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APPLET CORRELATION Applet Concept Illustrated Description Applet Activity Sample from a population Assesses how well a sample represents the population and the role that sample size plays in the process Produces random sample from population from specified sample size and population distribution shape Reports mean, median, and standard deviation; applet creates plot of sample 4.4, 240; 5.1, 355; 5.3, 279 Sampling distributions Compares means and standard deviations of distributions; assesses effect of sample size; illustrates unbiasedness Simulates repeatedly choosing samples of a fixed size n from a population with specified sample size, number of samples, and shape of population distribution Applet reports means, medians, and standard deviations; creates plots for both 6.1, 330; 6.2, 330 Random numbers Uses a random number generator to determine the experimental units to be included in a sample Generates random numbers from a range of integers specified by the user 1.1, 47; 1.2, 48; 3.6, 203; 4.1, 221; 5.2, 265 Long-run probability demonstrations illustrate the concept that theoretical probabilities are long-run experimental probabilities Simulating probability of rolling a Investigates relationship between theoretical Reports and creates frequency histogram for and experimental probabilities of rolling as each outcome of each simulated roll of a fair number of die rolls increases die Students specify number of rolls; applet calculates and plots proportion of 6s 3.1, 157; 3.2, 157; 3.3, 168; 3.4, 169; 3.5, 183 Simulating probability of rolling a or Investigates relationship between theoretical Reports outcome of each simulated roll and experimental probabilities of rolling or of a fair die; creates frequency histogram for as number of die rolls increases outcomes Students specify number of rolls; applet calculates and plots proportion of 3s and 4s 3.3, 168; 3.4, 169 Simulating the probability of heads: fair coin Investigates relationship between theoretical Reports outcome of each fair coin flip and cre- 4.2, 221 and experimental probabilities of getting ates a bar graph for outcomes Students specify heads as number of fair coin flips increases number of flips; applet calculates and plots proportion of heads Simulating probability of heads: unfair coin (P(H) = 2) Investigates relationship between theoretical and experimental probabilities of getting heads as number of unfair coin flips increases Reports outcome of each flip for a coin where 4.3, 239 heads is less likely to occur than tails and creates a bar graph for outcomes Students specify number of flips; applet calculates and plots the proportion of heads Simulating probability of heads: unfair coin (P(H) = 8) Investigates relationship between theoretical and experimental probabilities of getting heads as number of unfair coin flips increases Reports outcome of each flip for a coin where 4.3, 239 heads is more likely to occur than tails and creates a bar graph for outcomes Students specify number of flips; applet calculates and plots the proportion of heads Simulating the stock market Theoretical probabilities are long run experimental probabilities Simulates stock market fluctuation Students 4.5, 240 specify number of days; applet reports whether stock market goes up or down daily and creates a bar graph for outcomes Calculates and plots proportion of simulated days stock market goes up Mean versus median Investigates how skewedness and outliers affect measures of central tendency Students visualize relationship between mean and median by adding and deleting data points; applet automatically updates mean and median 2.1, 89; 2.2, 89; 2.3, 89 Applet Concept Illustrated Description Applet Activity Standard deviation Investigates how distribution shape and spread affect standard deviation Students visualize relationship between mean 2.4, 96; 2.5, 97; 2.6, 97; 2.7, 119 and standard deviation by adding and deleting data points; applet updates mean and standard deviation Confidence intervals for a proportion Not all confidence intervals contain the population proportion Investigates the meaning of 95% and 99% confidence Simulates selecting 100 random samples from the population and finds the 95% and 99% confidence intervals for each Students specify population proportion and sample size; applet plots confidence intervals and reports number and proportion containing true proportion 7.5, 369; 7.6, 370 Confidence intervals for a mean (the impact of confidence level) Not all confidence intervals contain the population mean Investigates the meaning of 95% and 99% confidence Simulates selecting 100 random samples from population; finds 95% and 99% confidence intervals for each Students specify sample size, distribution shape, and population mean and standard deviation; applet plots confidence intervals and reports number and proportion containing true mean 7.1, 351; 7.2, 351 Confidence intervals for a mean (not knowing standard deviation) Confidence intervals obtained using the sample standard deviation are different from those obtained using the population standard deviation Investigates effect of not knowing the population standard deviation Simulates selecting 100 random samples from 7.3, 361; 7.4, 361 the population and finds the 95% z-interval and 95% t-interval for each Students specify sample size, distribution shape, and population mean and standard deviation; applet plots confidence intervals and reports number and proportion containing true mean Hypothesis tests for a proportion Not all tests of hypotheses lead correctly to either rejecting or failing to reject the null hypothesis Investigates the relationship between the level of confidence and the probabilities of making Type I and Type II errors Simulates selecting 100 random samples from population; calculates