B- Chooce the correct answer:1-which one of the following statements are truea- The maximum value of f(x) is 1 b- The maximum value of F(x) is 1c- Both (a) and (b)d- None of these2- If a and b are constant then Variance (ax +b) is equal toa- a² var.(x)b-a² var.(x) +b²c-a² var.(x) +bd-var.(x) +b²

(2) We know, V(x+b) = V(x) [ for some constant b ] and V(ax) =a2V(x) [ for some constant a ]

Answered: 2- If a and b are constant then…

Advanced Engineering Mathematics

10th Edition

ISBN:9780470458365

Author:Erwin Kreyszig

Publisher:Erwin Kreyszig

Chapter2: Second-order Linear Odes

Section: Chapter Questions

Problem 1RQ

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Suppose X~ Uniform [10, 50], find: P70 Variance (X)
The total cost for a production process is $600 minus two times the number of units produced. The mean and variance of the number of produced are 200 and 20, respectively. So, the mean and variance of on the total cost are of O a. 1000, 560 Оb. 600, 200 О с. 400, 180 O d. 200, 80 NEXT PAGE OUS PAGE 直。 ENG ) 180 acer
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Consider data on every game played by the Brooklyn Nets in 2014 (82 games) that includes the variables margin, - the Net's margin of victory (number of points the Nets scored minus the number of points their opponent scored) for game i, and • home; - a dummy variable equal to 1 when the Nets are the home team (game i was played in their home arena) and equal to 0 when they are the away team (game i was played in the opponent's arena). I use the least-squares method to estimate the following regression model margin = a + ßhome; + ei Below is the Stata output corresponding to the estimated regression line: regress margin home if team===== "Brooklyn Nets" . Source Model Residual Total margin home _cons SS 1459.95122 15252.0488 16712 df 1459.95122 1 80 190.65061 None of the above 81 206.320988 Coef. Std. Err. 8.439024 3.049595 -5.219512 2.156389 MS t Number of obs F(1, 80) Prob > F R-squared O The Nets lost more games than they won in 2014 P>|t| 2.77 0.007 -2.42 0.018 Adj R-squared = Root…
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/Skip/1 A beverage company has two bottling plants in different parts of the world. To insure uniformity in their product, the variances of the weights of the bottles at the two plants should be equal. But Stephanie, the company's vice president of quality assurance, suspects that the variance at bottling plant A is more than the variance at bottling plant B. To test her suspicions, Stephanie samples 37 bottles from bottling plant A and 33 bottles from bottling plant B, with the following results (in ounces): Bottling Plant A: 12.4, 12.4, 13.4, 14.3, 12.7, 12.7, 13.7, 12.4, 12.8, 12.4, 12.9, 12.7, 12.8, 13.3, 13.8, 12.7, 12.4, 11.9, 12.9, 13, 13.1, 12.4, 12.7, 12.6, 12.5, 13.3, 12.2, 13.3, 12.8, 12.7, 13.2, 13.1, 11.7, 13, 12.3, 13.1, 12.4 Bottling Plant B: 12.9, 12, 13.4, 12.4, 12.4, 12.9, 12.7, 12.1, 11.4, 13.1, 14.1, 12.5, 12.7, 11.5, 12.3, 12.7, 13.7, 12.6, 13.9, 12.7, 12, 12.5, 11.7, 13.6, 11.4, 13.6, 14, 12, 11.5, 12, 11.7, 13, 11.8 Perform a hypothesis test using a 3% level of…
Consider data on every game played by the Brooklyn Nets in 2014 (82 games) that includes the variables margin; - the Net's margin of victory (number of points the Nets scored minus the number of points their opponent scored) for game i, and • home; - a dummy variable equal to 1 when the Nets are the home team (game i was played in their home arena) and equal to 0 when they are the away team (game i was played in the opponent's arena). I use the least-squares method to estimate the following regression model margin = a + ßhome; + ei Below is the Stata output corresponding to the estimated regression line: regress margin home if team==== "Brooklyn Nets" Source Model Residual Total margin home _cons SS 1459.95122 15252.0488 16712 df 1 80 Coef. Std. Err. MS 81 206.320988 8.439024 3.049595 -5.219512 2.156389 1459.95122 190.65061 t Number of obs F (1, 80) Prob > F R-squared. Adj R-squared = Root MSE P>|t| 2.77 0.007 -2.42 0.018 82 7.66 0.0070 0.0874 0.0760 13.808 [95% Conf. Interval]…

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2- If a and b are constant then Variance (ax +b) is equal to a- a² var.(x) b-a² var.(x) +b² c- a² var.(x) +b d-var.(x) +b²