2012 (EBOOK) statistical thinking improving business performance

FM.indd 3/2/2012 4:29:40 PM Statistical Thinking FM.indd 3/2/2012 4:29:40 PM Wiley & SAS Business Series The Wiley & SAS Business Series presents books that help senior-level managers with their critical management decisions Titles in the Wiley and SAS Business Series include: Activity-Based Management for Financial Institutions: Driving Bottom-Line Results by Brent Bahnub Branded! How Retailers Engage Consumers with Social Media and Mobility by Bernie Brennan and Lori Schafer Business Analytics for Customer Intelligence by Gert Laursen Business Analytics for Managers: Taking Business Intelligence beyond Reporting by Gert Laursen and Jesper Thorlund Business Intelligence Competency Centers: A Team Approach to Maximizing Competitive Advantage by Gloria J Miller, Dagmar Brautigam, and Stefanie Gerlach Business Intelligence Success Factors: Tools for Aligning Your Business in the Global Economy by Olivia Parr Rud Case Studies in Performance Management: A Guide from the Experts by Tony C Adkins CIO Best Practices: Enabling Strategic Value with Information Technology, Second Edition by Joe Stenzel Credit Risk Assessment: The New Lending System for Borrowers, Lenders, and Investors by Clark Abrahams and Mingyuan Zhang Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring by Naeem Siddiqi Customer Data Integration: Reaching a Single Version of the Truth, by Jill Dyche and Evan Levy Demand-Driven Forecasting: A Structured Approach to Forecasting by Charles Chase Enterprise Risk Management: A Methodology for Achieving Strategic Objectives by Gregory Monahan Executive’s Guide to Solvency II by David Buckham, Jason Wahl, and Stuart Rose Fair Lending Compliance: Intelligence and Implications for Credit Risk Management by Clark R Abrahams and Mingyuan Zhang FM.indd 3/2/2012 4:29:40 PM Foreign Currency Financial Reporting from Euros to Yen to Yuan: A Guide to Fundamental Concepts and Practical Applications by Robert Rowan Information Revolution: Using the Information Evolution Model to Grow Your Business by Jim Davis, Gloria J Miller, and Allan Russell Manufacturing Best Practices: Optimizing Productivity and Product Quality by Bobby Hull Marketing Automation: Practical Steps to More Effective Direct Marketing by Jeff LeSueur Mastering Organizational Knowledge Flow: How to Mae Knowledge Sharing Work by Frank Leistner Performance Management: Finding the Missing Pieces (to Close the Intelligence Gap) by Gary Cokins Performance Management: Integrating Strategy Execution, Methodologies, Risk, and Analytics by Gary Cokins Retail Analytics: The Secret Weapon by Emmett Cox Social Network Analysis in Telecommunications by Carlos Andre Reis Pinheiro The Business Forecasting Deal: Exposing Bad Practices and Providing Practical Solutions by Michael Gilliland The Data Asset: How Smart Companies Govern Their Data for Business Success by Tony Fisher The Executive’s Guide to Enterprise Social Media Strategy: How Social Networks Are Radically Transforming Your Business by David Thomas and Mike Barlow The New Know: Innovation Powered by Analytics by Thornton May The Value of Business Analytics: Identifying the Path to Profitability by Evan Stubbs Visual Six Sigma: Making Data Analysis Lean by Ian Cox, Marie A Gaudard, Philip J Ramsey, Mia L Stephens, and Leo Wright For more information on any of the above titles, please visit www.wiley.com FM.indd 3/2/2012 4:29:40 PM FM.indd 3/2/2012 4:29:40 PM Statistical Thinking Improving Business Performance Second Edition Roger Hoerl and Ron Snee John Wiley & Sons, Inc FM.indd 3/2/2012 4:29:40 PM Copyright © 2012 by Roger W Hoerl and Ronald D Snee All rights reserved Published by John Wiley & Sons, Inc., Hoboken, New Jersey The first edition of this book was Statistical Thinking: Improving Business Performance, Roger Hoerl and Ronald D Snee, Duxbury Press, 2002 (0-534-38158-8) Published simultaneously in Canada 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, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 646-8600, or on the Web at www.copyright.com Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748-6011, fax (201) 748-6008, or online at http://www.wiley.com/go/permissions Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose No warranty may be created or extended by sales representatives or written sales materials The advice and strategies contained herein may not be suitable for your situation You should consult with a professional where appropriate Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages For general information on our other products and services or for technical support, please contact our Customer Care Department within the United States at (800) 7622974, outside