and plots z-statistic and P-value for each Students specify population proportion, sample size, and null and alternative hypotheses; applet reports number and proportion of times null hypothesis is rejected at 0.05 and 0.01 levels Hypothesis tests for a mean Not all tests of hypotheses lead correctly to either rejecting or failing to reject the null hypothesis Investigates the relationship between the level of confidence and the probabilities of making Type I and Type II errors Simulates selecting 100 random samples from 8.1, 407; 8.2, 417; 8.3, 417; population; calculates and plots t statistic and 8.4, 417 P-value for each Students specify population distribution shape, mean, and standard deviation; sample size, and null and alternative hypotheses; applet reports number and proportion of times null hypothesis is rejected at both 0.05 and 0.01 levels Correlation by eye Correlation coefficient measures strength of linear relationship between two variables Teaches user how to assess strength of a linear relationship from a scattergram Computes correlation coefficient r for a set 11.2, 652 of bivariate data plotted on a scattergram Students add or delete points and guess value of r; applet compares guess to calculated value Regression by eye The least squares regression line has a smaller SSE than any other line that might approximate a set of bivariate data Teaches students how to approximate the location of a regression line on a scattergram Computes least squares regression line for a set of bivariate data plotted on a scattergram Students add or delete points and guess location of regression line by manipulating a line provided on the scattergram; applet plots least squares line and displays the equations and the SSEs for both lines 8.5, 433; 8.6, 434 11.1, 625 Get the Most Out of Pearson MyLab Statistics Pearson MyLab Statistics, Pearson’s online tutorial and assessment tool, creates personalized experiences for students and provides powerful tools for instructors With a wealth of tested and proven resources, each course can be tailored to fit your specific needs Talk to your Pearson Representative about ways to integrate Pearson MyLab Statistics into your course for the best results Data-Driven Reporting for Instructors • Pearson MyLab Statistics’ comprehensive online gradebook automatically tracks students’ results to tests, quizzes, homework, and work in the study plan • The Reporting Dashboard makes it easier than ever to identify topics where students are struggling, or specific students who may need extra help Learning in Any Environment • Because classroom formats and student needs continually change and evolve, Pearson MyLab Statistics has built-in flexibility to accommodate various course designs and formats • With a new, streamlined, mobile-friendly design, students and instructors can access courses from most mobile devices to work on exercises and review completed assignments Visit www.mystatlab.com and click Get Trained to make sure you’re getting the most out of Pearson MyLab Statistics Available in Pearson MyLab Statistics for Your Introductory Statistics Courses Pearson MyLab Statistics is the market-leading online resource for learning and teaching statistics Leverage the Power of StatCrunch Pearson MyLab Statistics leverages the power of StatCrunch–powerful, web-based statistics software Integrated into Pearson MyLab Statistics, students can easily analyze data from their exercises and etext In addition, access to the full online community allows users to take advantage of a wide variety of resources and applications at www.statcrunch.com Bring Statistics to Life Virtually flip coins, roll dice, draw cards, and interact with animations on your mobile device with the extensive menu of experiments and applets in StatCrunch Offering a number of ways to practice resampling procedures, such as permutation tests and bootstrap confidence intervals, StatCrunch is a complete and modern solution Real-World Statistics Pearson MyLab Statistics video resources help foster conceptual understanding StatTalk Videos, hosted by fun-loving statistician Andrew Vickers, demonstrate important statistical concepts through interesting stories and real-life events This series of 24 videos includes assignable questions built in Pearson MyLab Statistics and an instructor’s guide www.mystatlab.com This page intentionally left blank ThirTeenTh ediTion GLoBAL ediTion James T McClave Terry Sincich Info Tech, Inc University of South Florida University of Florida 330 Hudson Street, NY NY 10013 Editorial Director: Chris Hoag Editor in Chief: Deirdre Lynch Acquisitions Editor: Patrick Barbera Acquisitions Editor, Global Edition: Sourabh Maheshwari Editorial Assistant: Justin Billing Program Manager: Tatiana Anacki Project Manager: Christine O’Brien Assistant Project Editor, Global Edition: Aurko Mitra Program Management Team Lead: Karen Wernholm Project Management Team Lead: Peter Silvia Senior Manufacturing Controller, Global Edition: Kay Holman Media Producer: Jean Choe Media Production Manager, Global Edition: Vikram Kumar TestGen Content Manager: John Flanagan 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Statistics, Pearson MyLab Statistics Plus, Pearson MathXL, LEARNING CATALYTICS, AND TESTGEN are exclusive trademarks owned by Pearson Education, Inc or its affiliates in the U.S and/or other countries Pearson Education Limited KAO Two KAO Park Harlow CM17 9NA United Kingdom and Associated Companies throughout the world Visit us on the World Wide Web at: www.pearsonglobaleditions.com © Pearson Education Limited 2018 The rights of James T McClave and Terry T Sincich to be identified as the authors of this work have been asserted by them in accordance with the Copyright, Designs and Patents Act 1988 Authorized adaptation from the United States edition, entitled Statistics, 