DOCSLIB.ORG

Statistical Education for Engineers: an Initial Task Force Report Author(S): Robert V

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Statistical Education for Engineers: an Initial Task Force Report Author(S): Robert V Statistical Education for Engineers: An Initial Task Force Report Author(s): Robert V. Hogg Source: The American Statistician, Vol. 39, No. 3 (Aug., 1985), pp. 168-175 Published by: Taylor & Francis, Ltd. on behalf of the American Statistical Association Stable URL: http://www.jstor.org/stable/2683924 Accessed: 24-11-2015 10:16 UTC Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at http://www.jstor.org/page/ info/about/policies/terms.jsp JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact [email protected]. Taylor & Francis, Ltd. and American Statistical Association are collaborating with JSTOR to digitize, preserve and extend access to The American Statistician. http://www.jstor.org This content downloaded from 163.117.20.121 on Tue, 24 Nov 2015 10:16:11 UTC All use subject to JSTOR Terms and Conditions StatisticalEducation forEngineers: An Initial Task Force Report ROBERT V. HOGG et al.* 1. Identifythe customersand theirspecific statistical A three-dayworking conference to developrecommenda- needs; tionsfor the statistical education of engineerstook place at 2. Outlineindustrial training programs for practicing theUniversity of Iowa in July1984. Althoughother areas, engineersand a reasonablefirst course for undergraduate suchas business,chemistry, and biology, are also important engineeringstudents to satisfythese needs, based on the to improvingthe productsof industry,we restrictedour clear identificationof theneed forapplied statistics; considerationto engineering.This reportof the findings 3. Identifyresource materials and definepedagogical addressesthe following questions: Who are thecustomers? approaches,as possible,to makethese programs effective. What are theirstatistical needs? What typesof courses, Of course,given thatthe audiencefor these programs given by the industryfor practicing engineers (called in- consistsof engineersand appliedscientists, statistical ap- house courses in thisreport), are desirable?How do we plicationsshould complementand reinforceengineering approachstatistical education for engineering management? conceptsand methods,particularly in laboratorypractice, Whattypes of resourcesare desirablefor instructional pur- developmentand assessmentof engineeringmodels, col- poses? Whatis the best firstcourse in statisticsfor engi- lectionand interpretationof data, productand processde- neeringstudents? How can we convincethe engineering sign,and productionand processcontrol. professionthat statistical ideas and methodscan help im- To utilizethe training that occurs effectively, engineering provequality and productivityin Americanindustry and managementmust understand and supportthe use of statis- thusits competitive position in theinternational market? ticaltools. Withoutthis understanding, there is a real pos- sibilitythat the statistical techniques will not be used.Although the conferencewas not able to focusextensive efforts on 1. INTRODUCTION howto buildsuch management awareness, there was a clear Therehas beenmuch publicity in recent years concerning consensusthat statistical education for engineers must also Americanindustry's inability to competesuccessfully in the includesome educationfor their managers. internationalmarketplace. Many of theseconcerns focus on One surprisingaspect of thework of theconference was improvingquality and productivity and thus the competitive thedegree to whichall participantsagreed-despite diverse positionof Americanindustry. To addressthese concerns experienceand occupations-on the basic statisticalcon- effectivelyrequires contributions from many disciplines. ceptsthat all engineersneed to appreciate.These include AppliedStatistics is one such discipline.It providesengi- 1. The omnipresenceof variability neerswith such usefultools as statisticalquality control, 2. The use of simple graphicaltools, such as histo- designof experiments, graphical data analysis, and powerful grams,scatterplots, probability plots, residualplots, and buteasy-to-use statistical computer packages. Yet manyof controlcharts thesetools are notbeing used in appropriateplaces. How 3. The basic conceptsof statisticalinference can thisbe in an age whentough worldwide competition 4. The importanceand essentialsof carefullyplanned leavesus littlechoice but to takeadvantage of all technical experimentaldesigns tools available?The answeris, in part,that American en- 5. The philosophiesof Shewhart,Deming, and other gineershave not been taughtabout these tools-and this practitionersconcerning the manufacturing of quality prod- situationneeds to be changed. ucts Withthis conviction, 42 statisticiansand engineerspar- ticipatedin a three-dayconference on statisticaleducation For manystatisticians and engineeringeducators, this forengineers at theUniversity of Iowa, Iowa City,July 23- reportreinforces the realizationthat an interdisciplinary, 25, 1984. cooperativeapproach is necessaryto respondto the chal- Our goals in thisconference were to lengesthat industry faces. It is imperativethat a dialogue betweenthe engineering and statisticalprofessions take place if majorgains are to be made in qualityand productivity. *RobertV. Hogg is Professor,Department of Statisticsand Actuarial Someof us atuniversities believe that there is littlechance Science,University of Iowa, Iowa City,Iowa 52242. The authorwants it madevery clear, however, that the other 41 participantsare also coauthors of persuadingacademic