(2) Solve Chapter 2, Problem 2 (30 points). Using phstat, generate box plot and dot scale diagram for all numeric columns (10 points). Use data below. Insurance Survey Age Gender Education Marital Status Years Employed Satisfaction* Premium/Deductible** 36 F Some college Divorced 4 4 N 55 F Some college Divorced 2 1 N 61 M Graduate degree Widowed 26 3 N 65 F Some college Married 9 4 N 53 F Graduate degree Married 6 4 N 50 F Graduate degree Married 10 5 N 28 F College graduate Married 4 5 N 62 F College graduate Divorced 9 3 N 48 M Graduate degree Married 6 5 N 31 M Graduate degree Married 1 5 N z57 F College graduate Married 4 5 N 44 M College graduate Married 2 3 N 38 M Some college Married 3 2 N 27 M Some college Married 2 3 N 56 M Graduate degree Married 4 4 Y 43 F College graduate Married 5 3 Y 45 M College graduate Married 15 3 Y 42 F College graduate Married 12 3 Y 29 M Graduate degree Single 10 5 N 28 F Some college Married 3 4 Y 36 M Some college Divorced 15 4 Y 49 F Graduate degree Married 2 5 N 46 F College graduate Divorced 20 4 N 52 F College graduate Married 18 2 N *Measured from 1-5 with 5 being highly satisfied. **Would you be willing to pay a lower premium for a higher deductible? (3) Chapter 3 – Solve Problem 23. (10 x 4 = 40 points) (4) Probability Distributions. 4.1. Generate 500 random numbers between 1 and 100 using RANDBETWEEN function. (10 points) 4.2. Plot the theoretical normal and weibull probability distributions, as line plots, for range 0 to 150 based on your 500 random numbers. For Weibull distribution, use alpha = 1.8 and beta = mean of 500 values – 10. (20 points) 4.3 Show calculations and tables used to plot graphs/figures. (10 points) (5) Joint Probability. Solve problem 35 from chapter 3. Use data below. Joint probability calculations (10 points). a, b and c (30 points) Note: Round all decimal values to 3 digits of precision. Never Married Married, spouse present Married, spouse absent Separated Widowed Divorced Not a High School Grad 4120320 High School Graduate 7777104 Some College No Degree 4789872 Associate’s Degree 1828392 Bachelor’s Degree 5124648 Advanced Degree 2137416 15516160 36382720 18084352 8346624 19154432 9523712 1847880 1188090 5145683 2968680 2368024 1667010 4670488 7003040 1184012 842715 1765010 3806000 465392 336165 556657 1674640 670712 405240 977544 2340690 301136 165780 475195 1217920 Note: For all questions, show screenshots of equations/formulae used. Explain work. PHStat Notes Using the PHStat Stack Data and Unstack Data Tools One‐ and Two‐Way Tables and Charts Normal Probability Tools Generating Probabilities in PHStat Confidence Intervals for the Mean Confidence Intervals for Proportions Confidence Intervals for the Population Variance Determining Sample Size One‐Sample Test for the Mean, Sigma Unknown One‐Sample Test for Proportions Using Two‐Sample t‐Test Tools Testing for Equality of Variances Chi‐Square Test for Independence Using Regression Tools Stepwise Regression Best-Subsets Regression Creating x‐ and R‐Charts Creating p‐Charts Using the Expected Monetary Value Tool p. p. p. p. p. p. p. p. p. p. p. p. p. p. p. p. p. p. p. 28 63 97 98 136 136 137 137 169 169 169 170 171 209 211 212 267 268 375 p. p. p. p. p. p. p. p. p. p. p. p. p. p. p. p. p. p. p. p. p. p. 29 61 61 62 63 63 134 135 135 171 209 209 211 243 243 244 298 298 298 299 299 375 p. p. p. p. p. p. 338 339 339 341 341 342 Excel Notes Creating Charts in Excel 2010 Creating a Frequency Distribution and Histogram Using the Descriptive Statistics Tool Using the Correlation Tool Creating Box Plots Creating PivotTables Excel‐Based Random Sampling Tools Using the VLOOKUP Function Sampling from Probability Distributions Single‐Factor Analysis of Variance Using the Trendline Option Using Regression Tools Using the Correlation Tool Forecasting with Moving Averages Forecasting with Exponential Smoothing Using CB Predictor Creating Data Tables Data Table Dialog Using the Scenario Manager Using Goal Seek Net Present Value and the NPV Function Using the IRR Function Crystal Ball Notes Customizing Define Assumption Sensitivity Charts Distribution Fitting with Crystal Ball Correlation Matrix Tool Tornado Charts Bootstrap Tool TreePlan Note Constructing Decision Trees in Excel p. 376 This page intentionally left blank Useful Statistical Functions in Excel 2010 Description AVERAGE(data range) BINOM.DIST(number_s, trials, probability_s, cumulative) BINOM.INV(trials, probability_s, alpha) Computes the average value (arithmetic mean) of a set of data. Returns the individual term binomial distribution. Returns the smallest value for which the cumulative binomial distribution is greater than or equal to a criterion value. Returns the left-tailed probability of the chi-square distribution. Returns the right-tailed probability of the chi-square distribution. Returns the test for independence; the value of the chi-square distribution and the appropriate degrees of freedom. Returns the confidence interval for a population mean using a normal distribution. Returns the confidence interval for a population mean using a t-distribution. Computes the correlation coefficient between two data sets. Returns the exponential distribution. Returns the left-tailed F-probability distribution value. Returns the left-tailed F-probability distribution value. Calculates a future value along a linear trend. Calculates predicted exponential growth. Returns an array that describes a straight line that best fits the data. Returns the cumulative lognormal distribution of x, where ln (x) is