13th Edition, ISBN 978-0-13-408021-5 by James T McClave and Terry T Sincich, published by Pearson Education © 2017 All rights reserved No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without either the prior written permission of the publisher or a license permitting restricted copying in the United Kingdom issued by the Copyright Licensing Agency Ltd, Saffron House, 6–10 Kirby Street, London EC1N 8TS All trademarks used herein are the property of their respective owners The use of any trademark in this text does not vest in the author or publisher any trademark ownership rights in such trademarks, nor does the use of such trademarks imply any affiliation with or endorsement of this book by such owners ISBN 10: 1-292-16155-8 ISBN 13: 978-1-292-16155-6 British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library 10 Typeset by Integra Printed and bound by Vivar in Malaysia Contents Preface 13 Applications Index Chapter 21 Statistics, data, and Statistical Thinking 29 1.1 The Science of Statistics 30 1.2 Types of Statistical Applications 31 1.3 Fundamental Elements of Statistics 1.4 Types of Data 1.5 Collecting Data: Sampling and Related Issues 39 1.6 The Role of Statistics in Critical Thinking and Ethics 44 33 37 Statistics in Action: Social Media Network Usage—Are You Linked In? 30 Using Technology: MINITAB: Accessing and Listing Data 53 Chapter Methods for describing Sets of data 57 2.1 Describing Qualitative Data 59 2.2 Graphical Methods for Describing Quantitative Data 2.3 Numerical Measures of Central Tendency 2.4 Numerical Measures of Variability 2.5 Using the Mean and Standard Deviation to Describe Data 2.6 Numerical Measures of Relative Standing 107 2.7 Methods for Detecting Outliers: Box Plots and z-Scores 2.8 Graphing Bivariate Relationships (Optional) 121 2.9 Distorting the Truth with Descriptive Statistics 82 93 99 111 126 Statistics in Action: Body Image Dissatisfaction: Real or Imagined? Using Technology: MINITAB: Describing Data 70 58 142 TI-83/TI–84 Plus Graphing Calculator: Describing Data 142 Chapter Probability 145 3.1 Events, Sample Spaces, and Probability 147 3.2 Unions and Intersections 160 3.3 Complementary Events 3.4 The Additive Rule and Mutually Exclusive Events 3.5 Conditional Probability 3.6 The Multiplicative Rule and Independent Events 175 163 165 172 CONTENTS 3.7 Some Additional Counting Rules (Optional) 3.8 Bayes’s Rule (Optional) 197 187 Statistics in Action: Lotto Buster! Can You Improve Your Chance of Winning? 146 Using Technology: TI-83/TI-84 Plus Graphing Calculator: Combinations and Permutations 211 Chapter discrete random Variables 212 4.1 Two Types of Random Variables 214 4.2 Probability Distributions for Discrete Random Variables 4.3 Expected Values of Discrete Random Variables 4.4 The Binomial Random Variable 4.5 The Poisson Random Variable (Optional) 242 4.6 The Hypergeometric Random Variable (Optional) 247 217 224 229 Statistics in Action: Probability in a Reverse Cocaine Sting: Was Cocaine Really Sold? 213 Using Technology: MINITAB: Discrete Probabilities 257 TI-83/TI-84 Plus Graphing Calculator: Discrete Random Variables and Probabilities 257 Chapter Continuous random Variables 260 5.1 Continuous Probability Distributions 262 5.2 The Uniform Distribution 263 5.3 The Normal Distribution 5.4 Descriptive Methods for Assessing Normality 5.5 Approximating a Binomial Distribution with a Normal Distribution (Optional) 5.6 267 281 290 The Exponential Distribution (Optional) 295 Statistics in Action: Super Weapons Development—Is the Hit Ratio Optimized? 261 Using Technology: MINITAB: Continuous Random Variable Probabilities and Normal Probability Plots 307 TI-83/TI-84 Plus Graphing Calculator: Normal Random Variable and Normal Probability Plots 308 Chapter Sampling distributions 310 6.1 The Concept of a Sampling Distribution 312 6.2 Properties of Sampling Distributions: Unbiasedness and Minimum Variance 319 6.3 The Sampling Distribution of xQ and the Central Limit Theorem 6.4 The Sampling Distribution of the Sample Proportion Statistics in Action: The Insomnia Pill: Is It Effective? 311 Using Technology: MINITAB: Simulating a Sampling Distribution 341 332 323 www.downloadslide.net Credits Chapter p 32 Source: Human Factors, Vol 56, No 31, May 2014; p 32 Source: Academy of Management Journal, Oct 2007; p 49 James T McClave, Terry Sincich; pp 53–56 Screenshots from Minitab Corporation Courtesy of Minitab Corporation Chapter p 59 Based on Li E.C, Williams S.F and Volpe R.D, “The Effects of Topic and Listener Familiarity of Discourse Variables in Procedural and Narrative Discource Tasks”, The Journal of Communication Disorders, Vol: 28, No: 01, Mar 1995, Page: 44 (Table 1) p 60 Summary Table from SPSS Reprint Courtesy of International Business Machines Corporation, © International Business Machines Corporation; p 67 Based on Journal of Moral Education, March 2010; p 67 Source: International Rhino Foundation, 2014; pp 67–68 Based on Sangkuk Lee, Journal of Family History (Jan 2010); p 68 Screenshot from SAS © 1999 SAS In stitute Inc Reprinted with Permission; p. 68 Source: Union of Concerned Scientists, Nov 2012; p 68 Source: Wu J, et al Track Allocation Optimization at a Railway Station: Mean-Variation Model and Case Study, Journal of Transportation Engineering, Vol: 39, No: 5, May 2013 (Extracted from Table 4); p 69 Source: Braga A.A, Hureau D.M & Papachristos A.V, Deterring Gang-Involved Gun Violence: Measuring the Impact of Boston’s Operation Ceasefire on Street Gang Behaviour, Journal of Quantitative Criminology, Vol: 30, No: 01, March 2014 (adapted from Figure 3); p 69 Source: McMunn-Dooley C & Welch M.M, Nature of Interactions Among Young Children and Adult Caregivers in a Children’s Museum, Early Childhood Education Journal, Vol: 42, No: 02, March 2014 (adapted from Figure 2); p 69 Source: Roskes M et al., The Right Side? Under Time Pressure, Approach Motivation Leads to Right Oriented Bias, Psychological Science, Vol: 22, No: 11, Nov 2011 (adapted from Figure 2); p 70 Based on Ayotte J.D, Argue D.M and McGarry F.J, Methyl Tert-Butyl Ether Occurrence and Related Factors in Public and Private Wells in Southeast New Hampshire, Environmental Science and Technology, Vol: 39, No: 1, Jan 2005, Page: 9–16; p 78 Based on Boyer D.M, Evans A.R and Jernvall J, Evidence of Dietary Differentiation Among Late PaleoceneEarly Eocene Plesiadapids (Mammalia, Primates), American Journal of Physical Anthropology, Vol: 142, © 2010 (Table A3); p 78 Source: Harell K, The Fallen and Their Swords: A New Explanation for the Rise of the Shaft Graves, American Journal of Archaeology, Vol: 118, No: 1, January 2014 (Figure 1); p 78 Source: Patson T, Teaching Stage Fright? 