1. **Problem Statement:** We want to find the equation of the straight line that best fits the data relating monthly e-commerce sales (Y) to online advertising costs (X) based on survey results from 7 online stores. 2. **Given Information:** - Correlation coefficient $r = 0.980$ indicates a strong positive linear relationship. - Regression equation form: $$Y = b_0 + b_1 X$$ where $b_0$ is the intercept and $b_1$ is the slope. - From the data, the fitted line is given as: $$\text{Monthly E-commerce Sales (in 10)} = 125.8 + 171.5 \times \text{Online Advertising Dollars (100)}$$ 3. **Interpretation of the equation:** - The intercept $b_0 = 125.8$ means when online advertising dollars are zero, the expected monthly sales are 125.8 (in 10 units). - The slope $b_1 = 171.5$ means for each increase of 1 unit in online advertising dollars (100 units), the monthly sales increase by 171.5 (in 10 units). 4. **Summary statistics:** - Standard error of estimate $S = 49.9684$ - Coefficient of determination $R^2 = 96.13\%$ means 96.13% of the variation in sales is explained by advertising costs. - Adjusted $R^2 = 95.35\%$ accounts for the number of predictors. 5. **Conclusion:** The best fit line equation is: $$Y = 125.8 + 171.5 X$$ where $Y$ is monthly e-commerce sales (in 10 units) and $X$ is online advertising dollars (in 100 units). This confirms a strong positive linear relationship between advertising costs and sales.