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A company is considering expansion of its current facility to meet increasing demand. If demand is high in the future, the major expansion would result in an additional profit of $800,000, but if demand is low then there would be a loss of $500,000. If demand is high, the minor expansion will result in profits of $200,000, but if demand is low then there is a loss of $100,000. The company has an option of not expanding. a. How would you advise the company? (Give a number of possible solutions under various assumptions.) b. If you found out that the probability of a high demand is .75, what would your advice be then? What is the maximum you would be willing to pay for perfect information?
Variable regression model - matrix of independent variables- multiple regression and, in particular.
Let X be the sample mean of a random sample of size n from a uniform distributionon the interval [0, b], and let bb = 2X be an estimator of the upper endpoint b.(a) Demonstrate that bb is an unbiased estimator of b, and find the variance of th..
Consider the way the table is arranged. If the hypothesis is correct, should we find a positive sign on Somers' dyx or a negative Signon Somers' dyx? Explain how you know.
What is the probability in the sample?
What conclu-sions can you reach concerning calories, protein, andcholesterol?3
1. in this exercise we will be building regression models for predicting house prices. we will be using data collected
How many cases of relays and capacitors should Harkin Electronics produce during that period? If your answer is in fractional units of cases that is acceptable - do not round to whole number of cases.
Use a regression model with dummy variables as follows to develop an equation to account for seasonal effects in the data. Qtr1 = 1 if Quarter 1, 0 otherwise; Qtr2 = 1 if Quarter 2, 0 otherwise; Qtr3 = 1 if Quarter 3, 0 otherwise.
A manufacturer of electronics kits has found
quantitative analysis for decision making course assignment. 1- graphical solutions in linear programming have limited
"Range of optimality" describes the impact of simultaneous changes in objective function values and right-hand-side values.
Obtain a time-series plot and correlogram of the residuals from (9) and comment on them with regard to the violation of classical assumptions. Test for fourth-order autocorrelation using the Breusch-Godfrey procedure
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