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Implications for Music Educators, British Journal of Music Education, Vol: 31, No: 1, March 2014 (Adapted from Figure 1); p. 90 Based on Paul I.M, et al Effect of Honey, Dextromethorphan, and no Treatment on Nocturnal Cough and Sleep Quality for Coughing and their Parents, Archives of Pediatrics and Adolescent Medicine, Vol: 161, No: 12, Dec 2007 (Data Simulated); p 90 Based on Boyer D.M, Evans A.R and Jernvall J, Evidence of Dietary Differentiation Among Late Paleocene-Early Eocene Plesiadapids (Mammalia, Primates), American Journal of Physical Anthropology, Vol: 142, © 2010 (Table A3); pp 90–91 Based on Wiseman F, et al A New Method for Ranking Total Driving Perfrmance on the PGA Tour, Sports Journal, Vol: 10, No: 1, Winter 2007 (Table 2); p 91 Based on Pfeiffer M et al Community Organization and Species Richness of Ants in Mongolia Along an Ecological Gradient from Steppe to Gobi Desert, Journal of Biography, Vol: 30, No: 12, Dec 2003 (Table and 2); p 92 Based on Statistical Abstract of the United States, 2012 (Table 942), U.S Energy Information Administration, Electric Power Annual; p 97 Source: Harell K, The Fallen and Their Swords: A New Explanation for the Rise of the Shaft Graves, American Journal of Archaeology, Vol: 118, No: 1, January 2014 (Figure 1); p 97 Source: Patson T, Teaching Stage Fright? Implications for Music Educators, British Journal of Music Education, Vol: 31, No: 1, March 2014 (Adapted from Figure 1); pp 97–98 Based on Paronuzzi P, Rockfall-Induced Block Propagation on a Soil Slope, Northern Italy, Environmental Geology, Vol: 58, 2009 (Table 2); p 98 Based on Paul I.M, et al Effect of Honey, Dextromethorphan, and no Treatment on Nocturnal Cough and Sleep Quality for Coughing and their Parents, Archives of Pediatrics and Adolescent Medicine, Vol: 161, No: 12, Dec 2007 (Data Simulated); p 98 Based on Boyer D.M, Evans A.R and Jernvall J, Evidence of Dietary Differentiation Among Late Paleocene-Early Eocene Plesiadapids (Mammalia, Primates), American Journal of Physical Anthropology, Vol: 142, 2010 (Table A3); pp 104–105 Source: Chew S, et al Do Social Robots Walk or Roll?, International Conference on Social Robotics, Vol: 6414, 2010 (adapted from Figure 2); pp 106–107 Based on Cole K, et al Animal Assisted Therapy Decreases Hemodynamics, Plasma Epinephrine and State Anxiety in Hospitalized Heart Failure Patients, American Journal of Critical Care, 16: 575–585; p 120 Based on Grassberger 884 www.downloadslide.net CREDITS 885 M and Hoch W, Ichthyotherapy as Alternative Treatment for Patients with Psoriasis: A Pilot Study, Evidence-Based Research in Complementary and Alternative Medicine, Vol: 3, No: 4, Dec 2004 (Figure 3), National Center for Complementary and Alternative Medicine; p 120 Based on Paronuzzi P, Rockfall-Induced Block Propagation on a Soil Slope, Northern Italy, Environmental Geology, Vol: 58, 2009 (Table 2); p. 122 Based on Bayonet Point Hospital, Coronary Care Unit; p 124 Based on Schaffer J and Heiny E.L, The Effects of Elevation on Slugging Percentage in Major League Baseball, Chance, Vol: 19, No: 1, Winter 2006 (Figure 2); p 124 Source: Shester G.G, Explaining Catch Variation Among Baja California Lobster Fishers Therough Spatial Analysis of Trap-Placement Decisions, Bulletin of Marine Science, Vol: 86, No: 2, April 2010 (Table 1); p 124 Based on Shand J, et al Variability in the Location of the Retinal Ganglion Cell Area Centralis is Correlated with Ontogenetic Changes in Feeding Behaviour in the Blackbream, Acanthopagrus Butcher, Brain and Behaviour, Vol: 55, No: 4, April 2000 (Figure 10H); p 124 Based on Khan M.S, Paper Diagnostic for Instant Blood Typing, Analytical Chemistry, Vol: 82, No: 10, May 2010 (Figure 4b); pp 124–125 Based on Russell D.A, Basketballs as Spherical Acoustic Cavities, American Journal of Physics, Vol: 78, No: 6, June 2010 (Table 1); p 125 Based on Russell D.A, Basketballs as Spherical Acoustic Cavities, American Journal of Physics, Vol: 78, No: 6, June 2010 (Table 1); p 125 Source: Rusterholz H, Aydin D and Baur B, Population Structure and Genetic Diversity of Relict Populations of Alyssum Montanum on Limestone Cliffs in the Northern Swiss Jura Mountains, Alpine Botany, Vol: 122, No: 2, November 2012, (Tables and 2); p 125 Based on Wade T.G, et al Distribution and Causes of Global Forest Fragmentation, Conservation Ecology, Vol: 72, No: 2, Dec 2003 (Table 6); pp 125–126 Based on Barry J, Estimating Rates of Spreading and Evaporation of Volatile Liquids, Chemical Engineering Progress, Vol: 101, No: 1, Jan 2005; p 126 Based on The Dock and Harbour Authority; p 134 Table 2: Books Categorized by Level from Extensive Reading in Japanese by Claire Ikumi Hitosugi and Richard R Day from Reading In a Foreign Language, Vol: 16, No: 01 Copyright © 2004 Used by permission of National Foreign Language Resource Center; p 134 Based on Berg I and Bliedon S, The Pots of Phylakopi: Applying Statistical Techniques to Archaeology, Chance, Vol: 