the United States at (317) 572-3993 or fax (317) 572-4002 Wiley also publishes its books in a variety of electronic formats Some content that appears in print may not be available in electronic books For more information about Wiley products, visit our web site at www.wiley.com Library of Congress Cataloging-in-Publication Data: ISBN: 978-1-1180-9477-8 Printed in the United States of America 10 FM.indd 3/2/2012 4:29:40 PM To the memory of Arthur E Hoerl, Horace P Andrews, and Ellis R Ott—great teachers from whom we learned much about the theory and use of statistical thinking FM.indd 3/2/2012 4:29:40 PM FM.indd 3/2/2012 4:29:40 PM A p p e n d i x I t Critical Values Table I.1 t curve Central area –t critical value Central Area Captured: Confidence Level: Degrees of Freedom t critical value 80 80% 90 90% 95 95% 98 98% 99 99% 998 99.8% 999 99.9% 3.08 1.89 1.64 1.53 1.48 6.31 2.92 2.35 2.13 2.02 12.71 4.30 3.18 2.78 2.57 31.82 6.97 4.54 3.75 3.37 63.66 9.93 5.84 4.60 4.03 318.31 23.33 10.21 7.17 5.89 636.62 31.60 12.92 8.61 6.86 10 1.44 1.42 1.40 1.38 1.37 1.94 1.90 1.86 1.83 1.81 2.45 2.37 2.31 2.26 2.23 3.14 3.00 2.90 2.82 2.76 3.71 3.50 3.36 3.25 3.17 5.21 4.79 4.50 4.30 4.14 5.96 5.41 5.04 4.78 4.59 11 12 13 14 15 1.36 1.36 1.35 1.35 1.34 1.80 1.78 1.77 1.76 1.75 2.20 2.18 2.16 2.15 2.13 2.72 2.68 2.65 2.62 2.60 3.11 3.06 3.01 2.98 2.95 4.03 3.93 3.85 3.79 3.73 4.44 4.32 4.22 4.14 4.07 16 17 18 19 20 1.34 1.33 1.33 1.33 1.33 1.75 1.74 1.73 1.73 1.73 2.12 2.11 2.10 2.09 2.09 2.58 2.57 2.55 2.54 2.53 2.92 2.90 2.88 2.86 2.85 3.69 3.65 3.61 3.58 3.55 4.02 3.97 3.92 3.88 3.85 21 22 23 24 25 1.32 1.32 1.32 1.32 1.32 1.72 1.72 1.71 1.71 1.71 2.08 2.07 2.07 2.06 2.06 2.52 2.51 2.50 2.49 2.49 2.83 2.82 2.81 2.80 2.79 3.53 3.51 3.49 3.47 3.45 3.82 3.79 3.77 3.75 3.73 (continued) 497 bapp09.indd 497 3/2/2012 4:20:53 PM 498â•… ╛╛S t a t i s t i c a l T h i n k i n g Table I.1╇ (Continued) Central Area Captured: Confidence Level: z Critical Values bapp09.indd 498 80 80% 90 90% 95 95% 98 98% 99 99% 998 99.8% 999 99.9% 26 27 28 29 30 1.32 1.31 1.31 1.31 1.31 1.71 1.70 1.70 1.70 1.70 2.06 2.05 2.05 2.05 2.04 2.48 2.47 2.47 2.46 2.46 2.78 2.77 2.76 2.76 2.75 3.44 3.42 3.41 3.40 3.39 3.71 3.69 3.67 3.66 3.65 40 60 120 1.30 1.30 1.29 1.68 1.67 1.66 2.02 2.00 1.98 2.42 2.39 2.36 2.70 2.66 2.62 3.31 3.23 3.16 3.55 3.46 3.37 ∞ 1.28 1.645 1.96 2.33 2.58 3.09 3.29 3/2/2012 4:20:53 PM A p p e n d i x J Standard Normal Probabilities (Cumulative z Curve Areas) TABLE J.1 Tabulated area = probability Standard normal (z) curve z z 00 01 02 03 04 05 06 07 08 09 –3.8 –3.7 –3.6 –3.5 0000 0001 0002 0002 0000 0001 0002 0002 0000 0001 0001 0002 0000 0001 0001 0002 0000 0001 0001 0002 0000 0001 0001 0002 0000 0001 0001 0002 0000 0001 0001 0002 0000 0001 0001 0002 0000 0001 0001 0002 –3.4 –3.3 –3.2 –3.1 –3.0 0003 0005 0007 0010 0013 0003 0005 0007 0009 0013 0003 0005 0006 0009 0013 0003 0004 0006 0009 0012 0003 0004 0006 0008 0012 0003 0004 0006 0008 0011 0003 0004 0006 0008 0011 0003 0004 0005 0008 0011 0003 0004 0005 0007 0010 0002 0003 0005 0007 0010 –2.9 –2.8 –2.7 –2.6 –2.5 0019 0026 0035 0047 0062 0018 0025 0034 0045 0060 0018 0024 0033 0044 0059 0017 0023 0032 0043 0057 0016 0023 0031 0041 0055 0016 0022 0030 0040 0054 0015 0021 0029 0039 0052 0015 0021 0028 0038 0051 0014 0020 0027 0037 0049 0014 0019 0026 0036 0048 –2.4 –2.3 –2.2 –2.1 –2.0 0082 0107 0139 0179 0228 0080 0104 0136 0174 0222 0078 0102 0132 0170 0217 0075 0099 0129 0166 0212 0073 0096 0125 0162 0207 0071 0094 0122 0158 0202 0069 0091 0119 0154 0197 0068 0089 0116 0150 0192 0066 0087 0113 0146 0188 0064 0084 0110 0143 0183 (continued) 499 bapp10.indd 499 3/3/2012 12:27:30 PM 500â•… ╛╛S t a t i s t i c a l T h i n k i n g TABLE J.1╇ (Continued) z 00 01 02 03 04 05 06 07 08 09 –1.9 –1.8 –1.7 –1.6 –1.5 0287 0359 0446 0548 0668 0281 0351 0436 0537 0655 0274 0344 0427 0526 0643 0268 0336 0418 0516 0630 0262 0329 0409 0505 0618 0256 0322 0401 0495 0606 0250 0314 0392 0485 0594 0244 0307 0384 0475 0582 0239 0301 0375 0465 0571 0233 0294 0367 0455 0559 –1.4 –1.3 –1.2 –1.1 –1.0 0808 0968 1151 1357 1587 0793 0951 1131 1335 1562 0778 0934 1112 1314 1539 0764 0918 1093 1292 1515 0749 0901 1075 1271 1492 0735 0885 1056 1251 1469 0721 0869 1038 1230 1446 0708 0853 1020 1210 1423 0694 0838 1003 1190 1401 0681 0823 0985 1170 1379 –0.9 –0.8 –0.7 –0.6 –0.5 1841 2119 2420 2743 3085 1814 2090 2389 2709 3050 1788 2061 2358 2676 3015 1762 2033 2327 2643 2981 1736 2005 2296 2611 2946 1711 1977 2266 2578 2912 1685 1949 2236 2546 2877 1660 1922 2206 2514 2843 1635 1894 2177 2483 2810 1611 1867 2148 2451 2776 –0.4 –0.3 –0.2 –0.1 –0.0 3446 3821 4207 4602 5000 3409 3783 4168 4562 4960 3372 3745 4129 4522 4920 3336 3707 4090 4483 4880 3300 3669 4052 4443 4840 3264 3632 