Bivariate statistical measures regression and linear correlation

Bivariate statistical measures regression and linear correlation

1. Bivariate statistics is a branch of statistics that deals with the analysis of two variables simultaneously.

   • It focuses on understanding the relationship between two variables, such as their correlation or regression.

2. Linear regression

   • Is a statistical method for modeling the relationship between a dependent variable (often denoted as "y") and one or more independent variables (often denoted as "x").
     • The goal of linear regression is to find the "best-fit" line that describes the relationship between the variables, such that the differences between the observed values and the predicted values (the residuals) are minimized.

3. Correlation coefficient

   • Is a statistical measure that indicates the strength and direction of the linear relationship between two variables. It is denoted by "r" and takes values between -1 and 1.
     • If the value of r is positive, it indicates a positive correlation between the variables, which means that as one variable increases, the other variable also tends to increase.
     • If the value of r is negative, it indicates a negative correlation, which means that as one variable increases, the other variable tends to decrease.
     • If the value of r is zero, it indicates no correlation between the variables.

4. The importance lies in that linear regression allows us to model the relationship between two variables and predict the values ​​of one variable based on another and the coefficient of consequences gives us a productive measure of the strength and direction of the relationship between two variables.