The data table available below gives the annual cost per square foot of 50 commercial leases for office space in a city and the reciprocal of the number of square feet. Use the cost per square foot as...

Extracted text: The data table available below gives the annual cost per square foot of 50 commercial leases for office space in a city and the reciprocal of the number of square feet. Use the cost per square foot as the response variable and the reciprocal of the number of square feet as the explanatory variable complete parts (a) through (c). (a) How many leases lie outside the 95% prediction intervals for leases of their size? Does the location of these data indicate a problem with the fitted model? (Hint: Are all of these residuals on the same side, positive or negative, of the regression?) There are leases that lie outside the 95% prediction intervals. (Type a whole number.) Does this indicate a problem with the fitted model? O A. Yes, all of the residuals for these data are negative. O B. Yes, all of the residuals for these data are positive. O C. No, none of the leases lie outside the 95% prediction intervals. O D. No, the residuals for these data are evenly divided between positive and negative. (b) Given the context of the problem (costs of leasing commercial property), list several possible lurking variables that might be responsible for the size and position of leases with large residual costs. Which of the following are possible lurking variables? Select all that apply. O A. The condition of the building O B. The location of the city O c. The type of business O D. The type of building O E. The age of the building O F. The location of the building within the city O G. The economic conditions in the city (c) The leases with the four largest residuals have something in common. What is it, and does it help you identify a lurking variable? What do the four leases with the largest residuals in terms of absolute value have in common? O A. The x-values for these leases are near the minimum x-value. O B. The x-values for these leases are near the maximum x-value. O C. The x-values for these leases are near the average x-value. O D. The x-values for these leases are the minimum and the maximum x-values. Does this help identify a lurking variable? V the prediction intervals are V for these x-values.


Extracted text: Cost and area data TIT Cost 1 Cost Square Foot 15.177 Number of Square Feet 0.00023274 Square Foot 16.339 Number of Square Feet 0.00030662 15.322 0.00017586 16.823 0.00021664 14.534 0.00001199 17.388 0.00049304 0.00032233 15.343 0.00017042 15.461 14.693 0.00007745 0.00014383 16.489 0.00008444 16.767 14.526 0.00030256 15.877 0.00011262 15.126 0.00019374 15.284 0.00023278 14.931 0.00012477 14.656 0.00037358 16.672 0.00033635 0.00031089 17.317 0.00033186 15.864 15.447 0.00004029 16.071 0.00022382 0.00005874 16.166 0.00001847 15.086 16.337 0.00006119 16.001 0.00008716 0.00038027 19.058 0.00034061 18.911 14.731 0.00008316 15.425 0.00031117 16.071 0.00019216 17.066 0.00038725 16.363 0.00035243 14.386 0.00002919 16.388 0.00013243 16.084 0.00040724 15.933 0.00018328 17.031 0.00049644 15.126 0.00022587 0.00020026 15.146 18.111 14.863 14.497 0.00014203 0.00049998 0.00003893 0.00005546 0.00023546 16.288 16.059 0.00006475 16.843 14.983 0.00003124 15.631 0.00008181 15.934 0.00012788 14.494 0.00003439