a) Determine the Pearson correlation (r) between income and consumption and discuss your result To determine the Pearson correlation coefficient (r) between income and consumption from the data provided in the image, we first need to understand that the Pearson correlation coefficient measures the linear correlation between two variables, in this case, income and consumption. The coefficient ranges from -1 to 1, where 1 indicates a perfect positive linear relationship, -1 indicates a perfect negative linear relationship, and 0 indicates no linear relationship. Mean of Income = (67,640.52 + 54,001.57 + 59,787.38 + 72,408.93 + 68,675.98 + 40,227.22 + 59,500.86 + 48,486.43 + 48,967.81 + 54,105.99) / 10 = 57,379.269 Mean of Consumption = (56,832.64 + 52,472.63 + 53,635.09 + 60,535.52 + 59,159.78 + 35,850.15 + 57,071.10 + 39,763.35 + 42,739.59 + 41,014.31) / 10 = 49,977.426 The Pearson correlation coefficient (r) between income and consumption is 0.922, indicating a strong positive linear relationship. The OLS regression coefficients are b1 = 0.825 and b0 = 2,609.546, resulting in the regression equation: Consumption = 2,609.546 + 0.825 * Income. The Pearson correlation coefficient measures the strength and direction of the linear relationship between two variables. A value close to 1 indicates a strong positive relationship, as is the case here with a value of 0.922. This suggests that as income increases, consumption also tends to increase