4013 4404 4801 3228 3594 3974 4364 4761 3192 3557 3936 4325 4721 3156 3520 3897 4286 4681 3121 3483 3859 4247 4641 Standard normal (z) curve Tabulated area = probability z 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 bapp10.indd 500 00 5000 5398 5793 6179 6554 6915 7257 7580 7881 8159 8413 8643 8849 9032 9192 9332 9452 9554 9641 9713 01 5040 5438 5832 6217 6591 6950 7291 7611 7910 8186 8438 8665 8869 9049 9207 9345 9463 9564 9649 9719 02 5080 5478 5871 6255 6628 6985 7324 7642 7939 8212 8461 8686 8888 9066 9222 9357 9474 9573 9656 9726 03 5120 5517 5910 6293 6664 7019 7357 7673 7967 8238 8485 8708 8907 9082 9236 9370 9484 9582 9664 9732 04 5160 5557 5948 6331 6700 7054 7389 7704 7995 8264 8508 8729 8925 9099 9251 9382 9495 9591 9671 9738 z 05 5199 5596 5987 6368 6736 7088 7422 7734 8023 8289 8531 8749 8944 9115 9265 9394 9505 9599 9678 9744 06 5239 5636 6026 6406 6772 7123 7454 7764 8051 8315 8554 8770 8962 9131 9279 9406 9515 9608 9686 9750 07 08 09 5279 5675 6064 6443 6808 7157 7486 7794 8078 8340 8577 8790 8980 9147 9292 9418 9525 9616 9693 9756 5319 5714 6103 6480 6844 7190 7517 7823 8106 8365 8599 8810 8997 9162 9306 9429 9535 9625 9699 9761 5359 5753 6141 6517 6879 7224 7549 7852 8133 8389 8621 8830 9015 9177 9319 9441 9545 9633 9706 9767 3/3/2012 12:27:31 PM Appendix J â•… 501 TABLE J.1╇ (Continued) bapp10.indd 501 z 00 01 02 03 04 05 06 07 08 09 2.0 2.1 2.2 2.3 2.4 9772 9821 9861 9893 9918 9778 9826 9864 9896 9920 9783 9830 9868 9898 9922 9788 9834 9871 9901 9925 9793 9838 9875 9904 9927 9798 9842 9878 9906 9929 9803 9846 9881 9909 9931 9808 9850 9884 9911 9932 9812 9854 9887 9913 9934 9817 9857 9890 9916 9936 2.5 2.6 2.7 2.8 2.9 9938 9953 9965 9974 9981 9940 9955 9966 9975 9982 9941 9956 9967 9976 9982 9943 9957 9968 9977 9983 9945 9959 9969 9977 9984 9946 9960 9970 9978 9984 9948 9961 9971 9979 9985 9949 9962 9972 9979 9985 9951 9963 9973 9980 9986 9952 9964 9974 9981 9986 3.0 3.1 3.2 3.3 3.4 9987 9990 9993 9995 9997 9987 9991 9993 9995 9997 9987 9991 9994 9995 9997 9988 9991 9994 9996 9997 9988 9992 9994 9996 9997 9989 9992 9994 9996 9997 9989 9992 9994 9996 9997 9989 9992 9995 9996 9997 9990 9993 9995 9996 9997 9990 9993 9995 9997 9998 3.5 3.6 3.7 3.8 9998 9998 9999 9999 9998 9998 9999 9999 9998 9999 9999 9999 9998 9999 9999 9999 9998 9999 9999 9999 9998 9999 9999 9999 9998 9999 9999 9999 9998 9999 9999 9999 9998 9999 9999 9999 9998 9999 9999 1.0000 3/3/2012 12:27:31 PM bapp10.indd 502 3/3/2012 12:27:31 PM Index Page numbers followed by f indicates figure and t indicates table A abandoned call rates regression analysis with multiple predictor variables, 254–61 regression analysis with one predictor variable, 246–54 abstracts, 441 accounts payable cause-and-effect matrix, 195f accounts receivable, days sales outstanding (DSO), 474–75 accuracy, 85, 86 Ackoff, Robert, 24, 300 adjusted R-squared statistic, 260 advertising, effect on sales business models, 233 data over time, 51–52 design of experiments, 273, 299–301 experiment description, 24–27 hypothesis, 27–28 outcomes, 28–29 statistical inference tools, 308, 317 statistical thinking strategy, 39–41, 42, 43 variation reduction, 47 affinity diagrams, 125–26, 179–85 airline industry, 367 alternative hypothesis, 334–35 American Statistical Association, 450, 451 analysis of means (ANOM), 344 analysis of results, 286, 294–98 analysis of variance (ANOVA), 341–43, 346, 374, 410 analyze, DMAIC step, 128, 132–35 Anheuser-Busch, Inc., 24–29 See also advertising, effect on sales ANOM (analysis of means), 342 ANOVA (analysis of variance), 341–43, 346, 374, 410 antagonistic interactions, 291 area under the curve, 375 assignable causes, 46–47 assumption, 41, 51, 107, 156, 236, 241, 244–45, 249–52, 260, 271, 308, 312– 13, 320–22, 324–29, 331–32, 340–47, 357–59, 363, 370–72, 384–85, 388, 391, 393, 404, 408, 410–11 attribute data, 155 automobile accidents, 146, 147f automobile industry, capability ratios, 222 Automotive Industry Action Group, 87 average and averages confidence intervals, 318–21, 327–28, 357 defined, 13–14 hypothesis tests, 340–43 sample average, 383–86 average deviation, 14, 217, 391–92 “average of the averages,” 384 B baby wipe flushability case study See flushable wipes case study Baggerly, Keith, 143 balance of trade, 6f bank customers, average number arriving per hour, 52 banking telephone waiting time case study, 101–5, 107–8, 144, 159 bell curve, 358, 365 benchmarking, 11–12, 77–78 biased sampling, 152–53 billing process, 4–5, 59, 64, 73–75 binomial distributions, 366f, 368–70, 372 blending models, 234 blocking, 301–03, 303 blood glucose levels, 414–20 Borror, C M., 87 Box, G E P., 51, 231, 232, 236, 270, 285 Box-Cox family of power transformations, 408 box plots, 109, 164–67 Brache, A P., 81 