On the Best Predictive General Linear Model for Data Analysis: A Tolerance Region Algorithm for Prediction

2013 ◽  
Vol 13 (4) ◽  
pp. 513-524 ◽  
Author(s):  
C.P. Kitsos ◽  
Vasilios Zarikas
Keyword(s):  
Data Analysis ◽  
Linear Model ◽  
General Linear Model ◽  
General Linear ◽  
Tolerance Region

scholarly journals The Effectiviness of Promotional Benefit Towards Buying Intention Moderated By The Brain’s Tendency of Consumers

2017 ◽  
Vol 20 (1) ◽  
pp. 89
Author(s):  
Evan Stiawan ◽  
Tantri Yanuar Rahmat Syah
Keyword(s):  
Data Analysis ◽  
Mobile Phone ◽  
Linear Model ◽  
General Linear Model ◽  
General Linear ◽  
Analysis Method ◽  
Price Discount ◽  
Left Brain ◽  
Right Brain ◽  
Left And Right

This research’s goal is to measure the influence of promotional benefit towards buying intention moderated the brain’s tendency of consumers. The object of this research is the consumers whom ever bought a mobile phone. The purpose of this research is to find out the consumer’s buying intention when price discount is higher than premium, the influence of price discount and premium toward buying intention which is moderated by the tendency of consumer’s left and right brain. The data analysis method used is ANOVA One Way and General Linear model (GLM). The result of the research shows that premium is more positively evaluated than price discount which is means the consumer tends to re-buy when premium promotion offered is higher that price discount. The group of consumers that get price discount and right brain tendency tend to have a higher buying intention than the group of consumers that get price discount and left brain tendency, also for the group of consumers that get price discount and right brain tendency tend to have a higher buying intention than the group of consumers that get premium and right brain tendency.

scholarly journals A Comparison of a General Linear Model and the Ratio Estimator

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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