The sheet called HousePr contains data on prices of houses that have sold recently and two attributes of the house: the number of bedrooms and the size. Column 1 is the selling price of the house in thousands of dollars, and Column 2 is the size in hundreds of square feet.

a. Draw a scattergram of price vs. size. Discuss whether this scattergram supports an assumption of a linear relationship between price and size.

b. Using Excel, obtain the equation of the linear regression line that fits this data for price vs. size.

c. Using relevant Excel output, discuss whether the true slope of the regression line is different from zero.

d. What is the expected price for a house with a size of 2000 square feet? Using relevant Excel output, discuss whether the margin of error of this expected price will be low or high.

To investigate the relationship between house size and selling price, a scatter plot and linear regression analysis using Excel is performed. The linear regression output will help determine if there is a significant relationship between the variables and the margin of error for predictions.

Explanation:

The student has been tasked with creating a scatter plot to visualize the relationship between house size and its selling price, and then to calculate the linear regression line that best fits this data using Excel. After creating the scatter plot, one would expect to see that if there is a linear relationship, the points on the scatter plot would align in a fashion that resembles a line.

In Excel, this can be further quantified by fitting a linear regression line and obtaining its equation, which is commonly in the form of îy = a + bxï, where 'y' represents the dependent variable (price), 'x' represents the independent variable (size), 'a' is the y-intercept, and 'b' is the slope of the line.

Once the regression line is established in Excel, we would look at the output to determine if the slope 'b' is significantly different from zero, which indicates a relationship between size and price. In the context of house prices, this would mean that as the size of the house increases, the price is also expected to change.

The significance can be determined from t-tests and p-values provided in Excel's regression output.

Furthermore, when predicting the expected price of a house with 2000 square feet, we would use the regression equation to calculate this value. The margin of error for this prediction can be evaluated by considering the confidence interval around the prediction, which is also available in Excel's regression analysis output.

A narrower confidence interval indicates a lower margin of error, while a wider interval suggests a higher margin of error and less precise prediction.