2010 03 21 210212 ArmyGuySunday

1084 Words Jun 16th, 2015 5 Pages
K Brown is the principal owner of Brown oil inc, after quitting his university teachihng job, ken has been able to increase his annual salary by a factor of over 100. at the present time, ken is forced to consider purchasing some more equipment for brown oil because of competition. His alternatives are showing in the following table: Equipment Favorable Market $ Unfavorable Market$ Sub 100 300,000 -200,000
Oiler J 250,000 -100,000
Texan 75,000 -18,000 For ex, if ken purchases a Sub 100 and if there is a favorable market he will realize a provit of 300,000. On the other hand if the market is unfavorable ken will suffer a loss of 200,000. But ken has alwasy been very optimistic decison maker
a) what type of decision is Ken facing
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Interpret the value of r2 in the context of the problem r2 = 0.8474 r = sqrt(0.8474) = 0.92054
84% of the variance in the second exam score is explained by the first exam score.
3)Accountatns at the firm walker and walker believed that several travleing executives submit unusualy high travel voucherswhen they return from trips. The accounts. took a smaple of 200 vouchs sumbited last year the then developed the following mulitiple regression equation relating expected travel cost (Y) to number days on the road (X1) and distance traveled (X2) in miles: y= $90,000+$48.50X1+$0.40X2
The coefficient of correlation computed was 0.68.
a) if thomas williams returns form a 300 mile trip that took him of of town for 5 days, what is the expected amount that he should claim as expenses? y= $90+$48.50(5)+$0.40(300) y = $452.50
b) williams submitted a reimbursement request for $685 what should the accountant do?
The accountant should calculate a 95% prediction interval for the regression model, and see whether or not $685 falls within it. If not, then the accountant can be 95% certain that Williams’ expenses are too high, based on the current model. This does not necessarily mean that Williams has submitted a fraudulent report, as there may be another common cause.
c) comment on the vailidit of this model/. Should any other variable be included? Which ones? Why
If the correlation coefficient is 0.68, then the

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