engineers to includemore statistics of thisreport; they and theiraffiliations are listedin the Appendix.In in theircurricula unless industry unmistakably demands it. particular,the team leaders H. T. David, WilliamGolomski, Bert Gunter, It is thereforeessential that influential industrial leaders and WilliamParr were extremelyhelpful, as was RichardGunst, who promotethe importanceof statisticalideas and methods. helpedprepare a preliminarydraft of thisreport. In addition,two other Certainlysuccessful applications provide our most con- statisticians,who did notattend, should be mentioned:Ron Snee, forhis suggestionsin theplanning stages of theconference, and GerryHahn, for vincingargument. This reportprovides some suggestions his recommendationsthroughout the entireprocess, particularly in the on what should be includedin these programs.It is an preparationof themanuscript. importantfirststep, and everyopportunity should be taken 168 The American Statistician, August 1985, Vol. 39, No. 3 ?) 1985 American Statistical Association This content downloaded from 163.117.20.121 on Tue, 24 Nov 2015 10:16:11 UTC All use subject to JSTOR Terms and Conditions to spreadthe word among practicing professional engineers ing,and aid in communication;Process flow diagrams; and and engineeringeducators. We invitereaders to sharetheir Paretodiagrams that quantify the priorities and list the "vital commentsand experienceswith us by contactingHogg. It few" keysof effectiveefforts. is clearthat the same type of conference concerning business 2. Fundamentals(Fund.) of data variabilityand basic statisticswould also be worthwhilefor the improvement of statisticalmethods for process understanding, control, and qualityand productivity.In anycase, progressin statistical improvementare absolutelynecessary. The engineermust educationshould be an ongoingeffort. understandwhat is observed:the actual value plus some randomvariable (noise). To improveproductivity and qual- 2. STATISTICAL AREAS IMPORTANT ity,reduction in the variabilityof theprocess is required. TO INDUSTRY Accordingly,there is a greatneed to have theprocess gen- eratedata that can be usedto monitor,control, and improve thatthe conference addressed was, Who Thefirst question theprocess. are thecustomers and whatare theirstatistical needs? The 3. Designof Experiments (DOX) indicateshow to probe answersresulted in twolists that then formed the rows and a processto getthe most information at minimalcost. We thecolumns of a matrix,the general element of whichre- shouldstress the interactive nature of experimentationand flectsthe amountof knowledgeneeded by thattype of howthis approach fits into the strategy of ongoing improve- engineerin thatparticular statistical area. Of course,the mentand process learning. That is, itshould be emphasized specificsof these classifications vary from company to com- thatstatistical methods are consistentwith and essentialin pany; but the participantsagreed that these-as generic the applicationof the scientificmethod. Topics to be in- distinctions-arebroadly applicable even though some spe- cludedin DOX includemeasurement error, independence, cificclassifications have been omitted. replicationversus duplication, randomization, blocking, main effects,full and fractionalfactorials (as comparedto the 2.1 Customers poorerone-at-a-time approach), interaction, confounding, Customerswere categorized along the following lines: responsesurface design, product robustness, nested designs and variancecomponents, evolutionary operation/plant ex- 1. Engineeringmanagement is responsiblefor the work perimentation,mixture designs, and acceleratedtesting. performedby thestatistically trained engineers. 4. StatisticalInference (S. Inf.)concerns the error struc- 2. Qualityengineers are responsiblefor product, ma- tureof an estimate.That is, engineersshould
Load more

Recommended publications
  • IMS Presidential Address(Es) Each Year, at the End of Their Term, the IMS CONTENTS President Gives an Address at the Annual Meeting
    Volume 50 • Issue 6 IMS Bulletin September 2021 IMS Presidential Address(es) Each year, at the end of their term, the IMS CONTENTS President gives an address at the Annual Meeting. 1 IMS Presidential Addresses: Because of the pandemic, the Annual Meeting, Regina Liu and Susan Murphy which would have been at the World Congress in Seoul, didn’t happen, and Susan Murphy’s 2–3 Members’ news : Bin Yu, Marloes Maathuis, Jeff Wu, Presidential Address was postponed to this year. Ivan Corwin, Regina Liu, Anru The 2020–21 President, Regina Liu, gave her Zhang, Michael I. Jordan Address by video at the virtual JSM, which incorpo- 6 Profile: Michael Klass rated Susan’s video Address. The transcript of the two addresses is below. 8 Recent papers: Annals of 2020–21 IMS President Regina Liu Probability, Annals of Applied Probability, ALEA, Probability Regina Liu: It’s a great honor to be the IMS President, and to present this IMS and Mathematical Statistics Presidential address in the 2021 JSM. Like many other things in this pandemic time, this year’s IMS Presidential Address will be also somewhat different from those in the 10 IMS Awards past. Besides being virtual, it will be also given jointly by the IMS Past President Susan 11 IMS London meeting; Olga Murphy and myself. The pandemic prevented Susan from giving her IMS Presidential Taussky-Todd Lecture; Address last year. In keeping with the IMS tradition, I invited Susan to join me today, Observational Studies and I am very pleased she accepted my invitation. Now, Susan will go first with her 12 Sound the Gong: Going 2020 IMS Presidential Address.
