Mendota Foods sells a variety of food products around the world. The company engages directly with individuals with an interest in cooking by offering ideas and recipes in various staff blogs and podcasts. The management team at Mendota believes this drives brand loyalty and allows Mendota to charge a premium for its products.

One initiative that Mendota has been running for the last three years is a call center with staff who can provide help and answer questions about cooking in general and Mendota products in particular. The chief financial officer is interested in learning more about the costs of this initiative and how they vary with the demands placed on the center. To help answer the question, the financial staff have collected call center volume $($number of calls$)$ and call center cost for the last $12$ months.

During a discussion of those results, one of the cost analysts suggests that length of the calls $($total hours$)$ is probably a better driver of call center costs. The managers asked the analyst to collect the information and report on how it should be used. The combined data follow:

Month Number of Calls Hours Call Center Costs

$160,75015,860$ $ $287,710$

$267,15017,140268,095$

$360,25016,000238,500$

$462,00014,500244,515$

$545,25012,620182,700$

$674,15020,000280,690$

$752,10013,180252,710$

$865,60017,540282,795$

$977,50021,150314,925$

$1045,25010,400153,675$

$1166,70018,460292,410$

$1269,90018,820271,885$

Using the results from a simple regression of call center cost on call center time $($hours$),$ input the following data:

d$-1.$ Enter the regression coefficients.

d$-2.$ Estimate the cost for a month with $16,000$ hours of call center time using the results from a simple regression of call center cost on call center time $($hours$).$