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.It is a modeling technique where a dependent variable is predicted based on one or more independent variables.
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.
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.
Linear regression is a statistical test applied to a data set to define and quantify the relation between the considered variables.
Univariate statistical tests such as Chi-square, Fisher's exact test, t-test, and analysis of variance (ANOVA) do not allow taking into account the effect of other covariates/confounders during analyses (Chang 2004).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.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.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.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.