Which statistical method is used to analyze strong and weak correlations in data responses?

The correct approach for analyzing strong and weak correlations in data responses is correlation matrix. A correlation matrix provides a systematic way to quantify the relationship between multiple variables by displaying the correlation coefficients between them. These coefficients indicate the strength and direction of linear relationships, with values ranging from -1 to 1. A value close to 1 indicates a strong positive correlation, while a value close to -1 indicates a strong negative correlation. Values near 0 suggest a weak or no correlation.

Regression analysis is typically used to predict the value of a dependent variable based on one or more independent variables, rather than simply identifying the strength of correlations. Factor analysis is a technique aimed at identifying underlying relationships between variables and reducing data dimensionality. Cluster analysis focuses on categorizing data into groups based on similarities, rather than measuring correlations directly. Each of these methods serves a unique purpose in data analysis but does not specifically target the measurement of correlation strengths as the correlation matrix does.