brainstorming, 177–79 Brassard, M., 180–83 budgeting, for experiments, 285 Budweiser, 24–29 See also advertising, effect on sales Burdick, R K., 87 Busch, August, Jr., 24 business improvement, 3–21 case study, 4–5 global competition requirements, 5–7 introduction, management approaches, 10–14, 421–23 model for, 7–10 503 bindex.indd 503 3/5/2012 8:14:35 PM 504â•… ╛╛I n d e x business improvement (continued) statistical thinking applications, 18–20 statistical thinking principles, 12–18 summary, 421 business plan issues, affinity diagram depicting, 180–83 business processes, 55–89 analysis of, 65–78 dynamic nature of, 41, 51–53 examples, 56–62 identification of, 63–65 introduction, 55–56 measurement, 74–78, 82–87 principles of statistical thinking, 15–18 reengineering of, 10, 11, 122, 491–96 SIPOC model, 56, 62–63, 75 summary, 421–22 systems, 79–82 C call centers, 58, 66–67, 234, 301–302 See also abandoned call rates capability analysis, 140f, 205–7, 222–26 casinos, 358 causal relationships, tools for understanding cause-and-effect (C&E) matrix, 194–97 failure mode and effects analysis (FMEA), 197–201 Five Whys, 125, 201–2 Is-is not analysis, 124–25, 202–5 within process improvement framework, 140f See also cause-and-effect diagrams cause-and-effect diagrams, 191–93 benefits, 191 examples, 191–92 limitations, 191 newspaper error case study, 133f procedure, 193 purpose, 191 resin manufacturing case study, 98–99, 109, 191 tips, 193 variations, 193 cause-and-effect (C&E) matrix, 194–97 c charts See control charts C&E (cause-and-effect) matrix, 194–97 center points, 303 central limit theorem, 383–86 Champy, James, 487 characterization stage of experimentation, 280, 282 checksheets, 106, 124, 143–46 chemical analysis measurement, 83, 84f chemical reactions, 235 chemical yield study, 475–78 chi-squared test, 344–45, 366f, 481, 485–88 Cicero, 483 Clemente, Roberto, 139 bindex.indd 504 common-cause variation, 17–18, 46–50, 107–8 communication one-paragraph summaries or abstracts, 441 presentations, 439–41 of transformation results, 409–10 written reports, 441–43 computer passwords, 159 confidence intervals for average, 318–21 defined, 308 for difference between two averages, 327–28, 357 for difference between two proportions, 328–29 examples, 310–11 general structure, 320 and hypothesis testing, 333–34, 337, 339 for proportion, 322–23 for regression coefficient, 325–26 for standard deviation, 323–25 vs prediction intervals, 317–18 confidentiality, 450 conflict resolution, 435–36 Consumer Price Index (CPI), 390 continuous data, 164–71, 339–44, 365–66 continuous probability distributions, 374– 82, 484–89 control, DMAIC step, 128, 135–36 control charts, 207–22 benefits, 208 examples, 208–9 integer data, 364 limitations, 208 procedure, 209–21 purpose, 207 tips, 221–22 Coombes, Kevin, 143 copying documents, 66, 68f core processes, 80–81, 82f corporate tax payments, cause and effect diagram, 191–92 corporate travel agencies, customer satisfaction survey, 148, 149f correlation coefficients, 171 credit cards account opening process, 60, 73 age distribution of holders, 161–62 collection strategies, 233 payment amount vs balance, 168 currency, 397 customer complaint resolution, 176f customer feedback, 75 customer order process, 58–59, 73–74, 76 customers defined, 9, 62, 66, 420–21 SIPOC model, 62 cycle time 3/5/2012 8:14:35 PM i n d e x˘â•›ï»¿â•›â•… billing example, 4–5, 59–60 continuous data, 365 continuous distribution, 374 credit approval example, 209f, 215, 357 customer order-to-receipt example, 162, 343 exponential distribution, 380–82 measurement, 75–76 mortgage approval example, 327–28, 330, 334 non-value-added processes, 66 probability distributions, 366 reengineering, 494 small business loans example, 357–58, 362 transformation, 408–10 D data types, 363–66 vs information, 10 data analysis, 42 data collection for hypothesis testing, 335 over time, 51–52 subject matter knowledge, 40–41, 50–51 tools, 140f, 142–50 See also sampling data mining, 236–37 data quality, 151–54, 335 data quantity, 154–55, 335 Davis, P., 233 decrease the variation, 47–48 defectives, 212, 213 defects, 212 define, DMAIC step, 128, 130–31 Deming, W Edwards, 15 deployment flowcharts, 175 descriptive statistics, 156 See also graphical analysis design of experiments (DOE), 269–305 approaches to, 270–73 blocking, 301–2 center points, 303 defined, 109 examples, 273–79 four or more variables, 467–78 introduction, 269–70 larger experiments, 299–301 process, 284–86 randomization, 285–86, 302–03 statistical approach to, 270, 271–73, 279–86 summary, 425 three-factor experiments, 292–99 tips, 304 two-factor experiments, 286–92 df (degrees of freedom), 313 diabetes control case study, 414–20, 422–23 bindex.indd 505 505 discrete data, 