normally distributed with parameters mean and standard deviation. Computes the median (middle value) of a set of data. Computes the modes (most frequently occurring values) of a set of data. Computes the mode of a set of data. Returns the normal cumulative distribution for the specified mean and standard deviation. Returns the inverse of the cumulative normal distribution. Returns the standard normal cumulative distribution (mean = 0, standard deviation = 1). Returns the inverse of the standard normal distribution. Computes the kth percentile of data in a range, exclusive. Computes the kth percentile of data in a range, inclusive. Returns the Poisson distribution. Computes the quartile of a distribution. Computes the skewness, a measure of the degree to which a distribution is not symmetric around its mean. Returns a normalized value for a distribution characterized by a mean and standard deviation. Computes the standard deviation of a set of data, assumed to be a sample. Computes the standard deviation of a set of data, assumed to be an entire population. Returns values along a linear trend line. Returns the left-tailed t-distribution value. Returns the two-tailed t-distribution value. Returns the right-tailed t-distribution. Returns the left-tailed inverse of the t-distribution. Returns the two-tailed inverse of the t-distribution. Returns the probability associated with a t-test. Computes the variance of a set of data, assumed to be a sample. Computes the variance of a set of data, assumed to be an entire population. Returns the two-tailed p-value of a z-test. CHISQ.DIST(x, deg_freedom, cumulative) CHISQ.DIST.RT(x, deg_freedom, cumulative) CHISQ.TEST(actual_range, expected_range) CONFIDENCE.NORM(alpha, standard_dev, size) CONFIDENCE.T(alpha, standard_dev, size) CORREL(arrayl, array2) EXPON.DIST(x, lambda, cumulative) F.DIST(x. deg_freedom1, deg_freedom2, cumulative) F.DIST.RT(x. deg_freedom1, deg_freedom2, cumulative) FORECAST(x, known_y’s, known_x’s) GROWTH(known_y’s, known_x’s, new_x’s, constant) LINEST(known_y’s, known_x’s, new_x’s, constant, stats) LOGNORM.DIST(x, mean, standard_deviation) MEDIAN(data range) MODE.MULT(data range) MODE.SNGL(data range) NORM.DIST(x, mean, standard_dev, cumulative) NORM.INV(probability, mean, standard_dev) NORM.S.DIST(z) NORM.S.INV(probability) PERCENTILE.EXC(array, k) PERCENTILE.INC(array, k) POISSON.DIST(x, mean, cumulative) QUARTILE(array, quart) SKEW(data range) STANDARDIZE(x, mean, standard_deviation) STDEV.S(data range) STDEV.P(data range) TREND(known_y’s, known_x’s, new_x’s, constant) T.DIST(x, deg_freedom, cumulative) T.DIST.2T(x, deg_freedom) T.DIST.RT(x, deg_freedom) T.INV(probability, deg_freedom) T.INV.2T(probability, deg_freedom) T.TEST(arrayl, array2, tails, type) VAR.S(data range) VAR.P(data range) Z.TEST(array, x, sigma) This page intentionally left blank Fifth Edition STATISTICS, DATA ANALYSIS, AND DECISION MODELING James R. Evans University of Cincinnati Boston Columbus Indianapolis New York San Francisco Upper Saddle River Amsterdam Cape Town Dubai London Madrid Milan Munich Paris Montreal Toronto Delhi Mexico City São Paulo Sydney Hong Kong Seoul Singapore Taipei Tokyo Editorial Director: Sally Yagan Editor in Chief: Donna Battista Senior Acquisitions Editor: Chuck Synovec Editorial Project Manager: Mary Kate Murray Editorial Assistant: Ashlee Bradbury Director of Marketing: Maggie Moylan Executive Marketing Manager: Anne Fahlgren Production Project Manager: Renata Butera Operations Specialist: Renata Butera Creative Art Director: Jayne Conte Cover Designer: Suzanne Duda Manager, Rights and Permissions: Hessa Albader Cover Art: pedrosek/Shutterstock Images Media Project Manager: John Cassar Media Editor: Sarah Peterson Full-Service Project Management: Shylaja Gatttupalli Composition: Jouve India Pvt Ltd Printer/Binder: Edwards Brothers Cover Printer: Lehigh-Phoenix Color/Hagerstown Text Font: Palatino Credits and acknowledgments borrowed from other sources and reproduced, with permission, in this textbook appear on the appropriate page within text. 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This publication is protected by Copyright, and permission should be obtained from the publisher prior to any prohibited reproduction, storage in a retrieval system, or transmission in any form or by any means, electronic, mechanical, photocopying, recording, or likewise. To obtain permission(s) to use material from this work, please submit a written request to Pearson Education, Inc., Permissions Department, One Lake Street, Upper Saddle River, New Jersey 07458, or you may fax your request to 201-236-3290. Many of the designations by manufacturers and sellers to distinguish their products are claimed as trademarks. Where those designations appear in this book, and the publisher was aware of a trademark claim, the designations have been printed in initial caps or all caps. Library of Congress Cataloging-in-Publication Data Evans, James R. (James Robert) Statistics, data analysis, and decision modeling / James R. Evans. —5th ed. p. cm. ISBN-13: 978-0-13-274428-7 ISBN-10: 0-13-274428-7 1. Industrial management—Statistical methods. 2. Statistical decision. I. Title. HD30.215.E93 2012 658.4r033—dc23 2011039310 10 9 8 7 6 5 4 3 2 1 ISBN 10: 0-13-274428-7 ISBN 13: 978-0-13-274428-7 To Beverly, Kristin, and Lauren, the three special women in my life. —James R. Evans This page intentionally left blank BRIEF CONTENTS PART I Statistics and Data Analysis 1 Chapter 1 Chapter 2

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