We are asked to calculate the missing values for the observed selling price of 394.2 (thousands of dollars) and square footage of 1984, using the provided predicted selling price equation:

Predicted Selling Price = 33.14 + 0.18(Square Footage)

Step-by-step calculation:

1. Predicted Selling Price:

   • The square footage is 1984.
   • Use the equation to calculate the predicted selling price.

   $\text{Predicted Selling Price} = 33.14 + 0.18 \times 1984$

   $\text{Predicted Selling Price} = 33.14 + 357.12 = 390.26 \text{ (thousands of dollars)}$

2. Error:

   • The observed selling price is 394.2 (thousands of dollars).
   • The error is the difference between the observed and predicted selling prices:

   $\text{Error} = \text{Observed Selling Price} - \text{Predicted Selling Price}$

   $\text{Error} = 394.2 - 390.26 = 3.94 \text{ (thousands of dollars)}$

3. Squared Error:

   • The squared error is the square of the error value:

   $\text{Squared Error} = (\text{Error})^2$

   $\text{Squared Error} = (3.94)^2 = 15.5236$

Final Table Entry for Observed Selling Price = 394.2 and Square Footage = 1984:

• Predicted Selling Price = 390.26
• Error = 3.94
• Squared Error = 15.52

Would you like me to explain further or solve any related questions? Here are 5 possible related questions to explore:

1. How do you interpret the squared error in the context of regression analysis?
2. How would the sum of squared errors change if the predicted prices were off by a larger margin?
3. What are the steps for calculating the regression line for such data?
4. How can we assess the accuracy of our model beyond the sum of squared errors?
5. What would happen to the model's predictions if we used a different formula for predicting the selling price?

Tip: To reduce the sum of squared errors, we can try optimizing the regression model or use techniques like ridge regression or Lasso if multicollinearity is an issue in more complex models.