    [Show full text]
  • STATISTICS in the DATA SCIENCE ERA: a Symposium to Celebrate 50 Years of Statistics at the University of Michigan
    STATISTICS IN THE DATA SCIENCE ERA: A Symposium to Celebrate 50 Years of Statistics at the University of Michigan September 20-21, 2019 Rackham Graduate School 915 E. Washington Street Ann Arbor, MI 48109 A Symposium to Celebrate 50 Years of Statistics at the University of Michigan 1 PROGRAM Friday, September 20, 2019 Saturday, September 21, 2019 9:30-10:30 ...............Coffee and Registration 8:30-9:00 ................ Coffee 10:30-10:50 ..............Opening Remarks Invited Session 2 Chair: Snigdha Panigrahi, University of Michigan Xuming He and Dean Anne Curzan 9:00-9:30 ................ Adam Rothman, University of Minnesota Shrinking Characteristics of Precision Matrix Keynote Session 1 Chair: Ambuj Tewari, University of Michigan Estimators 10:50-11:50 ...............Susan Murphy, Harvard University Online Experimentation with Learning Algorithms 9:30-10:00 ............... Bodhisattva Sen, Columbia University in a Clinical Trial Multivariate Rank-based Distribution-free Nonparametric Testing using Measure 11:50-12:00 ...............Group Photo Transportation PROGRAM 12:00-1:30 ................Lunch and Poster Session 10:00-10:30 ............. Min Qian, Columbia University Personalized Policy Learning using Longitudinal University of Michigan Invited Session 1 Chair: Ziwei Zhu, Mobile Health Data 1:30-2:00 .................Sumanta Basu, Cornell University Large Spectral Density Matrix Estimation by 10:30-11:00 .............. Coffee Break Thresholding Invited Session 3 Chair: Kean Ming Tan, University of Michigan 2:00-2:30 .................Anindya Bhadra, Purdue University 11:00-11:30 ............... Ali Shojaie, University of Washington Horseshoe Regularization for Machine Learning in Incorporating Auxiliary Information into Learning Complex and Deep Models Directed Acyclic Graphs 2:30-3:00 .................Coffee Break 11:30-12:00 ..............
    [Show full text]
  • The Completely Sufficient Statistician (Css)
    Kansas State University Libraries New Prairie Press Conference on Applied Statistics in Agriculture 2002 - 14th Annual Conference Proceedings THE COMPLETELY SUFFICIENT STATISTICIAN (CSS) Ralph G. O'Brien Follow this and additional works at: https://newprairiepress.org/agstatconference Part of the Agriculture Commons, and the Applied Statistics Commons This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License. Recommended Citation O'Brien, Ralph G. (2002). "THE COMPLETELY SUFFICIENT STATISTICIAN (CSS)," Conference on Applied Statistics in Agriculture. https://doi.org/10.4148/2475-7772.1196 This is brought to you for free and open access by the Conferences at New Prairie Press. It has been accepted for inclusion in Conference on Applied Statistics in Agriculture by an authorized administrator of New Prairie Press. For more information, please contact [email protected]. Conference on Applied Statistics in Agriculture Kansas State University Applied Statistics in Agriculture 1 THE COMPLETELY SUFFICIENT STATISTICIAN (CSS) Ralph G. O'Brien Department ofBiostatistics and Epidemiology Cleveland Clinic Foundation, Cleveland, OH 44195 Today's ideal statistical scientist develops and maintains a broad range of technical skills and personal qualities in four domains: (1) numeracy, in mathematics and numerical computing; (2) articulacy and people skills; (3) literacy, in technical writing and in programm,ing; and (4) graphicacy. Yet, many of these components are given short shrift in university statistics programs. Can even the best statistician today really be "completely sufficient?" Introduction Let me start by saying that I was absolutely delighted, grateful, and humbled to be invited to give the workshop and keynote address at this meeting.
