Abstract
As is known, finding the parameters of multiple linear regression is an important case. Of course, these parameters can be easily found with the help of the computer. In this study, in addition to the formula of the parameters of linear regression, the general formulas of the parameters of 5 and less independent variables of multiple linear regression are given with a certain order. The derivations of the formulas presented are given step by step. In addition to classical matrix form, these new formulas for estimation of the parameters of multiple linear regression could be proposed especially to the researchers not using computer program for calculating the complex operations. By using these formulas, the researcher can estimate easily the parameters of multiple linear regression without using a computer and so the researcher can compose easily the table of variance analysis to interpret the regression made. Since for 6 and more independent variables, the tables of the parameters of multiple linear regression are too long and they take up too much space, the general formulas of the parameters of 6 and more independent variables of multiple linear regression could not be given in this study.
Highlights
The process of determining the relationship between one dependent variable and one or more independent variable(s) is called regression
In addition to the formula of the parameters of linear regression, the general formulas of the parameters of 5 and less independent variables of multiple linear regression are given with a certain order
Since for 6 and more independent variables, the tables of the parameters of multiple linear regression are too long and they take up too much space, the general formulas of the parameters of 6 and more independent variables of multiple linear regression could not be given in this study
Summary
The process of determining the relationship between one dependent variable and one or more independent variable(s) is called regression. Statistics, and many sciences, regression is one of the important topics. Regression is a means of predicting a dependent variable based on one or more independent variables. This is done by fitting a line or surface to the data points that minimizes the total error. The line or surface is called the regression model or equation. In this study firstly we will work with simple linear regression. After that we will work with multiple linear regression
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