www.ebookslides.com 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, 216; 4.6, 231 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 4.7, 260; 4.8, 260 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, 43; 1.2, 44; 3.6, 183; 4.1, 202 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, 151; 3.3, 162; 3.4, 163; 3.5, 177 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, 162; 3.4, 163 Simulating the probability of heads: fair coin Investigates relationship between theoretical Reports outcome of each fair coin flip and cre- 3.2, 151; 4.2, 203 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, 216 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, 216 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, 216 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, 85; 2.2, 85; 2.3, 85 www.ebookslides.com This page intentionally left blank www.ebookslides.com Applet Concept Illustrated Description Applet Activity Standard deviation Investigates how distribution shape and spread affect standard deviation Students visualize relationship between mean 2.4, 92; 2.5, 93; 2.6, 93; 2.7, 115 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 5.5, 303; 5.6, 304 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 5.1, 285; 5.2, 285 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 5.3, 295; 5.4, 295 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 6.1, 341; 6.2, 351; 6.3, 351; population; calculates and plots t statistic and 6.4, 351 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 9.2, 563 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 6.5, 367; 6.6, 368 9.1, 536 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 www.ebookslides.com This page intentionally left blank www.ebookslides.com T welfTh ediTion Global ediTion James T McClave Terry Sincich Info Tech, Inc University of South Florida University of Florida 330 Hudson Street, NY, NY 10013 www.ebookslides.com 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: Jerry Kataria Media Producer: Jean Choe Media Production Manager, Global Edition: Vikram Kumar TestGen Content Manager: John Flanagan MathXL Content Manager: Bob Carroll Product Marketing Manager: Tiffany Bitzel Field Marketing Manager: Andrew Noble Marketing Assistant: Jennifer Myers Senior Author Support/Technology Specialist: Joe Vetere Rights and Permissions Project Manager: Gina Cheselka Procurement Specialist: Carol Melville Associate Director of Design: Andrea Nix Program Design Lead: Barbara Atkinson Text Design: Integra Illustrations: Integra Cover Design: Lumina Datamatics Cover Images: vesna cvorovic/Shutterstock Acknowledgments of third party content appear on page 636, which constitutes an extension of this copyright page PEARSON, ALWAYS LEARNING, Pearson MyLab 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 A First Course in Statistics, 12th Edition, ISBN 978-0-13-408062-8 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: 1-292-16541-3 ISBN 13: 978-1-292-16541-7 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 www.ebookslides.com Contents Preface 11 Applications Index Chapter 19 Statistics, data, and Statistical Thinking 1.1 The Science of Statistics 1.2 Types of Statistical Applications 1.3 Fundamental Elements of Statistics 1.4 Types of Data 1.5 Collecting Data: Sampling and Related Issues 1.6 The Role of Statistics in Critical Thinking and Ethics 25 26 27 29 33 35 40 Statistics in Action: Social Media Network Usage—Are You Linked In? Using Technology: MINITAB: Accessing and Listing Data Chapter 49 Methods for describing Sets of data 53 2.1 Describing Qualitative Data 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 2.7 Methods for Detecting Outliers: Box Plots and z-Scores 2.8 Graphing Bivariate Relationships (Optional) 