# Linear Regression Research Paper

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However, partial correlation and regression are the tests that allow the researcher to control the effect of confounders in the understanding of the relation between two variables (Chang 2003).

In biomedical or clinical research, the researcher often tries to understand or relate two or more independent (predictor) variables to predict an outcome or dependent variable.

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.

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.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.R Square is the coefficient of determination which here means that 92% of the variation can be explained by the variables.Adjusted R square adjusts for multiple variables and should be used here. [Table 7] shows how to create a linear regression equation from the data.By continuing to use this site, you consent to the use of cookies.We use cookies to offer you a better experience, personalize content, tailor advertising, provide social media features, and better understand the use of our services.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.Both correlation and regression provide this opportunity to understand the “risk factors-disease” relationship (Gaddis and Gaddis 1990).While correlation provides a quantitative way of measuring the degree or strength of a relation between two variables, regression analysis mathematically describes this relationship.

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