categorized by two factors - a row factor and a column factor. Ronald Fischer mentioned it in 1925 in his book 'Statistical Methods for Researchers.'
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Using SAS Macros for Multiple Mediation Analysis in R.

Paige Fisher1, Wentao Cao1, Qingzhao Yu1

  • 1Louisiana State University Health Sciences Center, New Orleans, US.

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Summary
This summary is machine-generated.

This study introduces SAS macros for advanced mediation analysis, enabling complex variable relationships beyond generalized linear models (GLM). The new method integrates regression trees and splines for robust statistical inference.

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Confounding AnalysisMediation AnalysisR package mmaSAS Macros

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Area of Science:

  • Biostatistics
  • Statistical Modeling
  • Computational Statistics

Background:

  • Mediation analysis assesses third-variable effects on exposure-response relationships.
  • Generalized linear models (GLM) are standard but limited for nonlinearities and interactions.

Purpose of the Study:

  • To develop SAS macros for implementing advanced mediation analysis.
  • To extend the capabilities of the R package 'mma' to the SAS environment.

Main Methods:

  • Developed SAS macros to interface with the 'mma' R package.
  • Utilized generalized linear models, multiple additive regression trees, and smoothing splines.
  • Applied the method to perform mediation analysis within SAS.

Main Results:

  • Successfully created SAS macros for mediation analysis.
  • Enabled the use of advanced statistical techniques (regression trees, splines) in SAS.
  • Facilitated the analysis of complex exposure-response relationships.

Conclusions:

  • The developed SAS macros provide a powerful tool for mediation analysis.
  • This integration enhances statistical capabilities for researchers using SAS.
  • The method accommodates nonlinear relationships and interactions effectively.