155, 344–45 discrete probability distributions, 367–73, 483–84, 486t discrete variables, 164–67 distribution process, is-is not analysis for late delivery in, 203, 204f DMAIC (Define, Measure, Analyze, Improve and Control), 12, 128–37 document process flow, 105, 106f, 172–77 DOE See design of experiments (DOE) dot method of multivoting, 190 Dow Jones Industrial Average (DJIA), 45– 47, 51, 395–97 draft, military, 151 Drucker, Peter F., 55 Duke University, 143 dynamic nature of business processes, 41, 51–53 E economy, global competition‘s impact on, 5–7 Edison, Thomas A., 93, 270 efficiency, 299 election polls and forecasts, 150, 152–53 electronics assembly, 71–72 empirical models, 235–36 Empirical Rule, 224–25, 379–80 employee retention case study, 311–13 employee satisfaction, 75 exercise equipment service contract sales, 330–33, 334, 335–39, 366, 368–70 experiments, scientific method step, 43 See also design of experiments (DOE) exponential distribution, 366f, 380–82 exponentially weighted moving average (EWMA), 221 extrapolation, 254 F facilities evaluation, 63 factors, 292 failure mode and effects analysis (FMEA), 197–201 F-distribution, 366f, 387, 398, 481, 488t Fernandez, M M., 76 finite population, sampling from, 350–52 finite population correction factor (FPCF), 350 first principle of statistical thinking, 15–16 fishbone diagram, 32 Fisher, Ronald, 387 Five Whys, 125, 201–2 flowcharts, 105, 106f, 172–77 flushable wipes case study brainstorming, 178 description, 111–17 is-is not analysis, 124–25, 203 multivoting, 189 problem-solving framework, 124–25, 126 3/5/2012 8:14:35 PM 506â•… ╛╛i n d e x FMEA (failure mode and effects analysis), 197–201 FPCF (finite population correction factor), 350 fractional factorial design, 281, 467t frames, 153, 314–16 French curve, 237 F-test, 343 Fuller, F T., 71 future values, prediction interval for, 326–27 G Gaussian distribution, 358, 365 General Electric, 12 global competition, economic impact, 5–7 goodwill case study, 398–407 Goodyear, 180–83 Gosset, William S., 388–89 graphical analysis, 156–71 box plots, 109, 164–67 histograms, 107–8, 126, 160–64 within process improvement framework, 140f run charts, 156–58 See also Pareto charts; scatter plots graphics, use in written reports, 442 Greek letters, 318 H Hammer, M., 81, 491 haphazard experimentation, 270 Hau, I., 30–39 hidden plant, 72–74 hidden replication, 299 hiring process, 60 histograms, 107–8, 126, 160–64 homogeneity of variance, 344 Hunter, J S., 51, 232, 236, 270, 285 Hunter, W G., 51, 232, 236, 270, 285 Huxley, Thomas, 307 hypothesis, scientific method step, 43 hypothesis testing and confidence intervals, 333–34, 337, 339 consistency between data and null hypothesis, 335–37 for continuous data, 339–44 data obtainment, 335 for discrete data, 344–45 examples, 311–13 formal statement of hypothesis, 334–35 process, 330–33 for regression analysis, 345–46 rejecting or failing to reject, 338–39 role of, 308–9 sample size formulas, 346–52 bindex.indd 506 I IBM, 81f ID (interrelationship digraphs), 109, 126, 185–88 improve, DMAIC step, 128, 135 improvement See business improvement Ince, D., 143 increase the average, 47 independence, 244, 389 independent, 125, 188, 244, 249, 298, 308, 327, 370, 390–91 independently, 26, 175, 180, 233, 271, 290, 328–29, 341–44 inference See statistical inference theory infinite population, sampling from, 347–50 inflation, 397 information, vs data, 10 information technology, 94 in-person interviews, 448 inputs, 15, 62 institutionalization, 47 insurance, 48–50 integer data, 364–65 interaction, 240, 289–91, 397–98 interpretation of data, 50–51 interrelationship digraphs (ID), 109, 126, 185–88 interviews, 448, 449–50 inverse transformations, 398, 407–8 investment growth, 392–96 is/is not analysis, 124–25, 202–5 J JMP, 159, 169, 240–41 Juran, Joseph M., 15 K Kepner, C H., 124 KISS principle, 69–70 knowledge-based tools, 172–205 affinity diagrams, 125–26, 179–85 brainstorming, 177–79 cause-and-effect (C&E) matrix, 194–97 failure mode and effects analysis (FMEA), 197–201 Five Whys, 125, 201–2 flowcharts, 105, 106f, 172–77 interrelationship digraphs, 109, 126, 185–88 is-is not analysis, 124–25, 202–5 multivoting, 189–91 within process improvement framework, 140f See also cause-and-effect diagrams L labeled scatter plots, 169 Latin letters, 316 Lean Six Sigma, 12, 94, 128, 130 3/5/2012 8:14:35 PM i n d e x˘â•›ï»¿â•› â•… learning organizations, 11 least squares, 240–46 legal document process failures, FMEA for, 198f, 199 Likert scales, 150 linear combinations, 389–92 linear relationship, 235 loading, 121–22 loan process, 357–58, 362 logarithms, 395, 398, 407–8 logistics, suboptimization, 79 Lyday, R W., 87 M machine learning, 236 mail surveys, 448 main effects, 294–98 