Which statistical method is most suitable for quantifying the strength and direction of a linear relationship between two continuous variables?

Explanation: The Pearson correlation coefficient measures the strength and direction of a linear relationship between two continuous variables. It produces a value between -1 and 1, where -1 indicates a perfect negative correlation, 1 indicates a perfect positive correlation, and 0 indicates no linear correlation. It is widely used when the variables are normally distributed and have a linear association. For example, in analyzing the relationship between sales revenue and advertising expenditure, the Pearson coefficient can determine how strongly these two variables are related. Option A: The Chi-square test evaluates relationships between categorical variables, not continuous variables. Option C: The Spearman rank correlation is a non-parametric alternative to Pearson’s, suitable for ordinal data or non-linear relationships. Option D: ANOVA tests differences between group means and is not used for correlation analysis. Option E: The Mann-Whitney U test compares medians of two independent samples and does not measure correlation.