This data table gives annual costs of 223 commercial leases. All of these leases provide office space in a Midwestern city in the United States. For the response, use the cost of the lease per square foot. As the explanatory variable, use the reciprocal of the number of square feet.
(a) Identify the leases whose values lie outside the 95% prediction intervals for leases of their size. Does the location of these data indicate a problem with the fitted model?
(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.
(c) The leases with the four largest residuals have something in common. What is it, and does it help you identify a lurking variable?