management approaches, 10–14 manufacturing benchmarking, 78 business process, 57–58 hidden plant, 72 interference, 357 process measurement, 83, 84f Six Sigma approach, 12 suboptimization, 79 tolling operations, 62 See also resin manufacturing case study marketing and market research personnel, 424 mathematical French curve See regression analysis mathematical statistics, 45 measure, DMAIC step, 128, 131–32 measurement process, 74–78, 82–87 See also sampling mechanistic models, 235 media, interpretation of data, 50–51 meetings, 434–35, 439–41 microbrewing process, 69, 70f mixed-level factorial experiments, 474–75 models and model building, 231–69 defined, 109, 231 examples, 232–35 introduction, 231–32 limitations of using existing process data, 264–65 strategies, 231–32 summary, 424–25 tips for, 265–66 types, 235–37 uses of, 237 verification, 461–64 See also design of experiments (DOE); regression analysis Moen, R D., 270 Montgomery, D C., 87, 233, 270, 352 multicollinearity, 261–64 bindex.indd 507 507 multiple responses, trade-offs among, 459–61 multivoting, 113, 126, 189–91 N National Institute of Standards and Technology (NIST), 86 negative effect, 262 newspaper accuracy case study, 130–37 New York City rental market, 80 New York Times, poor quality data, 143 Nightingale, Florence, 479 NIST (National Institute of Standards and Technology), 86 Nolan, T W., 270 “no-memory” property, 380 nominal data, 363–64 nonresponse bias, 152 non-value-added work, 65–74 examples, 65–69 hidden plant, 72–74 process complexity increase, 69–72 normal distributions, 358, 365, 366f, 374–82, 499–501 N over method of multivoting, 190 np charts, 212, 213 null hypothesis, 334, 335–39 O objectives, 284, 359 observations, 43 Occupational Safety and Hazards Administration (OSHA) incident rates, 218–19 OFAAT (one-factor-at-a-time) experimentation, 271 off-list pricing, top-down flowchart for, 177f OFIs See opportunities for improvements (OFIs) ogive, 160 oil companies, blending models, 234 Olympic medals, 365 one average, hypothesis tests for, 340 one-factor-at-a-time (OFAAT) experimentation, 271 one observation, prediction interval for, 321–22 one-paragraph summaries, 441 one-sided alternative, 335 operations management personnel, 426 opportunities for improvements (OFIs), 65–78 non-value-added work and complexity, 65–74 process measurements, 74–78 optimization stage of experimentation, 280, 282 order taking process, 58–59, 73–74, 76 order-to-receipt cycle time, 162f ordinal data, 364 3/5/2012 8:14:35 PM 508â•… ╛╛i n d e x OSHA (Occupational Safety and Hazards Administration) incident rates, 218–19 outliers, 253, 455 output benchmarking, 78 outputs, 15, 62, 64 P Pareto charts, 158–60 attribute data, 124 banking telephone waiting time case study, 103f, 104f benefits, 158 common-cause variation, 108 examples, 159 limitations, 159 multivoting results, 189–90 newspaper accuracy case study, 132f, 134f procedure, 159–60 purpose, 158 soccer team improvement case study, 32f, 34f tips, 160 variations, 160 passwords, 159 patent filings, 173–75 p charts, 212, 213 peaking, 121–22 Peck, E A., 233 personnel requisition process, 60 pharmaceutical industry, Plackett-Burman design, 281, 467t plastic parts case study, 277–79 Pohlen, Carolyn, 431, 414–20 Pohlen, Tom, 414–20 Poisson distribution, 211–12, 366f, 367, 370–73 polymer production process, 66, 67f Porter, Cole, 467 practical significance, 338–39 precise measurements, 85, 86–87 prediction intervals defined, 308 for future y values using regression equation, 326–27 for one observation, 321–22 vs confidence intervals, 317–18 predictions, 237 presentations, 439–41 presidential elections, 152–53 probability density functions (continuous probability distributions), 374–82, 484–89 probability distributions, 366–82 continuous distributions, 374–82, 484–89 defined, 358, 366–67 discrete distributions, 367–73, 483–84, 486t bindex.indd 508 linear combinations, 389–92 normal distributions, 358, 365, 366f, 374–82, 499–501 sampling distributions, 382–89 standard deviation of, 371 transformations, 392–410 types, 358 probability mass functions (discrete probability distributions), 367–73, 483–84, 486t probability theory, 358–63 problem solving, 123–27 basic framework, 123–27 case studies, 111–23 defined, 10 summary, 424 tools overview, 140 See also specific tools process benchmarking, 78 process capability, 140f, 205–7, 222–26 process complexity, 69–72 processes, generally See business processes process improvement, 105–10 basic framework, 105–10 case studies, 95–102 defined, 9–10 summary, 424 tools overview, 140 See also specific tools process measures, 74–78, 82–87 process resources, 64 process stability, 85, 106–7, 140f, 205–7 See also control charts process steps, SIPOC