2.9 Distorting the Truth with Descriptive Statistics 55 Using Technology: MINITAB: Describing Data 89 95 103 107 117 122 54 136 TI-83/TI–84 Plus Graphing Calculator: Describing Data Probability 66 78 Statistics in Action: Body Image Dissatisfaction: Real or Imagined? Chapter 26 137 139 3.1 Events, Sample Spaces, and Probability 3.2 Unions and Intersections 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 141 154 157 159 166 169 www.ebookslides.com 634 Index Quantitative literacy, 40 Quartiles, 104 Random error, 525 Random number generator, 36 Random sample explanation of, 36 simple, 36 Random variables See also Binomial random variables; Continuous random variables; Discrete random variables classification of, 197 explanation of, 191–192 hypergeometric, 208 normal, 220–230 standard normal, 222–225 variance of, 199–200 Randomized block experiments, 414 Randomized response sampling, 37–38 Range, 89 Rank-sum test See Wilcoxon rank sum test Rank sums, 428 Rare-event approach, 113 Regression analysis See also Linear regression explanation of, 525 probability distribution of random error and, 542 Regression line, 530 Regression residuals See Residuals Rejection region, 377 explanation of, 333, 336, 348 for one-and two-tailed tests, 337–340, 347–348, 363 Relative frequency distribution probability distribution for random variable and, 196–197 shape of population, 255–256 Relative frequency histograms, 68–70, 80–82, 196 Relative standing See Measures of relative standing Reliability, 32 Representative sample, 36 Research hypothesis See Alternative hypothesis Residual sum of squares See Sum of squares of error (SSE) Residuals, 530 Response variable, 526 Robust method, ANOVA as, 453 Sample means, 78–79 calculation of, 78 explanation of, 78, 248–249 formula for, 78 sampling distribution of, 254–259 symbol for, 79 Sample median calculation of, 80–82 explanation of, 80, 248 Sample point probabilities calculation of, 209–211 explanation of, 143–146 Sample points for coin-tossing experiment, 142 collection of, 145 for complex experiments, 148–150 explanation of, 141–143, 192 Sample size, 79 central limit theorem and, 476 determining, 482–483 method to determine, 423–425 population mean and, 306–308 population proportion and, 308–310 Sample space, 142–144 Sample standard deviation, 90 Sample variance, 90 Sample variance formula, 90 Sample z-score, 104 Samples biased, 41, 42 explanation of, 30 random, 36 representative, 36 Sampling cluster, 208 independent, 393–403, 475–479 lot acceptance, 244–245 Sampling distributions central limit theorem and, 256–257 explanation of, 249 method to find, 250–251 of sample mean, 254–259 simulation of, 249 standard deviation of, 255 Sampling error explanation of, 307 sample size to estimate difference between pair of parameters with specified, 423–424 SAS, linear regression on, 533, 544–545, 575–577 Scatterplots explanation of, 117 normal probability plot as, 234 use of, 117–119, 530 Selection bias, 38, 42 Self-selection, 39 Sign test, 376–379 application of, 378–379 conditions required for valid application of, 378 explanation of, 376 large-sample sign test for a population mean, 378 Signed rank test See Wilcoxon (paired difference) signed rank test Significance, level of, 336 Significance levels, observed explanation of, 342–346 method to find, 479 Skewness, 81, 82 Slope, 527 Small-sample inferences, 358 conditions for, 400, 415 Small samples conditions for inferences, 400, 415 population mean comparisons and, 398–403 Spearman, Charles E., 580 Spearman’s rank correlation coefficient, 579–581, 583 conditions required for a valid, 583 critical values of, 583 example of, 581–582 shortcut formula for, 580 Spread, 89 See also Variability SPSS, linear regression on, 533, 550 Standard