The accompanying dataset contains the listed prices (in thousands of dollars) and the number of square feet for 28 homes in a neighborhood. The data come from a simple random sample. For the SRM to work, we need to formulate the model in terms of cost per square foot. Use the selling price per square foot as y and the reciprocal of the number of square feet as x. (Note: If you keep track of the dimensions for the slope and intercept, you'll see that one represents fixed costs and the other, variable costs.) Complete parts a through e. Click the icon to view the data table of home prices and sizes. (a) Is the simple regression model a reasonable description of the association between the two variables? In particular, consider the conditions needed for the reliable use of the SRM. ○ A. All the conditions for the SRM are satisfied. OB. The errors are not independent. OC. The residuals are not normal. OD. The association between y and x is not linear. O E. There are obvious lurking variables. OF. The variances of the residuals are significantly different. (b) Give a 95% confidence interval for the fixed cost (the portion of the cost that does not change with the size of the home) associated with these home prices, along with a brief interpretation. The fixed cost is between $☐ and S☐ (Round to the nearest hundred dollars as needed.) Choose the correct interpretation below. OA. The fixed cost is zero. OB. The fixed cost is negative. OC. The fixed cost might be zero, negative, or considerable. OD. The fixed cost is positive.