13, No: 04, Fall 2000; p. 134 Table 4: Number of Books Read in the Ten-Week Period from Extensive Reading in Japanese by Claire Ikumi Hitosugi and Richard R Day from Reading In a Foreign Language, Vol: 16, No: 01 Copyright © 2004 Used by permission of National Foreign Language Resource Center; pp 135–136 Based on American Journal of Science, Vol: 305, No: 1, Jan 2005, Page: 16 (Table 2); p. 136 Based on Hood J.C, Teaching Against the Text: The Case of Qualitative Methods, Teaching Sociology, Vol: 34, Issue: 3, Page: 207 (Exhibit 2); p. 136 Based on Lichen Radionuclide Baseline Research Project, 2003, Page: 25, Orion, University of Alaska-Fairbanks; p. 137 High Dispersion Clusters of Galaxies from Observations of High Dispersion Clusters of Galaxies: Observations of High Dispersion Clusters of Galaxies: Constraints on Cold Dark Matter, The Astronomical Journal, Vol: 110, No: 1, Page: 37 by John M Hill, William R Oegerle, M.J Fitchett from The Astronomical Journal Copyright © 1995 IN ALL CASES for works published under any of the Creative Commons licenses the words “© AAS Reproduced with permission”; p 137 Based on Ross R.J.S, From Antisweatshop to Global Justice to Antiwar: How the New Left is the Same and Different from the Old New Left, Journal of World Systems Research, Vol: X, No: 1, Winter 2004 (Table and 3) pp 137–138 Based on McCowan B and Reiss D, Quantitative Comparison of Whistle Repertoires from Captive Adult Bottlenose Dolphins (Delphiniae Tursiops Truncates): A ReEvaluation of the Signature Whistle Hypothesis, Ethology, Vol: 100, No: 3, July 1995, Page: 200 (Table 2); p. 138 Based on Arriaga R.L, et al Scores on the MacArthur Communicative Development Inventory of Children from Low-Income and Middle Income Families, Applied Psycholinguistics, Vol: 19, No: 2, June 1998, Page: 217 (Table 7); p 138 Based on Yang W.H, A Freckle Criterion for the Solidification of Super alloys with a Tilted Solidification Front, JOM: Journal of the Minerals, Metals and Materials Society, Vol: 56, No: 9, Sept 2032; p 139 Based on McDonald.T.L, Erickson W.P and McDonald L.L, Analysis of Count Data from Before-After ControlImpact Studies, Journal of Agricultural, Biological Statistics, Vol: 5, No: 3, Sept 2028, Page: 277–8 (Table A.1); p 139 Based on Hill T.P, The First Digit Phenomenon, American Scientist, Vol: 86, No: 4, July-Aug 1998, Page: 363 (Figure 5); p. 139 Based on Oegerle W.R, Hill J.M and Fitchett M.J, Observations of High Dispersion Clusters of Galaxies: Constraints on Cold Dark Matter, The Astronomical Journal, Vol: 110, No: 1, July 1995 (Table and Figure 1); pp 142–14 Screenshots from Minitab Corporation Courtesy of Minitab Corporation; pp 152–153 Screenshots from Texas Instruments Courtesy of Texas Instruments Chapter p 159 Based on Hamilton R W “Why People Suggest What They not Want? 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p 823 Source: Malone, E.K Issa, R.A “Work-Life Balance and Organizational Commitment of Women in the U.S. Construction Industry”, Joumal of Professional Issues in Engineering Education & Practice, Vol 139, No 2, April 2013 (Table 11); p 823 APOE genotype and cognitive functioning in a large age-stratified population sample by Jorm A F et al from Neuropsycology, Vol 21, No l.January 2007 (Table l) Copyright © 2007 by American PsychologicalAssociation Used by permission of American Psycological Association; p 823 Based on Bryant, D J “Classifying simulated air threats with fast and frugal heuristics.” Journal of Behavioural Decision Making, Vol 20, January 2007 (Appendix C); p 823 Source: Gold, J.E., et al “Postures, typing strategies, and gender differences in mobile device usage: An observational study”, Applied Ergonomics, Vol 43, No 2, March 2012 (Table 2); p 824 Source: Bigham, G.D & Riney, M.R “Trend Analysis Techniques to Assist School Leaders in Making Critical Curriculum and Instruction Decisions”, Current Issues in Education, Vol 17, No 1, January 2014 (Table 6); 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p 831 Based on Krienert J.l, “Masculinity and Crime: A Quantitative Exploration of Messerschmidt’s Hypothesis”, Journal of Sociology, Vol 7, No.2, July 2003 (Table 4); p 832 The Flynn Effect and U.S Policies by Kanaya T, Scullin M.H, Ceci S.J from American Psychologist, Vol.58, No 10, Oct 2003(Figure 1) Copyright © 2003 by American Psycological Association Used by permission of American Psycological Association; p 832 A gang classification system for corrections by Knox, G W., et al from Journal of Gang Research, Vol 4, No 2, Winter I997 p 82 (Table 4) Copyright © 1997 by National Gang Crime Research Center Used by permission of National Gang Crime Research Center; p 833 The Computer-Assisted Hypnosis Scale: Standardization and Norming of a Computer-administered Measure of Hypnotic Ability by Carolyn D Grant and Michael R Nash from Psycological Assessment Vol.7 No I March I995, p 53 Copyright © 1995 by American Psychological Association Used by permission of American Psycological Association; p 834 Based on Gelman “A 55,000 residents desperately need your help!”, Chance, Vol l7 No 2, Spring 2004 (Figures l and 5); p 834 Based on Gangemi.A., and Mancini F “Guilt and focusing in decision-making” Journal of Behavioural Decision Making, Vol 20, Jan 2007 (Table 2); pp 835–836 Screenshots from Minitab Corporation Courtesy of Minitab Corporation Chapter 14 (available online) p 14-7 Source: Patston, T, ‘Teaching stage fright?