Bivariate analysis
[1] It involves the analysis of two variables (often denoted as X, Y), for the purpose of determining the empirical relationship between them.
[1] Bivariate analysis can be helpful in testing simple hypotheses of association.
Bivariate regression aims to identify the equation representing the optimal line that defines the relationship between two variables based on a particular data set.
This equation is subsequently applied to anticipate values of the dependent variable not present in the initial dataset.
Through regression analysis, one can derive the equation for the curve or straight line and obtain the correlation coefficient.
-intercept The least squares regression line is a method in simple linear regression for modeling the linear relationship between two variables, and it serves as a tool for making predictions based on new values of the independent variable.
Covariance can be difficult to interpret across studies because it depends on the scale or level of measurement used.
Pearson’s correlation coefficient is used when both variables are measured on an interval or ratio scale.
Examples are Spearman’s correlation coefficient, Kendall’s tau, Biserial correlation, and Chi-square analysis.Three important notes should be highlighted with regard to correlation: If the dependent variable—the one whose value is determined to some extent by the other, independent variable— is a categorical variable, such as the preferred brand of cereal, then probit or logit regression (or multinomial probit or multinomial logit) can be used.
When neither variable can be regarded as dependent on the other, regression is not appropriate but some form of correlation analysis may be.