model, 62 product, defined, 62, 66 product development case study, 271–75, 301–302 product display, three factor experiment, 292–98 projects business process analysis, 88–89 debriefing, 427 design of experiments, 304–5 model building, 269 process improvement and problem solving framework, 227 selection of, 53–54 statistical engineering, 137–38 statistical interference, 353, 411–12 summary, 427 proportions, confidence interval for, 322–23, 328–29 proposals, for experiments, 286 Provost, L P., 2670 public accounting services, box plot of hourly rates paid for, 164–65 pulse advertising, 29 3/5/2012 8:14:35 PM i n d e x˘â•›ï»¿â•›â•… Q quadratic model, 239–40 qualitative variables, regression analysis using, 455–59 quality function deployment (QFD), 195 R randomization, in experiments, 285–86, 302–3 random sampling, 41, 150, 151 rare occurrences, 212–13 R charts, 210, 213, 217 Readers’ Digest, 152 realized revenue case study, 117–23 rebates, 117–23 reengineering, 10, 11, 122, 491–96 regression analysis, 237–46 abuses of, 265 building models using, 240, 240f factor effects, 298 French curve, 237 hypothesis testing for, 343–44 least squares, 240–46 low adjusted R-squared value issue, 454–55 model verification, 461–64 multicollinearity, 263–66 multiple predictor variables, 239–40, 254–61 normal distributions as basis for, 374 one predictor variable, 238–39, 246–54 outliers, 455 process overview, 238–39 tips for, 266–67 trade-offs among multiple responses, 459–61 of two-level factorial design, 291–92 when some, or all variables are qualitative, 455–59 regression coefficients, 325–26, 345–46 rent controls, 80 replication, 285, 299 reproducibility, 87 residual analysis, 249–53 See also transformations resin manufacturing case study cause-and-effect diagram, 98–99, 109, 191 control chart, 208 description, 95–101 scatter plots, 99f, 168 special- vs common-cause identification, 107, 108, 109–10 stratification, 141 response surface experiments, 475–78 results, analysis of, 286, 294–98 results benchmarking, 78 reverse transformations, 398 bindex.indd 509 509 rework, 66, 72–73 Ricoh, 95–101 risk priority number (RPN), 200 root causes, 125, 127 root sum of squares formula, 390 R-squared statistic, 260, 454–55 “rule of thumb” for sample size, 155, 344–45 Rummler, G A., 81 run charts, 156–58 S safety data, 201–2, 218–19 sales, advertising’s effect on See advertising, effect on sales sales departments, suboptimization, 79–80 sample average, 383–86 sample size, 155, 344–50, 446 sample variance, 386–87 sampling for data quality, 151–54 from finite population, 350–52 from infinite population, 347–50 random sampling, 41, 150, 151 sample size, 154–55 tips, 150–55 use of, 83 sampling distributions, 382–89 central limit theorem, 383–86 importance of, 382 sample average, 383–86 sample variance, 386–87 scatter plots, 167–71 benefits, 168 correlation coefficients, 171 defined, 108–9 examples, 168 limitations, 168 procedure, 169 purpose, 167–68 resin manufacturing case study, 99f, 168 root cause identification, 127 tips, 169–71 variations, 169 S charts, 210, 213 scientific method, 43 Scott Paper Company, 111–17 scrap, 66, 72 screening stage of experimentation, 280 second principle of statistical thinking, 16 self-managed work teams, 11 self-selected opinion polls, 451 semiconductor manufacturing process, 57–58 sequential nature, 40 service, defined, 62, 66 service contract sales, 330–33, 334, 335–39, 366, 368–70 3/5/2012 8:14:36 PM 510â•… ╛╛i n d e x Shewhart, Walter, 216, 217 SIPOC model, 56, 62–63, 75 Six Sigma, 12 small business loans, 357–58, 362 snapshot studies, 51 Snedecor, George W., 387 Snee, R D., 87 soccer team improvement case study, 30–41, 42, 43, 52, 159 soft drinks, 308–9 special-cause variation detection and elimination of, 46–50, 107, 207, 220 is-is not analysis, 203 vs common-cause variation, 17–18, 107 See also control charts square root, 398, 407–8 stability, 85, 106–7, 140f, 205–7 See also control charts standard deviation confidence interval for, 323–25 defined, 14 hypothesis tests, 342–44 of linear combination, 391–92 of probability distribution, 371 sample variance, 387–88 standard normal distributions, 366f, 376–82, 499–501 standards, 107–8, 126 statistical engineering, 93–138 defined, 44, 94 DMAIC framework, 128–37 introduction, 93–94 principles, 94–95 summary, 422 tools overview, 140 See also problem solving; process improvement statistical inference theory, 355–412 applications, 356–58 data types, 363–66 framework for, 358–63 introduction, 355–56 linear combinations, 389–92 probability distributions, 358, 366–82 sampling distributions, 382–89 summary, 425 transformations, 392–410 statistical inference tools, 307–53 confidence and prediction intervals, 317–29, 479–81 defined, 307 