To conduct correlational analysis in data mining software, we use the Correlation Matrix.

• A. Regression
• B. Scatter Plot
• C. Correlation Matrix
• D. K-Means

Answer: (C)

Correlational analysis aims to measure how pairs of variables move together in terms of strength and direction. A correlation matrix does this in one compact view by computing the correlation coefficient for every pair of variables and placing those values in a single table. The result lets you quickly see which variables are positively or negatively related and how strong those relationships are, which is especially useful for spotting potential multicollinearity before building models or for exploring data structure in general.

This approach is preferred for correlational analysis because it summarizes all pairwise relationships at once. Regression focuses on predicting one variable from others, not on listing all pairwise associations. A scatter plot shows the relationship between two variables visually, but it becomes impractical to inspect every pair when many variables are involved. K-means is a clustering method used to group observations, not to measure variable relationships.