# Linear Regression Research Paper

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.

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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.After checking the normality assumptions for both variables, bivariate correlation is tested (Pearson's correlation = 0.696, P Now to check the linear regression, put SBP as the dependent and age as the Independent variable.This indicates the dependent and independent variables included in the test.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.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.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.

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