deviation for binomial random variable, 213 of discrete random variable, 199–200 explanation of, 89–90 interpretation of, 95–98 sample, 90 of sampling distribution, 255 symbols for, 91 variability and, 95 Standard error of statistic, 395 Standard normal distribution, 221 Standard normal random variable explanation of, 221 method to find, 222–225 Standard normal table explanation of, 220–221 use of, 222–224 used in reverse, 228 Statistic, standard error of, 395 Statistical inference See also Inferences; specific topics example of, 98–99 explanation of, 30 numerical descriptive measures and, 78 Statistical test elements of, 333 p-values and, 342–346 Statistical thinking, 41 Statisticians, 27 Statistics See also Descriptive statistics; Inferential statistics; specific topics applications of, 27–29 explanation of, 26–27 fundamental elements of, 29–33 types of, 27 Stem-and-leaf display, 70 explanation of, 67–68, 72 Straight-line model estimation using, 534–535, 543 explanation of, 525, 526 prediction using, 570 Stratified random sampling, 37 Sum of squares for treatments (SST), 447 Sum of squares of error (SSE), 447, 530, 531 explanation of, 531 Summation notation, 78 Surveys, 36, 39–42 Systematic sampling, 37 t-statistic, 376 confidence interval for population mean and, 289–294 hypothesis test for population mean and, 347–350 small-sample population mean comparisons and, 393, 398–399, 403 t-tests, comparison of two independent samples and, 453 www.ebookslides.com Index Target parameter explanation of, 278 identifying and estimating, 277–278, 336, 392–393 Test of hypothesis See Hypothesis test Test statistic, 376–377 explanation of, 332, 336 p-value and, 347 Treatment means, ANOVA F-test to compare, 449 Treatments, sum of squares for, 447 Tree diagrams to calculate probability of intersections, 171–172 explanation of, 142 Tukey, John, 68 Two-tailed hypothesis test, 337, 372–373, 477 Two-way table See also Contingency table categorical probabilities in, 494–501 conditional probability in, 167–169 explanation of, 156–157, 494 with fixed marginals, 500–501 Type I error, 333 Type II error, 334 Unconditional probabilities, 166 Unethical statistical practice, 41, 42, 122, 125 See also Ethics Union of events, 154–157 of mutually exclusive events, 160–161 Upper quartile (QU), 104 Variability explanation of, 79, 95 numerical measures of, 89–92 Variables See also Random variables explanation of, 29 Variance See also Population variances for binomial random variable, 213 confidence interval for population, 313–316 of random variable, 199–200 sample, 90 symbols for, 91 Venn, John, 143 Venn diagrams explanation of, 142 use of, 155, 191 Wells, H G., 40 Whiskers, 108, 109 Wilcoxon, Frank, 427 Wilcoxon rank sum test, 427–431 See also Mann-Whitney U-test application of, 429–431 conditions required for a valid, 429 635 critical values for, 428 explanation of, 427 large-sample, 431 Wilcoxon (paired difference) signed rank test, 437–438 application of, 439–440 conditions required for a valid, 438 explanation of, 438 large-sample, 440–441 Wilson’s adjustment for estimating p, 302 y-intercept, 527, 529–532 z-scores explanation of, 104 inference using, 104–105, 112–113 interpretation of, 105 method to find, 104–105 population, 104 sample, 104 use of, 113, 224 z-statistic, 376 confidence intervals and, 279–284 hypothesis test and, 347–350 large-sample population mean comparisons and, 393–398 www.ebookslides.com Credits Chapter Screenshots from Minitab: Screenshots from Minitab Corporation Courtesy of Minitab Corporation Screenshots from Texas Instruments: Screenshots from Texas Instruments Courtesy of Texas Instruments; Anson0618/Shutterstock; TheProductGuy/Alamy; Johnny Stockshooter/Alamy; TheProductGuy/Alamy; Source: Digical/E+/Getty