Consider the accompanying data on flexural strength (MPa) for concrete beams of a certain type.6.86.58.1 7.0 7.0 11.8 6.88.6 10.77.7 7.2 7.97.9 7.35.0 11.3 11.6 7.4 9.79.0 9.77.87.7 7.1 6.3 6.38.7Calculate a point estimate of the mean value of strength for the conceptual population of all beams manufactured in this fashion. [Hint: Ex, = 218.9.] (Round your ansver to three decimal places.)MPaState which estimator you used.Os/xOsJ Calculate a point estimate of the strength value that separates the weakest 50% of all such beams from the strongest 50%.MPaState which estimator you used.OsCalculate a point estimate of the population standard deviation o. [Hint: Ex = 1852.07.] (Round your answer to three decimal places.)MPaInterpret this point estimate.O This estimate describes the bias of the data.O This estimate describes the linearity of the data.O This estimate describes the center of the data.O This estimate describes the spread of the data.

Answered: Image /qna-images/answer/4d6ddb71-d92c-43d5-a7f0-18309087cb36.jpg

Answered: Consider the accompanying data on…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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ONLY THE LAST ONE Consider the accompanying data on flexural strength (MPa) for concrete beams of a certain type. 5.9 7.2 7.3 6.3 8.1 6.8 7.0 7.5 6.8 6.5 7.0 6.3 7.9 9.0 8.4 8.7 7.8 9.7 7.4 7.7 9.7 8.2 7.7 11.6 11.3 11.8 10.7 The data below give accompanying strength observations for cylinders. 6.5 5.8 7.8 7.1 7.2 9.2 6.6 8.3 7.0 8.3 7.8 8.1 7.4 8.5 8.9 9.8 9.7 14.1 12.6 11.9 Prior to obtaining data, denote the beam strengths by X1, . . . , Xm and the cylinder strengths by Y1, . . . , Yn. Suppose that the Xi's constitute a random sample from a distribution with mean μ1 and standard deviation σ1 and that the Yi's form a random sample (independent of the Xi's) from another distribution with mean μ2 and standard deviation σ2. (a) Use rules of expected value to show that X − Y is an unbiased estimator of μ1 − μ2. E(X − Y) = E(X) − E(Y) = μ1 − μ2 E(X − Y) = E(X) − E(Y) 2 = μ1 − μ2 E(X − Y) = nm E(X) − E(Y) = μ1 − μ2 E(X −…
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