The expected payoff if the vendor has perfect information is:
Answer(s): B
The vendor would like to sell coffee on cold days US $2,000) and soft drinks on hot days US $2,500). Hot days are expected 40°l0 of the time. Hence, the probability is 40°f0 of making US $2,500 by selling soft drinks. The chance of making US $2,000 by selling coffee is 600k. The payoff equation is:4 US $2,600) + .6 $2,000 = US $2,200A beverage stand can sell either soft drinks or coffee on any given day. If the stand sells soft drinks and the weather is hot, it will make US $2,500; if the weather is cold, the profit will be U $1,000. If the stand sells coffee and the weather is hot, it will make US $1,900; if the weather is cold, the profit will be U $2,000. The probability of cold weather on a given day at this time is 60%.
If the probability of hot weather, given a hot weather forecast, is 50%, how much would the vendor be willing to pay for the forecast?
If the weather is hot and coffee is served, the vendor earns US $1,900. If the vendor knows the weather will be hot, she would sell soft drinks and make U $2,500, a US $600 increase.Thus, the vendor should be willing to pay up to U $600 for perfect information regarding hot weather. However, if the forecasts are only 50% accurate, the information is not perfect. Accordingly, the vendor should be willing to pay only U $300 the U $600 potential increase in profits 50%) for the sometimes accurate forecasts.
An audit manager has just returned from an executive training program and has suggested that the audit department develop a mathematical model to help identify factors that may be causing changes in the cost of production. According to the manager, the model should recognize that the company currently has three separate production cost) ': enters.Which of the following approaches would best provide the analysis suggested by the audit manager?
Answer(s): C
Regression analysis extends correlation to find an equation for the linear relationship among variables. The behavior of a dependent variable, such as cost of production, is explained in terms of one or more independent variables for example, raw material costs, employees, overtime). Thus, multiple regression analysis determines functional relationships among quantitative variables.
The following data on variables x and y was collected from June to October:The correlation coefficient between variables x and is nearest to:
A correlation coefficient of -1.00 describes a perfect inverse correlation; that is, the observations fall exactly along a straight line and the value of one variable increases decreases) as the other decreases increases). In the example to the left, the equation of the straight line is:
A company} is formulating its plans for the coming year, including the preparation of its cash budget. Historically, 30% of the company's sales are cash sales. The remaining 70% are credit sales with the following collection pattern.For the month of April, the total cash receipts from sales and collections on account would be:
The cash receipts for April equal April's cash sales US $4,000,000 x 30°l0 = US $1,200,000), 40% of April's credit sales, and 580/o of March's credit sales. Consequently, total cash receipts equal US $3, 78-1,600 [$1,200,000 + $4,000,000 40°lax 70°/o) + $8,600,000 68°f0 70%)].A bank has two drive-in lanes to serve customers: one attached to the bank itself and one on an island. One teller serves bath stations. The bank is interested in determining the average waiting times of customers and has developed a model based on random numbers. The two key factors are the time between successive car arrivals and the time customers wait in line. Assume that the analysis begins with cars just arriving at bath service windows. bath requiring 3 minutes of service time. Car 1 is the attached window attached to the bank unless that window has more cars waiting than the island window. The lone teller will always serve the car that arrived first. If two cars arrive simultaneously, the one at the attached window will be served before the one at the island.
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