–Implications for music educators British Joumal ofMusic Education, Vol 31, No 1, March 2014 (adapted from Figure 1); p 14-8 Based on Boyer D.M, Evans A.R and Jernvall J, “Evidence of Dietary Differentiation Among Late Paleocene–Early Eocene Plesiadapids (Mammalia, Primates)”, American Journal of Anthrapology, Vol 142, © 2010, Table A3; p 14-9, Based on Thery.M, et al., “Specific Color Sensitivities of Prey and Predator Explain Camouflage in Different Visual www.downloadslide.net CREDITS 891 Systems”, Behavioural Ecology, Vol.16, No.1, Jan 2005 (Table 1); p 14-9, Based on Shester G.G, “Explaining Catch Variation among Baja California Lobster Fishers Through Spatial Analysis of Trap-Placement Decision”, Bulletin of Marine Science, Vol 86, No 2, April 2010 (Table 1); 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p 14-23, Interactive Homework in Middle School: Effects on Family Involvement and Science Achievement by Van Voorhis from Journal of Educational Research,96(6), pp 323–338 Copyright © 2003 by Frances Van Voorhis Used by permission of Frances Van Voorhis; p 14-28, Screenshots from SPSS Reprint Courtesy of International Business Machines Corporation, © International Business Machines Corporation; p 14-30, Source: Ozur, M “Comparing Methods for Predicting Immediate Settlement of Shallow Foundations on Cohesive Soils Based on Hypothetical and Real Cases”, Environmental & Engineering Geoscience, Vol 18, No 4, November 2012 (from Table 4); p 14-30 Based on Virginia Transportation Research Council, “Research Report: The Impact of Red Light Cameras( Photo-Red Enforcement) on Crashes in Virginia”, June 2007; p 14-31, Based on Ahmed S and Asraf R.M, “Making Sense of Text Strategies Used by Good and Average Readers”, The Reading Matrix, Vol 4, No 1, April 2004 (Table 2); p 14-31, Source: Shamia, P and Harinarayana, T “Solar energy generation potential along national highways”, international Joumal of Energy and Environmental Engineering, Vol 49, No 1, December 2013 (Table 3); p 14-32, Based on Vrea A, et al., “The Use of Visual Evoked Potentials to Follow-up Prisoners of War After Release from Detention Camps”, Collegium Antropologicium, Vol 21, No 1, June 1997, p 232 (Data simulated from information provided in Table 3); p 14-32, Source: Hearst M.O, et al., “lnventoryuversus checlaist approach to assess middle school a la carts fog availability”, Journal of School Health, Vol 79, No 12, December 2009 (Table 3); p 14-32, Based on Ohberg L, et al., “Eccentric Training in Patients With Chronic Achilles Tendinosis: Normalized Tendon Structure and Decresed Thickness at Follow-up”, British Journal of Sports Medicine, Vol 38, No.1, Feb 1,2004(Table 2); p 14-32, Based on Terami H and Walanabe M, “Excessive Transitory Migration of Guppy Populations III: Analysis of Perception of Swimming Space and a Mirror Effect”, Zoological Science, Vol,6, 1989; p 14-33, James T McClave; p 14-40, Based on Buchowski M.S, et al., “Energy Expenditure of Genuine Laughter”, International Journal of Obesity, Vol 31, No 1, January 2007 (Figure 4); 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p 14-47, An Illustrated Review of Some Farmer Participatory Research Techniques by Riley J, Fielding W.J from Journal of Agricultural, Biological and Environmental Statistics, Vol 6, No.1, Mar 2001(Table 1) Copyright © 2001 by International Biometric Society Used by permission of International Biometric Society; p 14-48, Based on Hemmings B, Smith M, Graydon J and Dyson R, “Effects of Massage on Psychological Restoration, Perceived Recovery and Repeated Sports Performance”, British Journal of Sports Medicine, Vol 34, No 2, Apr 2000(Adapated from Table 3); p 14-48, Based on Data from Elizabeth Schreiher, Department of Statistics Kansas State University Manhattan, Kansas; p 14-54, Based on Pfeiffer m, et al., “Community Organization and Species Richness of Ants in Mongolia Along an Ecological Gradient from Steppe to Gobi Dessert”, Journal of Biogeography, Vol 30, No 12, Dec 2003 (Tables and 2); p 14-55, Explaining Catch variation Among Baja California Lobster fishers through Spatial Analysis of Trap-Placement Decisions by Shester G.G from Bulletin of Marine Science, Vol 86, No 2, April 2010 (Table 1) Copyright © 2010 by University of Miami, Bulletin of Marine Science Used by permission of University of Miami, Bulletin of Marine Science; p 14-155, Based on Hemmings B, Smith M, Graydon J and Dyson R, “Effects of Massage on Psychological Restoration, Perceived Recovery and Repeated Sports Performance”, British Journal of Sports Medicine, Vol 34, No 2, Apr 2000(Data Adapated from Figure 3); p 14-55, Based on Seal N and Seal J, “Eating Patterns of the Rural Families of Overweight Pre-school Children: A Pilot Study”, Journal of Education and Human Development, Vol 1, No 3, 2009 (Figure 1); p 14-56, Based on Morris P.E and Fritz C.O, “The Name Game: Using Retrieval Practice to Improve the Learning of Names”, Journal of Experimental Psycology-Applied, Vol 6, No 2, June 2000 (Data simulated from figure 2); p 14-57, Based on Singer T, et al., “Empathy for Pain Involves the Affective but not Sensory Components of Pain”, Science, Vol 303, Feb 20,2004 (Adapted from fig 4); p 14-57, Based on Hertwig R, Pachur T and Kurzenhauser S, “Judgements of Risk Frequencies Tests of Possible Cognitive Mechanisms”, Journal of Experimental Psychology Learning, Memory and Cognition, Vol 31, No 4, July 2005 (Table 1); p 14-60, Based on Lichen Radionuclide Baseline Research Project,2003, p 25, Orion, University of Alaska-Fairbanks; p 14-60, Based on C.P Keller, et al., “Planning the Next Generation Regional Atlases: Input from Educators”, Journal of Geograpghy, Vol 94, No 3, May/June 1995, p,413 (Table 1); p 14-60, Based on Shand J, et al., “Variability in the Location of the Retinal Ganglion Cell Area Centralis is Correlated with Ontogenetic Changes in Feeding Behaviour in the Black Bream, Acanthopagrus’ Butcheri’ ” Brain and Behaviour, Vol 55, No 4, Apr 2000 (Figure II); p 14-61, Based on Bonadia P, et al., “Aluminosilicate Refractories for Aluminium Cell Linings”, American Ceramic Society Bulletin, Vol 84, No 2, Feb 2005( Table II); p. 14-62, Based on American Journal of Science, Vol 305, No 1, Jan 2005, p.16 (Table 2); p 