Regression and correlation are two related statistical techniques that are used to describe the relationship between two variables. Correlation is a measure of how two variables are related. It measures the degree to which one variable is related to another. It can be used to identify relationships between variables as well as to predict future values.
Regression on the other hand is a modeling technique used to predict the value of a dependent variable based on the value of one or more independent variables. It is used to explain the relationship between two or more variables and to predict the value of a dependent variable from an independent variable.
The connection between correlation and regression is that the relationship between two variables can be described using correlation, and regression can be used to determine the degree of influence that one variable has on the other. Correlation helps to identify linear patterns between two variables, while regression takes those identified patterns and uses them to make predictions about future values.
In other words, correlation and regression can be used together to better understand the relationship between two variables. By using correlation to identify patterns and regression to determine the degree of influence, researchers can gain a better understanding of the relationship between two variables.
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