# Linear Regression Research Paper This article explains the basic concepts and explains how we can do linear regression calculations in SPSS and excel.

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The techniques for testing the relationship between two variables are correlation and linear regression.

Correlation quantifies the strength of the linear relationship between a pair of variables, whereas regression expresses the relationship in the form of an equation.

Regression analysis allows predicting the value of a dependent variable based on the value of at least one independent variable.

In correlation analysis, the correlation coefficient “r” is a dimensionless number whose value ranges from −1 to 1.

For example, to know whether the likelihood of having high systolic BP (SBP) is influenced by factors such as age and weight, linear regression would be used.

The variable to be explained, i.e., SBP is called the dependent variable, or alternatively, the response variables that explain it age, weight, and sex are called independent variables. Armonk, NY: IBM Corp.) in five steps to analyze data using linear regression.In this article, we have used simple examples and SPSS and excel to illustrate linear regression analysis and encourage the readers to analyze their data by these techniques. Conflicts of interest There are no conflicts of interest.Keywords: Continuous variable test, excel and SPSS analysis, linear regression How to cite this URL: Kumari K, Yadav S. J Pract Cardiovasc Sci [serial online] 2018 [cited 2019 Sep 7];-6. 2018/4/1/33/231939The concept of linear regression was first proposed by Sir Francis Galton in 1894.Linear regression is a statistical procedure for calculating the value of a dependent variable from an independent variable.Linear regression measures the association between two variables.A value toward −1 indicates inverse or negative relationship, whereas towards 1 indicate a positive relation.When there is a normal distribution, the Pearson's correlation is used, whereas, in nonnormally distributed data, Spearman's rank correlation is used.This may be understood as how the risk factors or the predictor variables or independent variables account for the prediction of the chance of a disease occurrence, i.e., dependent variable.Risk factors (or dependent variables) associate with biological (such as age and gender), physical (such as body mass index and blood pressure [BP]), or lifestyle (such as smoking and alcohol consumption) variables with the disease.It is a modeling technique where a dependent variable is predicted based on one or more independent variables.Linear regression analysis is the most widely used of all statistical techniques.

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