Introduction to Probability and Statistics for Engineers and Scientists, Fifth Edition is a proven text reference that provides a superior introduction to applied probability and statistics for engineering or science majors. The book lays emphasis in the manner in which probability yields insight into statistical problems, ultimately resulting in an intuitive understanding of the statistical procedures most often used by practicing engineers and scientists. Real data from actual studies across life science, engineering, computing and business are incorporated in a wide variety of exercises and examples throughout the text. These examples and exercises are combined with updated problem sets and applications to connect probability theory to everyday statistical problems and situations. The book also contains end of chapter review material that highlights key ideas as well as the risks associated with practical application of the material. Furthermore, there are new additions to proofs in the estimation section as well as new coverage of Pareto and lognormal distributions, prediction intervals, use of dummy variables in multiple regression models, and testing equality of multiple population distributions. This text is intended for upper level undergraduate and graduate students taking a course in probability and statistics for science or engineering, and for scientists, engineers, and other professionals seeking a reference of foundational content and application to these fields. - Clear exposition by a renowned expert author - Real data examples that use significant real data from actual studies across life science, engineering, computing and business - End of Chapter review material that emphasizes key ideas as well as the risks associated with practical application of the material - 25% New Updated problem sets and applications, that demonstrate updated applications to engineering as well as biological, physical and computer science - New additions to proofs in the estimation section - New coverage of Pareto and lognormal distributions, prediction intervals, use of dummy variables in multiple regression models, and testing equality of multiple population distributions.
Dr. Sheldon M. Ross is a professor in the Department of Industrial and Systems Engineering at the University of Southern California. He received his PhD in statistics at Stanford University in 1968. He has published many technical articles and textbooks in the areas of statistics and applied probability. Among his texts are A First Course in Probability, Introduction to Probability Models, Stochastic Processes, and Introductory Statistics. Professor Ross is the founding and continuing editor of the journal Probability in the Engineering and Informational Sciences. He is a Fellow of the Institute of Mathematical Statistics, a Fellow of INFORMS, and a recipient of the Humboldt US Senior Scientist Award.
Ross
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Setting equal to 0 gives i=1nxi+?i=1nwi=2nµ1+nµ2?i=1nyi+?i=1nwi=nµ1+2nµ2 yielding ^1=2Sxi+Swi-Syi3n,µ^2=2Syi+Swi-Sxi3n 6. The average of the distances is i50.456, and that of the angles is 40.27. Using these estimates the length of the tower, call it T, is estimated as follows: =Xtan?˜127.461 7. With Y = log(X), then X = eY. Because Y is normal with parameters µ and s2 X=EeY=eµ+s2/2EX2=Ee2Y=e2µ+2s2 giving that X=e2µ+2s2-e2µ+s2 (c) Taking the sample mean and variance of the logs of the data, yields the estimates that ^=3.7867,s^2=.0647. Hence, the estimate of E[X] is µ^+s^2/2=45.561. 8. ¯=3.1502 (a). ±1.96.1/5=3.06253.2379 (b). ±12.58.1/5=3.03483.2656 9. ¯=11.48 (a) ±1.96.08/10=11.48±.0496 (b) 8,11.48±1.645.08/10=-8,11.5216 (c) -1.645.08/10,8=11.43848 10. 74.6 ± 1.645(11.3)/9 = 74.6 ± 2.065 = (72.535, 76.665) 11. (a) Normal with mean 0 and variance 1 + 1/n (b) With probability ,-1.64?1-a,2n2}=1-a it follows that the lower confidence interval is given by <2?Xi/?1-a,2n2 Similarly, a 100(1 – a) percent upper confidence interval for ? is <2?Xi/?a,2n2 60. Since Var[(n - 1)Sx2/s2] = 2(n - 1) it follows that Var(Sx2) = 2s4/(n - 1) and similarly Var(Sy2) = 2s4/(n - 1). Hence, using Example 5.5b which shows that the best weights are inversely proportional to the variances, it follows that the pooled estimator is best. 61. As the risk of d1 is 6 whereas that of d2 is also 6 they are equally good. 62. Since the number of accidents over the next 10 days is Poisson with mean 10 k it follows that P{83|?} = e- 10?(10?)83/83!. Hence, ?|83=P83|?e-??P83|?e-?d?=c?83e-11? where c does not depend on ?. Since this is the gamma density with parameters 84.11 it has mean 84/11 = 7.64 which is thus the Bayes estimate. The maximum likelihood estimate is 8.3. (The reason that the Bayes estimate is smaller is that it incorporates our initial belief that ? can be thought of as being the value of an exponential random variable with mean 1.) 63. ?|x1…xn=fx1…xn|?g?/c=c?ne-?Sxie-??2=c?n+2e-?1+Sxi
where c × p(x1 … xn) does not depend on ?. Thus we see that the posterior distribution of ? is the gamma distribution with parameters n + 3.1 + Sxi: and so the Bayes estimate is (n + 3)/(1 + Sxi), the mean of the posterior distribution. In our problem this yields the estimate 23/93. 64. The posterior density of p is, from Equation (5.5.2) f (p|data) = 11!pi(1 - p)10 - i/1!(10 - i)! where i is the number of defectives in the sample of 10. In all cases the desired probability is obtained by integrating this density from p equal 0 to p equal .2. This has to be done numerically as the above does not have a closed form integral. 65 The posterior distribution is normal with mean 80/89(182) + 9/89(200) = 183.82 and...
Ross, Sheldon M.
Sheldon M. Ross is a professor in the Department of Industrial Engineering and Operations Research at the University of Southern California. He received his Ph.D. in statistics at Stanford University in 1968. He has published many technical articles and textbooks in the areas of statistics and applied probability. Among his texts are A First Course in Probability, Introduction to Probability Models, Stochastic Processes, and Introductory Statistics. Professor Ross is the founding and continuing editor of the journal <i>Probability in the Engineering and Informational Sciences</i>. He is a Fellow of the Institute of Mathematical Statistics, and a recipient of the Humboldt US Senior Scientist Award.
