Association between two independent variables is explained by:

Correct option is B

To understand this question, we need to clearly distinguish between correlation and regression, as they are both fundamental concepts in statistics:
�� 1. Correlation:
Definition: Correlation is a statistical measure that expresses the extent to which two variables are linearly related — that is, how one variable changes with respect to another.
It measures association but not causation.
The result is called the correlation coefficient (r), which ranges between -1 and +1:
+1: Perfect positive correlation
-1: Perfect negative correlation
0: No correlation
Use Case: When we want to know whether two independent variables (e.g., rainfall and temperature) move together or not.
�� 2. Regression:
Definition: Regression is used to predict the value of one variable (dependent) based on the value of another variable (independent).
It shows causal relationships.
Simple regression involves one independent and one dependent variable.
Multiple regression involves two or more independent variables to predict a single dependent variable.
Regression is not typically used to explain association between two independent variables.
�� Application to Question:
The question asks about the association between two independent variables.
Since regression and multiple regression involve a dependent variable, they are not appropriate here.
Correlation is the correct tool to assess the strength and direction of association between two independent variables.
✅ Correct Answer:
(b) Correlation