14-63, Based on Axelson D, et al., “A Preliminary Study of the Kiddie Schedule for Affective Disorders and Schizophrenia for School-Age Children Mania Rating Scale for Children and www.downloadslide.net 892 CREDITS Adolescents”, Journal of Child and Adolescent Psychopharmacology, Vol 13, No 4, Dec 2003 (Figure 2); p 14-63, Based on Billing B.A, Green B.P and Volz W.H, “Selection of Forum for Litigated Tax Issues”, Journal of Applied Business Research, Vol 12, No.4, Fall 1996, p 38 (Table 2); p 14-63, Based on Yang W.H, et al., “A Freckle Criterion for the Solidification of Super-alloys with a tilted Solidification Front”, JOM: Journal of the Minerals, Metals and Materials Society, Vol 56, No 9, Sept 2004 (Table iv); p 14-64, Based on Uyehara J.C and Narins P.M, “Nest Defense by Willow Fliycatchres to Brood-Parasitic Intruders”, The Condor, Vol 97, No.2, May 1995, p 364 (Figure 1); p 14-65–14-67, Screenshots from Minitab Corporation Courtesy of Minitab Corporation www.downloadslide.net This page intentionally left blank www.downloadslide.net This page intentionally left blank www.downloadslide.net This page intentionally left blank www.downloadslide.net MAKING CONNECTIONS USING DATA ■ Statistics IN Action Social Media network Usage— STATISTICS IN ACTION opens each chapter with a case study showing the connection between statistics and a contemporary, controversial, or high-profile issue The case study is revisited throughout the chapter, showing the relevance of statistical concepts Are You Linked in? The Pew Research Center, a nonpartisan organization funded by a Philadelphia-based charity, has conducted more than 100 surveys on Internet usage in the United States as part of the Pew Internet & American Life Project (PIALP) In a recent report titled Social Media Update, 2013, the PIALP examined adults’ (ages 18 and up) attitudes and behavior toward online social media networks Regarded merely as a fun, online activity for high school and college students just a few years ago, social media now exert tremendous influence over the way people around the world—of all ages—get and share information The five social media sites investigated in this report include Facebook, Twitter, Instagram, Pinterest, and LinkedIn The Pew Research Center contacted 1,445 Internet users via landline telephone or cell phone for the survey Several of the many survey questions asked are provided here as well as the survey results: • Social Networking: When asked if they ever use an online social networking site, adults responded: • Twitter Usage: When asked if they ever use Twitter, adults responded: Yes 18% No 82% • Overall Social Media Usage: When asked about how many of the five social networking sites they use, adults responded: 22% 36% 23% 12% 5% 2% Yes 73% (Average = 1.48 sites) No 27% In the following “Statistics in Action Revisited” sections, we discuss several key statistical concepts covered in this chapter that are relevant to the Pew Internet & American Life Project survey • Facebook Usage: When Facebook users were asked how often they visit the social media site, they responded: Several times a day 40% About once a day 24% 3–5 days a week 10% ■ Statistics IN Action Revisited • Identifying the Population, Sample, and Inference (p 37) 1–2 days a week 13% Every few weeks 6% • Identifying the Data Collection Method and Data Type (p 44) Less often 7% • Critically Assessing the Ethics of a Statistical Study (p 46) Page 30 Example EXAMPLES use real data to show how statistical methods can be applied to real life situations The Example format fosters problem-solving skills by taking a three-step approach: 7.6 Estimating a Population Proportion—Fraction Who Trust the President Problem Public-opinion polls are conducted regularly to estimate the fraction of U.S citizens who trust the president Suppose 1,000 people are randomly chosen and 637 answer that they trust the president How would you estimate the true fraction of all U.S citizens who trust the president? Solution What we have really asked is how you would estimate the probability p of success in a binomial experiment in which p is the probability that a person chosen trusts the president One logical method of estimating p for the population is to use the proportion of successes in the sample That is, we can estimate p by calculating pn = Number of people sampled who trust the president Number of people sampled where pn is read “p hat.” Thus, in this case, • Problem • Solution • Look Back (or Look Ahead) pn = 637 = 637 1,000 Look Back To determine the reliability of the estimator pn , we need to know its sampling distribution That is, if we were to draw samples of 1,000 people over and over again, each time calculating a new estimate pn , what would be the frequency distribution of all the pn -values? The answer lies in viewing pn as the average, or mean, number of successes per trial over the n trials If each success is assigned a value equal to and each failure is assigned a value of 0, then the sum of all n sample observations is x, the total number of successes, and pn = x>n is the average, or mean, number of successes per trial in the n trials The Central Limit Theorem tells us that the relative frequency distribution of the sample mean for any population is approximately normal for sufficiently large samples A NOW WORKEXERCISEsuggestion follows each Example ■ pn Page 365 pn Now Work Exercise 7.59a www.downloadslide.net Connecting facets of everyday life to statistics, McClave and Sincich offer a trusted, comprehensive text that stresses the development of statistical thinking, the assessment of credibility, and the value of inferences made from data USING TECHNOLOGY USING TECHNOLOGY sections, at the end of each chapter, offer statistical software tutorials with step-by-step instructions and screen shots for MINITAB® and, where