Solutions Manual - for Engineering and the Sciences NINTH EDITION ...

Solutions Manual Probability and Statistics for Engineering and the Sciences

NINTH EDITION

Jay Devore Prepared by Matthew A. Carlton

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Chapter 1 1 Chapter 2 48 Chapter 3 9

Chapter 4 126 Chapter 5 177 Chapter 6 206 Chapter 7 217 Chapter 8 234 Chapter 9 255 Chapter 10 285 Chapter 11 29 9 Chapter 12 330 Chapter 13 368 Chapter 14 40 6 Chapter 15 42 4 Chapter 16

CONTENTS

Overview and Descriptive Statistics Probability Discrete Rando m Variables and Probab ility Distributions Continuous Random Variables and Probability Distributions Joint Probability Distributions and Random Samples Point Estimation Statistical Intervals Based on a Single Sample Tests of Hyp otheses Based on a Single Sample Inferences Based on Two Samples The Analysis of Variance Multifacto r Analysis of Variance Simple Linear Regression and Correlation Nonlinear and Multiple Regression Goodness-o f-Fit Tests and Categorical Data Analysis Distributio n-Free Procedures Quality Co ntrol Methods 43

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1

CHAPTER 1

Section 1.1

1.

• Los Angeles Times, Oberlin Tribune, Gainesville Sun, Washington Post

• Duke Energy, Clorox, Seagate, Neiman Marcus

• Vince Correa, Catherine Miller, Michael Cutler, Ken Lee

• 2.97, 3.56, 2.20, 2.97

2.

• 29.1 yd, 28.3 yd, 24.7 yd, 31.0 yd

• 432 pp, 196 pp, 184 pp, 321 pp

• 2.1, 4.0, 3.2, 6.3

• 0.07 g, 1.58 g, 7.1 g, 27.2 g

3.

• How likely is it that more than half of the sampled computers will need or have needed

warranty service? What is the expected number among the 100 that need warranty service? How likely is it that the number needing warranty service will exceed the expected number by more than 10?

• Suppose that 15 of the 100 sampled needed warranty service. How confident can we be

that the proportion of all such computers needing warranty service is between .08 and .22? Does the sample provide compelling evidence for concluding that more than 10% of all such computers need warranty service?

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Chapter 1: Overview and Descriptive Statistics

2 4.

• Concrete populations: all living U.S. Citizens, all mutual funds marketed in the U.S., all

books published in 1980 Hypothetical populations: all grade point averages for University of California undergraduates during the next academic year, page lengths for all books published during the next calendar year, batting averages for all major league players during the next baseball season

• (Concrete) Probability: In a sample of 5 mutual funds, what is the chance that all 5 have

rates of return which exceeded 10% last year?Statistics: If previous year rates-of-return for 5 mutual funds were 9.6, 14.5, 8.3, 9.9 and 10.2, can we conclude that the average rate for all funds was below 10%?(Hypothetical) Probability: In a sample of 10 books to be published next year, how likely is it that the average number of pages for the 10 is between 200 and 250?Statistics: If the sample average number of pages for 10 books is 227, can we be highly confident that the average for all books is between 200 and 245?

5.

• No. All students taking a large statistics course who participate in an SI program of this

sort.

• The advantage to randomly allocating students to the two groups is that the two groups

should then be fairly comparable before the study. If the two groups perform differently in the class, we might attribute this to the treatments (SI and control). If it were left to students to choose, stronger or more dedicated students might gravitate toward SI, confounding the results.

• If all students were put in the treatment group, there would be no firm basis for assessing

the effectiveness of SI (nothing to which the SI scores could reasonably be compared).

• One could take a simple random sample of students from all students in the California State

University system and ask each student in the sample to report the distance form their hometown to campus. Alternatively, the sample could be generated by taking a stratified random sample by taking a simple random sample from each of the 23 campuses and again asking each student in the sample to report the distance from their hometown to campus.Certain problems might arise with self reporting of distances, such as recording error or poor recall. This study is enumerative because there exists a finite, identifiable population of objects from which to sample.

• One could generate a simple random sample of all single-family homes in the city, or a

stratified random sample by taking a simple random sample from each of the 10 district neighborhoods. From each of the selected homes, values of all desired variables would be determined. This would be an enumerative study because there exists a finite, identifiable population of objects from which to sample.

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