Images; Justin Horrocks/E+/Getty Images; The ProductGuy/Alamy; FooTToo/Shutterstock; 123RF; TheProductGuy/Alamy Chapter Summary Table from SPSS Reprint Courtesy of International Business Machines Corporation, © International Business Machines Corporation; Screenshot from SAS © 1999 SAS Institute Inc Reprinted with Permission; Gfdunt/Shutterstock; Luis Louro/Shutterstock; Elena Elisseeva/iStock/Getty Images; Luis Louro/Shutterstock; Luis Louro/Shutterstock; Lasse Kristensen/Shutterstock; Andrew Zarivny/Shutterstock; Hywit Dimyadi/Shutterstock; Luis Louro/Shutterstock; Goodluz/Shutterstock; Luis Louro/Shutterstock; Wavebreakmedia/Shutterstock; Table 02-Books Categorized by Level from “Extensive Reading in Japanese” by Claire Ikumi Hitosugi and Richard R Day in Reading In a Foreign Language, Vol: 16, No: 01 Copyright © 2004 by National Foreign Language Resource Center Used by permission of National Foreign Language Resource Center Chapter Ruben Pinto/Shutterstock; 123RF; Vladimir Wrangel/Shutterstock; Agencyby/iStock/Getty Images; Lorraine Kourafas/Shutterstock; 123RF; Pekka Jaakkola/iStock/Getty Images; Yvonne Chamberlain/E+/Getty Images; 123RF; Sculpies/Shutterstock; Darren Brode/ Shutterstock; 123RF; Screenshots from Texas Instruments Courtesy of Texas Instruments Chapter Reprint Courtesy of International Business Machines Corporation, © International Business Machines Corporation; Screenshots from Minitab Corporation Courtesy of Minitab Corporation; Dibrova/Shutterstock; Brad Sauter/Shutterstock; Santino Ambrogio/ E+/Getty Images; Vladimir Wrangel/Shutterstock; JohnKwan/Shutterstock; Tischenko Irina/Shutterstock; MistikaS/E+/Getty Images; Nathan Gutshall–Kresge/iStock/Getty Images; Daniel Hurst/iStock/Getty Images; JC559/E+/Getty Images; Brad Sauter/ Shutterstock; Figure 03-Frequency Distributions of the Difference in Elevation Estimates from “Uncertainty in Digital Elevation Models of Axel Heiberg Island, Artic Canada” by J Graham Cogley and F Jung-Rothenhäusler in Artic, Antarctic, and Alpine Research, Vol 36, No.2; Copyright © 2004 by Regents of the University of Colorado Used by permission of Regents of the University of Colorado; Sean MacD/Shutterstock; JohnKwan/Shutterstock; Hywit Dimyadi/Shutterstock Chapter Reprint Courtesy of International Business Machines Corporation, © International Business Machines Corporation; Screenshots from Minitab Corporation Courtesy of Minitab Corporation; Screenshot from SAS © 1999 SAS Institute Inc Reprinted with Permission; Adisa/Shutterstock; Dewayne Flowers/Shutterstock; Wavebreakmedia/Shutterstock; Elena Elisseeva/iStock/Getty Images; Robert Milek/Shutterstock; Dewayne Flowers/Shutterstock; Uyen Le/E+/Getty Images; Dmitry Naumov/Shutterstock; Dewayne Flowers/ Shutterstock; Willard/iStock/Getty Images; Berislav Kovacevic/Shutterstock; Dewayne Flowers/Shutterstock; Digical/E+/Getty Images Chapter Histogram from SPSS Reprint Courtesy of International Business Machines Corporation, © International Business Machines Corporation; Screenshots from Minitab Corporation Courtesy of Minitab Corporation; Screenshot from SAS © 1999 SAS Institute Inc Reprinted with Permission; Fotocrisis/Shutterstock; Niki Crucillo/Shutterstock; Lisa F Young/Shutterstock; Russell Gough/iStock/Getty Images; Niki Crucillo/Shutterstock; Wavebreakmedia/Shutterstock; Niki Crucillo/Shutterstock; Robert Byron/iStock/Getty Images; Maxim ibragimov/Shutterstock; Niki Crucillo/Shutterstock; Andrew Johnson/E+/Getty Images 636 www.ebookslides.com Credits 637 Chapter Reprint Courtesy of International Business Machines Corporation, © International Business Machines Corporation; Robyn Mackenzie/ Shutterstock; Andresr/Shutterstock; Oliver Hoffmann/Shutterstock; Andrzej Tokarski/iStock/Getty