Certainly! Let's continue with the calculations. **Step 3: Calculate Covariance** The covariance between income (X) and consumption (Y) is given by: \[ \text{Cov}(X, Y) = \frac{\sum (X_i - \bar{X})(Y_i - \bar{Y})}{n - 1} \] Substituting the given values: \[ \text{Cov}(X, Y) = \frac{771,334,668.1}{9} = 85,703,851.9 \] **Step 4: Calculate the Standard Deviations** The standard deviation for income (\(\sigma_X\)) and consumption (\(\sigma_Y\)) are calculated as follows: \[ \sigma_X = \sqrt{\frac{\sum (X_i - \bar{X})^2}{n - 1}} = \sqrt{103,923,888.96} = 10,194.31 \] \[ \sigma_Y = \sqrt{\frac{\sum (Y_i - \bar{Y})^2}{n - 1}} = \sqrt{83,258,555.57} = 9,122.45 \] **Step 5: Calculate Pearson Correlation** The Pearson correlation coefficient (\(r\)) is given by: \[ r = \frac{\text{Cov}(X, Y)}{\sigma_X \sigma_Y} = \frac{85,703,851.9}{92,933,000.1} = 0.922 \] The strong positive correlation (r = 0.922) indicates a linear relationship between income and consumption. **Step 6: Calculate the OLS Regression Coefficients** The OLS regression coefficients are: \[ b_1 = \frac{\text{Cov}(X, Y)}{\text{Var}(X)} = 0.825 \] \[ b_0 = \bar{Y} - b_1 \bar{X} = 2,609.546 \] The OLS regression equation is: \[ \hat{Y} = b_0 + b_1 X \] where \(\hat{Y}\) represents the predicted consumption based on income. If you have any further questions or need additional assistance, feel free to ask!