GFT decided to statistically test the idea that costs increased after month 15 (for what ever reason) using a linear regression model that includes a step cost to capture the apparent increase after month 15. The model they estimated is: Total Costs =b0+b1# of steps +b2 the number of clients b0 is the estimate of monthly fixed costs b1 is the cost per step. For months 1 to 15 the number of steps =0 and For for months 16 to 30 the number of steps =1 b2 is the variable cost per client The linear regression results were: Based on the regression results, if GFT forecasted the next two months (both months are subsequent to the end of the data collection period) to have 800 and 900 clients, respectively, what would be the total expected costs for this two month period? When looking over the above scatter graphs GFT noticed an odd pattern in the data and wondered whether their cost estimates would be reliable. The managers decided to look at the data from a different perspective and produced a scattergraph of Operating Costs vs Month for the 30 months. Which of the following explanations seems most plausible for the odd pattern in the Cost vs. \# of Clients graph. When looking over the above scatter graphs GFT noticed an odd pattern in the data and wondered whether their cost estimates would be reliable. The managers decided to look at the data from a different perspective and produced a scattergraph of Operating Costs vs Month for the 30 months. Which of the following explanations seems most plausible for the odd pattern in the Cost vs. \# of Clients graph. Costs are random and have no obvious pattern. The costs have a seasonal pattern due the nature of the fishing season. Months 1 to 15 have one average cost and Months 16 to 30 have a different average cost, perhaps due to the change in customer service policy. Costs increased sharply in month 16 because of inflation in the economy