A statistical method known as simple correlation is used to evaluate the direction and strength of a link between two variables. It is indicated by the correlation coefficient, which is commonly symbolized by the letter "r". Between -1 and 1, the correlation coefficient can lie. As one variable rises, the other tends to rise as well, and a positive value denotes a positive correlation, whereas a negative value denotes a negative correlation (as one variable rises, the other tends to fall). Zero means there is no correlation.

On the other hand, regression analysis is a statistical technique used to look at how one or more independent variables and a dependent variable are related. A single independent variable is used in simple regression, which seeks to estimate or forecast the value of the dependent variable based on the independent variable.

In a simple linear regression model, a straight line is used to represent the connection between the variables, which is considered to be linear. A simple linear regression model's equation is often expressed as follows:

On the other hand, regression analysis is a statistical technique used to look at how one or more independent variables and a dependent variable are related. A single independent variable is used in simple regression, which seeks to estimate or forecast the value of the dependent variable based on the independent variable.

In a simple linear regression model, a straight line is used to represent the connection between the variables, which is considered to be linear. A simple linear regression model's equation is often expressed as follows:

Noting that correlation does not indicate causality is important. One variable does not necessarily cause the other to change just because two variables are connected. Simply said, correlation evaluates the direction and strength of a link between two variables.

Regression analysis and correlation are both often utilized in many different disciplines, including economics, social sciences, finance, and many more. They aid in making forecasts and well-informed judgments based on data and offer useful insights into the correlations between variables.