A. Correlation coefficient.
Multicollinearity refers to the situation where two or more independent variables in a regression model are highly correlated with each other. One common approach to identifying multicollinearity is to calculate the correlation coefficient between pairs of independent variables. A high correlation coefficient (close to 1 or -1) indicates strong linear relationship between the variables, suggesting potential multicollinearity issues.
Option B, the chi-squared test, is used for testing independence between categorical variables, not for identifying multicollinearity between continuous variables.
Option C, the two-sample t-test, compares the means of two groups and is not directly related to identifying multicollinearity.
Option D, the two-way ANOVA, is used to analyze the effect of two categorical independent variables on a continuous dependent variable, and it does not directly address multicollinearity between independent variables.
So, option A, correlation coefficient, is the most appropriate method for identifying multicollinear attributes in a data set.
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Swift_and_Quick
6 months, 3 weeks ago