Experimental Design and Analysis with Emphasis on Communicating What Has Been Done: I. A Comparison of Statistical Models Using General Linear Model Procedure of SAS

<div>The primary purpose of this study was to evaluate the use of an Association Rule General Analytic System (ARGAS) versus the General Linear Model (GLM) for hypothesis testing. Results indicate that the ARGAS provides an better alternative method for testing hypotheses when the assumptions of the GLM are violated. The ARGAS approach can be used with any experimental design to which the GLM can be applied. ARGAS is free of the usual assumptions of the GLM. A second purpose of the study was to illustrate how the ARGAS can be used for hypothesis testing with commonly used experimental designs.</div><div><br></div><div> </div>

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An Association Rule General Analytic System (ARGAS) for hypothesis testing and data mining applications

10.31124/advance.7336763 ◽

2018 ◽

Author(s):

Rick Parente

Keyword(s):

Data Mining ◽

Experimental Design ◽

Hypothesis Testing ◽

Linear Model ◽

Association Rule ◽

General Linear Model ◽

Experimental Designs ◽

General Linear ◽

Testing Hypotheses ◽

Alternative Method

<div>The primary purpose of this study was to evaluate the use of an Association Rule General Analytic System (ARGAS) versus the General Linear Model (GLM) for hypothesis testing. Results indicate that the ARGAS provides an better alternative method for testing hypotheses when the assumptions of the GLM are violated. The ARGAS approach can be used with any experimental design to which the GLM can be applied. ARGAS is free of the usual assumptions of the GLM. A second purpose of the study was to illustrate how the ARGAS can be used for hypothesis testing with commonly used experimental designs.</div><div><br></div><div> </div>

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A Comparison of a General Linear Model and the Ratio Estimator

International Journal of Statistics and Probability ◽

10.5539/ijsp.v9n3p54 ◽

2020 ◽

Vol 9 (3) ◽

pp. 54

Author(s):

Morteza Marzjarani

Keyword(s):

Data Analysis ◽

Statistical Model ◽

Linear Model ◽

Statistical Models ◽

General Linear Model ◽

Least Square ◽

General Linear ◽

Data Sets ◽

Ratio Estimator ◽

The Impact

In data analysis, selecting a proper statistical model is a challenging issue. Upon the selection, there are other important factors impacting the results. In this article, two statistical models, a General Linear Model (GLM) and the Ratio Estimator will be compared. Where applicable, some issues such as heteroscedasticity, outliers, etc. and the role they play in data analysis will be studied. For reducing the severity of heteroscedasticity, Weighted Least Square (WLS), Generalized Least Square (GLS), and Feasible Generalized Least Square (FGLS) will be deployed. Also, a revised version of FGLS is introduced. Since these issues are data dependent, shrimp effort data collected in the Gulf of Mexico for the years 2005 through 2018 will be used and it is shown that the revised FGLS reduces the impact of heteroscedasticity significantly compared to that of FGLS. The data sets will also be checked for the outliers and corrections are made (where applicable). It is concluded that these issues play a significant role in data analysis and must be taken seriously. Further, the two statistical models, that is, the GLM and the Ratio Estimator are compared.

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