Abstract

The primary objective of this research article is to present the mathematical and statistical aspects of linear models and their characteristic properties. Linear model is the most common modeling used in science. Actually linear models have many different meanings depend on the context. Linear model is often preferred than other model such as quadratic model because of its ability to interpret easily. In the other hand most of the real life cases have linear relationship .Modeling the cases using linear model will able us to determine the relative influence of one or more independent variables to the dependent variable. In the present talk an attempt has been made to propose the specific forms of simple and multiple linear regression models. In this conversation mathematical aspects of linear models have been extensively depicted. Different types of mathematical models are discussed here and the methods of fitting transformed models are proposed.Furthermore specific form of linear statistical model is presented and the crucial assumptions of general linear model are extensively discussed.At the last stage of this article the method of ordinary least squares estimation of parameters of a linear model has been proposed

Highlights

  • Model refers to a set of functional or structural relationships between two or more characteristics

  • Regression analysis has a wide number of applications in almost all fields of science, including Engineering, Physical and Chemical Sciences; Economics, Management, Social, Life and Biological Sciences

  • A more plausible model may be specified as Equation (2) is called a Linear Regression Model or Linear Statistical Model

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Summary

A STUDY ON MATHEMATICAL AND STATISTICAL ASPECTS OF LINEAR MODELS

Article History: Received: 11 January 2021; Accepted: 27 February 2021; Published online: 5 April 2021

1.Introduction
A Three – variable Linear Regression Model may be written as
MATHEMATICAL ASPECTS OF LINEAR MODEL
TYPES OF LINEAR MATHEMATICAL MODEL
Yt of any general polynomial
LINEAR STATISTICAL MODEL:
X ˆ e e where ˆ is the least squares estimator of
Conclusion and Future Research
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