Images; Andresr/Shutterstock; Iofoto/Shutterstock; Charles Brutlag/Shutterstock Chapter Dmitry Yashkin/Shutterstock; Image Source/Getty Images); Walik/E+/Getty Images; Walik/E+/Getty Images Chapter Fotorich01/Shutterstock; Sarah Angeltun/Shutterstock; Angelo Gilardelli/Shutterstock; Sarah Angeltun/Shutterstock; Sarah Angeltun/ Shutterstock www.ebookslides.com Selected Formulas Chapter Chapter P(Ac) = - P(A) Relative Frequency = (frequency)/n x = s = Σx n Σ(x - x)2 n - P(Ah B) = P(A) + P(B) - P(Ax B) Σx = = P(A) + P(B) if A and B mutually exclusive (Σx)2 P(Ax B) = P(A|B) # P(B) = P(B|A) # P(A) n = P(A) # P(B) if A and B independent n - s = 2s2 z = P(A ͉ B) = x - m x - x = s s Chebyshev = At least a1 IQR = QU - QL k2 P(Ax B) P(B) N N! a b = n n!(N - n)! b100% Chapter Key FormulaS Random Variable Prob Dist’n Mean General Discrete: Table, formula, or graph for p(x) Binomial: p(x) = a n b px qn - x x # a x p(x) Variance 2# a (x - m) p(x) all x all x np npq m s2 m = s2 = mx = m sx = s >n x = 0, 1, 2, c, n Normal: Standard Normal: f(x) = f(z) = 1 e - 2 [(x - m)/s] s22p 22p e - 2 (z ) z = (x - m)>s Sample Mean: (large n) f(x) = e - 2 [(x - m)>s x] sx 12p www.ebookslides.com Chapter Test for m1 - m2: CI for m: x { 1za/2 s> 1n (large n) x { (ta/2)s> 1n (small n, s unknown) CI for p: pn { za/2 pn qn A n 2 Estimating m: n = (za/2) (s )/(SE) Estimating p: n = (za/2)2(pq)/(SE)2 Chapter Test for m: z = t = x - m t = (x1 - x2) - (m1 - m2) B s2p a 1 + b n1 n2 CI for md: xd { ta>2 Test for md: t = sd 1n xd - md sd > 1n Estimating m1 - m2: n1 = n2 = (za>2)2 (s21 + s22)>(ME)2 x - m s> 1n ANOVA Test: (large n) F = MST/MSE (small n, s unknown) s> 1n Test for p: z = 2 Chapter pn - p0 2p0 q0 >n Test for s : x = (n - 1)s >(s0) Chapter CI for p1 - p2: (pn - pn 2) { za>2 Test for p1 - p2: z = s21 s22 (x1 - x2) { za>2 + s(large n1 and n2) n2 B n1 Test for m1 - m2: s2p = (x1 - x2) - (m1 - m2) s21 s22 + B n1 n2 pn = x1 + x2 n1 + n2 s(large n1 and n2) CI for m1 - m2: 1 + b n1 n2 (ni - Ei)2 Ei Contingency table test: x2 = Σ Eij = s2p a pn qn a Ei = n( pi0) n1 + n2 - B B Estimating p1 - p2: n1 = n2 = (za>2)2 (p1q1 + p2q2)>(ME)2 Multinomial test: x2 = Σ (n1 - 1)s21 + (n2 - 1)s22 (x1 - x2) { ta>2 pn 2qn pn 1qn + n2 B n1 (pn - pn 2) - (p1 - p2) CI for m1 - m2: z = s(small n1 and/or n2) 1 + b s(small n1 and/or n2) n1 n2 RiCj n (nij - Eij)2 Eij www.ebookslides.com Chapter SSxx = Σx SSyy = Σy2 SSxy = Σxy - s2 = (Σx)2 n (Σy)2 n (Σx)(Σy) ny = bn0 + bn1x n1 = b SSxy SSxx n ix bn0 = y - b r = SSxy 2SSxy 2SSyy n SSE n - s = 2s2 r2 = SSyy - SSE SSyy n { (ta/2)s/2SSxx CI for b1: b Test for b1: t = n1 - b s/2SSxx CI for E(y) when x = xpn : yn { ta/2s (xp - x)2 + Bn SSxx (xp - x)2 CI for y when x = xpn : yn { ta/2s + + B n SSxx www.ebookslides.com This page intentionally left blank www.ebookslides.com This page intentionally left blank www.ebookslides.com This page intentionally left blank www.ebookslides.com This page intentionally left blank www.ebookslides.com This page intentionally left blank www.ebookslides.com This page intentionally left blank www.ebookslides.com This page intentionally left blank www.ebookslides.com 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 ... types: quantitative data and qualitative data Quantitative data are data that are measured on a naturally occurring numerical scale.* The following are examples of quantitative data: The temperature... collecting, analyzing, and interpreting data with statistics Content-Specific Changes to This edition • Chapter (Statistics, Data, and Statistical Thinking) Material on all basic sampling concepts... death penalty each year over a 10-year period Quantitative data are measurements that are recorded on a naturally occurring numerical scale In contrast, qualitative data cannot be measured on a