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International Journal of Research and Analytical Reviews (IJRAR)

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E-ISSN 2348-1269, P- ISSN 2349-5138 | ESTD Year: 2014

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  Published Paper Details:

  Paper Title  

GENERAL LINEAR REGRESSION

  Authors

   Kirti Gadekar ,  Sharad Gore

  Keywords

Regression , Simple Regression , multiple regression , linear regression , polynomial regression , logistic regression , regression tree , link function , regression path , path analysis.

  Abstract


: Linear regression is the most basic prediction model in Statistics. It includes simple and multiple linear regressions. Literature on regression includes several variations of regression. The most common variation is brought about by introducing non-linearity in the regression model. For instance, polynomial regression introduces non-linearity in predictor variables, while logistic regression introduces non-linearity in the response variable. A recent variation in the form of regression tree discretizes the response variable through partitioning data so that every leaf node of the regression tree has a distinct predicted value for the response variable. Some researchers have introduced the concept of link functions in order to develop paths. This approach has resulted in the development of structural equations modeling and path analysis. This paper proposes a general form of the regression model. The main feature of the proposed model is linearity in coefficients and non-linearity, if any, of predictors in the form of link functions. Link functions are identified by examining the nature of the relationship between predictor variables and response variable. The purpose of this paper is to present a unified regression equation that will be useful in all applications. Some illustrative examples are given to illustrate the procedure and discuss how the results can be interpreted.

  Cite this article

   Kirti Gadekar ,  Sharad Gore,   "GENERAL LINEAR REGRESSION", IJRAR - International Journal of Research and Analytical Reviews (IJRAR), E-ISSN 2348-1269, P- ISSN 2349-5138, Volume.6, Issue 1, Page No pp.42-46, January 2019, Available at : http://www.ijrar.org/IJRAR190M007.pdf  

  IJRAR's Publication Details

  Unique Identification Number - IJRAR190M007

  Paper ID - 195303

  Page Number(s) - 42-46

  Pubished in - Volume 6 | Issue 1 | January 2019

  DOI (Digital Object Identifier) -    http://doi.one/10.1729/Journal.20954

  Publisher Name - IJRAR | www.ijrar.org | E-ISSN 2348-1269, P- ISSN 2349-5138

  E-ISSN 2348-1269, P- ISSN 2349-5138

  Cite this article

   Kirti Gadekar ,  Sharad Gore,   "GENERAL LINEAR REGRESSION", IJRAR - International Journal of Research and Analytical Reviews (IJRAR), E-ISSN 2348-1269, P- ISSN 2349-5138, Volume.6, Issue 1, Page No pp.42-46, January 2019, Available at : http://www.ijrar.org/IJRAR190M007.pdf

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The IJRAR - International Journal of Research and Analytical Reviews (IJRAR) aims to explore advances in research pertaining to applied, theoretical and experimental Technological studies. The goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working in and around the world.


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