Explanation

Multicollinearity occurs when two or more independent variables in a regression model are highly correlated with each other. Among the given options:

• A. Inappropriate data pooling - This refers to combining data from different populations or time periods that should be analyzed separately, which can lead to structural breaks or parameter instability, not necessarily multicollinearity.

• B. Inappropriate variable scaling - This can affect the interpretation of coefficients but doesn't directly cause multicollinearity.

• C. Inappropriate form of variables - This is the most likely cause of multicollinearity. When variables are transformed or specified in ways that create artificial correlations (e.g., including both a variable and its square, or including variables that are functionally related), multicollinearity can result.

Correct Answer: C - Inappropriate form of variables is the most direct cause of multicollinearity in regression functional form.