Goodness of fit and variable selection in the fuzzy multiple linear regression

2006 ◽  
Vol 157 (19) ◽  
pp. 2627-2647 ◽  
Author(s):  
Pierpaolo D’Urso ◽  
Adriana Santoro
Keyword(s):  
Linear Regression ◽  
Variable Selection ◽  
Multiple Linear Regression ◽  
Goodness Of Fit

The Influence of Brand Extension Smartphone Samsung Galaxy Variable towards Brand Equity of Mother Brand of Samsung

2018 ◽  
Vol 3 (1) ◽  
pp. 06-15
Author(s):  
Sudaryanto Sudaryanto ◽  
Jery Courvisanos ◽  
Alif Puji Rahayu
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Brand Equity ◽  
Perceived Risk ◽  
Goodness Of Fit ◽  
Brand Extension ◽  
Jel Classification ◽  
Brand Extensions ◽  
Statistical Testing ◽  
Validity And Reliability

Objective - The purpose of this study is to identify the influence of similarity, reputation, perceived risk, and innovation as brand extensions of smartphones developed by Samsung, toward brand equity. Methodology/Technique - This study uses explanatory research. The population in this study consists of consumers of Samsung Galaxy mobiles for at least one month. Questionnaires were delivered to the respondents, after it had passed the validity and reliability tests. Following on from the the statistical testing, the data was analysed using a multiple linear regression. Then, the classical assumption test was conducted to determine the goodness of fit of the model. The data was collected using a questionnaire consisting of a closed statement, measured by a Likert Scale Findings - The results of this study show that similarity, reputation, perceived risk, and innovation as the variable dimensions have a significant effect on Brand Equity of Samsung Galaxy mobiles. Type of Paper: Empirical Keywords: Brand Extension; Brand Equity; Similarity; Reputation; Perceived Risk; Innovation; Explanatory Research. JEL Classification: M3, M30, M39.

scholarly journals Quantitative Structure–Activity Relationship and Molecular Docking Studies of Imidazolopyrimidine Amides as Potent Dipeptidyl Peptidase-4 (DPP4) Inhibitors

2019 ◽  
pp. 1-15 ◽  
Author(s):  
Leila Emami ◽  
Razieh Sabet ◽  
Amirhossein Sakhteman ◽  
Mehdi Khoshnevis Zade
Keyword(s):  
Genetic Algorithm ◽  
Linear Regression ◽  
Variable Selection ◽  
Multiple Linear Regression ◽  
Quantitative Structure Activity Relationship ◽  
Dipeptidyl Peptidase ◽  
Docking Studies ◽  
Quantitative Structure ◽  
Dipeptidyl Peptidase 4 ◽  
Structure Activity

Type 2 diabetes (T2DM) is a metabolic disorder disease and DPP-4 inhibitors are a class of oral hypoglycemic that blocks the dipeptidyl peptidase-4 (DPP-4) enzyme.  DPP-4 inhibitors reduce glucagon and blood glucose levels and don’t have side effects such as hypoglycemia or weight gain. In this paper, a series of imidazolopyrimidine amides analogues as DPP4 inhibitors were selected for quantitative structure-activity relationship (QSAR) analysis and docking studies. A collection of chemometric methods such as multiple linear regression (MLR), factor analysis-based multiple linear regression (FA-MLR), principal component regression (PCR), genetic algorithm for variable selection-MLR (GA-MLR) and partial least squared combined with genetic algorithm for variable selection (GA-PLS), were conducted to make relations between structural features and DPP4 inhibitory of a variety of imidazolopyrimidine amides derivatives. GA-PLS represented superior results with high statistical quality (R2 = 0.94 and Q2 = 0.80) for predicting the activity of the compounds. Docking studies of these compounds reveals and confirms that compounds 15, 18, 25, 26, and 28 are introduced as good candidates for DPP-4 inhibitors were introduced as a good candidate for DPP-4 inhibitory compounds.

scholarly journals Principles of QSAR Modeling

2020 ◽  
Vol 5 (3) ◽  
pp. 61-97 ◽  
Author(s):  
Paola Gramatica
Keyword(s):  
Genetic Algorithm ◽  
Linear Regression ◽  
Variable Selection ◽  
Multiple Linear Regression ◽  
Molecular Descriptors ◽  
Academic Career ◽  
Environmental Pollutants ◽  
Ordinary Least Squares ◽  
Qsar Modeling ◽  
Qsar Models

At the end of her academic career, the author summarizes the main aspects of QSAR modeling, giving comments and suggestions according to her 23 years' experience in QSAR research on environmental topics. The focus is mainly on Multiple Linear Regression, particularly Ordinary Least Squares, using a Genetic Algorithm for variable selection from various theoretical molecular descriptors, but the comments can be useful also for other QSAR methods. The need for rigorous validation, also external, and for applicability domain check to guarantee predictivity and reliability of QSAR models is particularly highlighted. The commented approach is the “predictive” one, based on chemometrics, and is usefully applied to the prioritization of environmental pollutants. All the discussed points and the author's ideas are implemented in the software QSARINS, as a legacy to the QSAR community.

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