appropriate, the TI-83/84 Plus Graphing Calculator MINITAB: Simulating a Sampling Distribution Step Select “Calc” on the MINITAB menu bar, and then click on “Random Data” (see Figure 6.M.1) Step Calculate the value of the sample statistic of interest for each sample To this, click on the “Calc” button on the MINITAB menu bar, and then click on “Row Statistics,” as shown in Figure 6.M.3 The resulting dialog box appears in Figure 6.M.4 Figure 6.M.1 MINITAB options for generating random data Step On the resulting menu list, click on the distribution of your choice (e.g., “Uniform”) A dialog box similar to the one (the Uniform Distribution) shown in Figure 6.M.2 will appear Figure 6.M.3 MINITAB selections for generating sample statistics for the simulated data , the arrival www.brighton-webs from the start Figure 6.M.4 MINITAB row statistics dialog box Figure 6.M.2 MINITAB dialog box for simulating the uniform distribution Page 341 Researchers at the University of South Florida Center for Biological Defense , he method has been found to work well powder Consider a powder specimen that has exactly Suppose that the number of anthrax spores in the sample detected by the new method follows MORE THAN 1,800 EXERCISES are available, ind the probability that or fewer anthrax spores are covering a wide range of applications that underscore anthrax the relevance of statistics to everyday life Most of the exercises incorporate real data, and a indicates that Construct the data set is available on the companion website Ethics IN Statistics Purposeful reporting of numerical descriptive statistics in order to mislead the target audience is considered unethical statistical practice Page 129 NEW! ETHICS BOXES appear where appropriate to highlight the importance Maintaining a conof ethical behavior stant temperature in awhen pipe wall in some hot process A new technique that utilizes collecting, analyzing, and the temperature was bolt-on trace elements to maintain (November interpreting statistical data the pipe wall Applying the Concepts—Intermediate 5.14 Social network densities Social networks involve interactions (connections) between members of the network Sociologists define network density as the ratio of actual network connections to the number of possible one-to-one connections For example, a network with 10 members 10 has a b = 45 total possible connections If that network has only connections, the network density is 5/45 = 111 Sociologists at the University of Michigan assumed that the density x of a social network would follow a uniform distribution between and (Social Networks, 2010) a On average, what is the density of a randomly selected social network? b What is the probability that the randomly selected network has a density higher than 7? c Consider a social network with only members Explain why the uniform model would not be a good approximation for the distribution of network density 5.15 Cycle availability of a system In the jargon of system maintenance, “cycle availability” is defined as the probability that a system is functioning at any point in time The U.S Department of Defense developed a series of performance measures for assessing system cycle availability (START, Vol 11, 2004) Under certain assumptions about the failure time and maintenance time of a system, cycle availability is shown to be uniformly distributed between and Find the following parameters for cycle availability: mean, standard deviation, 10th percentile, lower quartile, and upper quartile Interpret the results 5.16 Time delays at a bus stop A bus is scheduled to stop at a certain bus stop every half hour on the hour and the half hour At the end of the day, buses still stop after every 30 minutes, but because delays often occur earlier in the day, the bus is never early and is likely to be late The director of the bus line claims that the length of time a bus is late is uniformly distributed and the maximum time that a bus is late is 20 minutes a If the director’s claim is true, what is the expected number of minutes a bus will be late? b If the director’s claim is true, what is the probability that the last bus on a given day will be more than 19 minutes late? c If you arrive at the bus stop at the end of a day at exactly half-past the hour and must wait more than 19 minutes for the bus, what would you conclude about the director’s claim? Why? Page 266 www.downloadslide.net GLOBAL EDITION For these Global Editions, the editorial team at Pearson has collaborated with educators across the world to address a wide range of subjects and requirements, equipping students with the best possible learning tools This Global Edition preserves the cutting-edge approach and pedagogy of the original, but also features alterations, customization, and adaptation from the North American version This is a special edition of an established title widely used by colleges and universities throughout the world Pearson published this exclusive edition for the benefit of students outside the United States and Canada If you purchased this book within the United States or Canada, you should be aware that it has been imported without the approval of the Publisher or Author Pearson Global Edition ... Authorized adaptation from the United States edition, entitled Statistics, 13th Edition, ISBN 978-0-13-408021-5 by James T McClave and Terry T Sincich, published by Pearson Education © 2017 All rights... www.pearsonglobaleditions.com /mcclave and in Pearson MyLab Statistics OnlineTest Bank, a test bank derived from TestGen®, is available for download from Pearson’s online catalog at www.pearson globaleditions.com /mcclave. .. professors, whose reviews and comments on this and prior editions have contributed to the 13th edition: Reviewers Involved with the 13th Edition of Statistics Sarol Aryal, Montana State University—Billings