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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.
√ 27556.11 × √ 624.9 – (22.02) 2 =? × 5.95
1120.04 – 450.18 + 319.98 ÷ 8.06 = ?
24.99 × 32.05 + ? - 27.01 × 19.97 = 29.99 × 27.98
Find the approximate value of Question mark(?). No need to find the exact value.
18.07 × (47.998 ÷ 12.03) + 59.78% of 150.14 – √(255.86) = ...
(124.901) × (11.93) + 219.95 = ? + 114.891 × 13.90
41.5% of ? + 64.69% of 419.1 = 504.2
10.10% of 999.99 + 14.14 × 21.21 - 250.25 = ?
{(1799.89 ÷ 8.18) ÷ 9.09 + 175.15} = 25.05% of ?
(27.08)2 – (14.89)2 – (22.17)2 = ?
159.98% of 4820 + 90.33% of 2840 = ? + 114.99% of 1980