examples, 310–13 hypothesis tests, 330–52, 479–81 introduction, 307–9 process of applying, 314–17 summary, 425 types, 308–9 bindex.indd 510 statistical methods, 43–45 See also models and model building; statistical inference tools statistical significance, 338–39 statistical thinking applications of, 18–20 for business improvement, 3–21, 421–23 diabetes control case study, 414–20, 422–23 future research, 425–27 history of use, implementation steps, 13f principles of, 12–18 review of, 420–27 vs scientific method, 43 See also business processes; statistical engineering statistical thinking strategy, 23–54 advertising case study, 24–29, 39–41 business improvement process application, 41–43 dynamic nature of business processes, 51–53 introduction, 23–24 soccer team improvement case study, 30–41 summary, 421 synergy between data and subject matter knowledge, 50–51 variation in business processes, 45–50 statistics system, 44f Stengel, Casey, 427 stimulus-response relationship, 25–28 stock market, 393–97 straight-line model, 238–39 strategy See statistical thinking strategy stratification, 108, 125, 127, 141–42 structural variation, 52, 122 subject matter knowledge, 40–41, 42, 50–51, 127 suboptimization, 79–80, 81–82 subprocesses, 64–65 summary of project, 441 supermarket chain, three-factor experiment, 292–99 supersaturation, 25, 28–29 suppliers, SIPOC model, 62 surveys, 146–50 benefits, 148 confidentiality, 450 defined, 445–46 examples, 148 integrity issues, 450–51 limitations, 148 methods, 447–48 parties conducting, 447, 449–50 procedure, 148–50 purpose, 148 3/5/2012 8:14:36 PM i n d e x˘â•›ï»¿â•›â•… questions, 448–49 resources, 451 sample size, 446 tips, 150 variations, 150 synergistic interactions, 291 synergy between data and subject matter knowledge, 40–41, 50–51 systems of processes, 79–82 T tables, 442 tax payments, cause and effect diagram, 191–92 t critical values, 497–98 t-distribution, 366f, 387–89, 479 teams, 429–37 benefits of, 429–30 conflict resolution, 435–36 formation, 431 ingredients of successful, 432 meetings, 434–35 project selection, 431–32 reasons for failure, 436–37 stages of growth, 432–33 when to use, 430–31 telecommunications services, customer measure, 76 telemarketing, 58, 286–92, 301–2 telephone interviews, 448 telephone repair centers, business models, 234–35 telephone waiting time, 101–5, 107–8, 144, 159 test programs, 283–84 theoretical models, 235–36 third principle of statistical thinking, 16–17 3-sigma limits, 216–17 standard deviation limits, 216–17 three-factor experiments, 292–99 time, 365 time plots, 156–58 time series model, 236 tolling operations, 62 top-down flowcharts, 175 total quality management (TQM), 11 trade-offs among multiple responses, 459–61 transformations, 392–410 communication of results, 409–10 defined, 397 examples, 395–97 goodwill case study, 398–407 inverse transformations, 398 logarithms, 395, 398 process of applying, 407–10 reverse transformations, 398 square root, 398 use of, 392–95 bindex.indd 511 511 t-ratio, 259, 298, 345–46 travel agencies, customer satisfaction survey, 148 Tregoe, B B., 124 t-test, 340–41, 361–62, 374, 387 two averages, hypothesis tests for comparing, 340–41 two-factor experiments, 286–92 two-level factorial design, 287, 291–92, 467t, 468–74 two-sample t-test, 340–41 U u charts, 210–11 unapplied labor rate, 223–24 uncertainty, 154 V value-added work, 65–69 variables identification in experimental design, 284–85 in statistical inference, 359–61 variance inflation factors (VIF), 263–64 variances, hypothesis tests for comparing, 343–44 variation common-cause variation, 17–18, 46–50, 107–8 defined, 13–14 presence in all processes, 40, 45–50 special-cause variation, 17–18, 46–50, 107, 203, 207, 220 third principle of statistical thinking, 16–18 VIF (variance inflation factors), 263–64 Vining, C G., 233 W wages and earnings, 6f waste, 66 water absorbency case study, 310–11 weight gain, 233 Wheeler, D J., 87 Wooden, John, 439 work teams See teams World Vision, 61–62 written reports, 441–43 X X-bar charts, 210, 213, 217 Y Yellowstone National Park, 232, 236 Z z-distribution, 377–79, 388, 479 z-test, 345 3/5/2012 8:14:36 PM ... development of the concept of statistical thinking was the 2002 publication of the first textbook on the topic, Statistical Thinking; Improving Business Performance In the 10 years that followed... www.wiley.com FM.indd 3/2/2012 4:29:40 PM FM.indd 3/2/2012 4:29:40 PM Statistical Thinking Improving Business Performance Second Edition Roger Hoerl and Ron Snee John Wiley & Sons, Inc FM.indd... & Sons, Inc., Hoboken, New Jersey The first edition of this book was Statistical Thinking: Improving Business Performance, Roger Hoerl and Ronald D Snee, Duxbury Press, 2002 (0-534-38158-8) Published