A company is planning a plant expansion. It can build a large or small plant. The payoffs for the plant depend on the le

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A Company Is Planning A Plant Expansion It Can Build A Large Or Small Plant The Payoffs For The Plant Depend On The Le 1 (37.77 KiB) Viewed 19 times

A Company Is Planning A Plant Expansion It Can Build A Large Or Small Plant The Payoffs For The Plant Depend On The Le 2 (55.74 KiB) Viewed 19 times

A company is planning a plant expansion. It can build a large or small plant. The payoffs for the plant depend on the level of consumer demand for the company's products. The company believes that there is a 69% chance that demand for its products will be high and a 31% chance that it will be low. The present value of future cash flow and costs of the two plants follow. (The present value of future cash flow in millions of dollars.) Factory Size Large Small Demand High Low 210 110 95 75 Plant Cost ($ million) 10 2 Based on the probabilities of demand, what is the expected profit (in million dollars)? (give your answer with two decimal places. Do not write the dollar sign)

A local office furniture manufacturer has built a regression model to predict the sales revenues of its popular office desk based on the amount spent on advertising. It has developed the following Excel spreadsheet of the results: SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept X Variable 1 0.984444276 0.969130533 0.96527185 20.42132374 df 10 O (439.52, 521.21) O (514.36, 612.37) O (467.27, 548.96) (217.51, 299.20) (395.12, 476.81) 1 8 9 SS MS F Significance F 104739.6003 104739.6 251.156 2.5143E-07 3336.243706 417.03046 108075.844 Coefficients Standard Error 36.34235294 5.550294118 t Stat P-value Lower 95% 21.98328259 1.6531814 0.13689 -14.351188. 0.350222813 15.847894 2.5E-07 4.74267886 Upper 95% Lower 95.0% pper 95.0% 87.03589349 -14.35118761 87.0359 6.357909372 4.742678863 6.35791 An approximate 95% prediction interval for the sale revenues with an advertising budget of 80 (in thousands) is: