The Fundamental Premises of Efficient Linear Regression and Applying Regression

Open Access

Year : 2023 | Volume :9 | Issue : 2 | Page : 21-26
By

Chanchal

  1. Student Department of Mechanical Engineering, Noida International University, Greater Noida Uttar Pradesh India

Abstract

Linear regression is a statistical technique for estimating the value of a dependent variable from an independent variable. Linear regression is a way to assess how two variables are related. A dependent variable is predicted using this modelling technique based on one or more independent factors. Many analyses are based on linear regression. Sometimes the data must be changed to satisfy the needs of the analysis, or extra room must be made for the X variable’s high uncertainty. Alternative robust nonparametric approaches can be utilised if the conditions for linear regression analysis are not satisfied. When the straight line in a data set passes through the origin at 0, 0, simplified equations can be applied. The most common method for predicting the value of the Y variate at any value of the X variate is linear regression. However, occasionally, an inverse prediction is required, which requires a different strategy.

Keywords: Linear regression, standard deviation. Mahalanobis, least squares, dependent variable.

[This article belongs to Trends in Machine design(tmd)]

How to cite this article: Chanchal. The Fundamental Premises of Efficient Linear Regression and Applying Regression. Trends in Machine design. 2023; 9(2):21-26.
How to cite this URL: Chanchal. The Fundamental Premises of Efficient Linear Regression and Applying Regression. Trends in Machine design. 2023; 9(2):21-26. Available from: https://journals.stmjournals.com/tmd/article=2023/view=91746

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Regular Issue Open Access Article
Volume 9
Issue 2
Received September 23, 2022
Accepted September 30, 2022
Published January 30, 2023
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