    [Show full text]
  • A R Software for Wavelets and Statistics
    A R Software for Wavelets and Statistics Here is a list and a brief description of some important R packages related to/that make use of wavelets. Such packages are important as they enable ideas to be reproduced, checked, and modified by the scientific community. This is probably just as important as the advertisement of the ideas through scientific papers and books. This is the ‘reproducible research’ view expressed by Buckheit and Donoho (1995). Making software packages freely available is an extremely valuable service to the community and so we choose to acclaim the author(s) of each package by displaying their names! The descriptions are extracted from each package description from CRAN. The dates refer to the latest updates on CRAN, not the original publication date. Please let me know of any omissions. adlift performs adaptive lifting (generalized wavelet transform) for irregu- larly spaced data, see Nunes et al. (2006). Written by Matt Nunes and Marina Knight, University of Bristol, UK, 2006. brainwaver computes a set of scale-dependent correlation matrices between sets of preprocessed functional magnetic resonance imaging data and then performs a ‘small-world’ analysis of these matrices, see Achard et al. (2006). Written by Sophie Achard, Brain Mapping Unit, University of Cambridge, UK, 2007. CVThresh carries out level-dependent cross-validation for threshold selection in wavelet shrinkage, see Kim and Oh (2006). Written by Donghoh Kim, Hongik University and Hee-Seok Oh, Seoul National University, Korea, 2006. DDHFm implements the data-driven Haar–Fisz transform as described in 6.5, see references therein. Written by Efthimios Motakis, Piotr Fryzlewicz and Guy Nason, University of Bristol, Bristol, UK.
    [Show full text]
  • LORI MCLEOD, Phd
    LORI MCLEOD, PhD Head, Psychometrics Education PhD, Quantitative Psychology, University of North Carolina, Chapel Hill, NC MA, Quantitative Psychology, University of North Carolina, Chapel Hill, NC BS, Statistics, Mathematics Education (graduated summa cum laude), North Carolina State University, Raleigh, NC Summary of Professional Experience Lori McLeod, PhD, is Head of Psychometrics at RTI-HS. Dr. McLeod is a psychometrician with more than 15 years of experience in instrument development and validation, as well as experience conducting systematic assessments of clinical and economic literature and developing appropriate health outcome strategies. In her Psychometrics role, she has conducted many psychometric evaluations of both paper-and-pencil and computer-administered instruments. These investigations have included the assessment of scale reliability, validity, responsiveness, and work to identify PRO responders. In addition, Dr. McLeod has experience conducting and analyzing data from clinical trials and observational studies, including data to document the burden of disease. Dr. McLeod has published numerous related manuscripts in Applied Psychological Measurement, Pharmacoeconomics, Quality of Life Research, and Psychometrika. She has experience in a wide variety of therapeutic areas, including chronic pain, dermatology, oncology, psychiatry, respiratory, sleep disorders, urology, and sexual dysfunction. Dr. McLeod currently serves as the co-leader of the Psychometric Special Interest Group for the International Society on Quality
    [Show full text]
  • Careers for Math Majors
    CAREERS FOR MATH MAJORS Cheryl A. Hallman Asst. Dean / Director Career Center #1 YOU’VE GOT WHAT EMPLOYERS WANT WHAT EMPLOYERS LOOK FOR IN A CANDIDATE? ANALYTICAL & CRITICAL THINKING • Defining and formulating problems • Interpreting data and evaluating results • Understanding patterns and structures • Critical analysis and quantitative thinking RESEARCH • Applying Theoretical Approaches to research problems • Analyzing and interpreting statistics COMMUNICATIONS • Computer modeling • Writing • Explaining complex concepts THE #2 TOP CAREERS BELONG TO MATH MAJORS TOP CAREERS Careercast.com 2019 1. Data Scientist Projected Growth:19.00% 2. Statistician Projected Growth:33.00% TOP CAREERS Careercast.com 2019 3. University Professor Projected Growth:15.00% 7. Information Security Analyst Projected Growth:28.00% TOP CAREERS Careercast.com 2019 8. Mathematician Overall Rating:127 Projected Growth:33.00% 9. Operations Research Analyst Projected Growth:27.00% TOP CAREERS Careercast.com 2019 10. Actuary Projected Growth:22.00% #3 SALARIES AREN’T BAD SALARIES • 15 Most Valuable College Majors – Forbes • 8 College Degrees That Will Earn Your Money Back – Salary.com DATA SCIENTIST Median Salary: $114,520 STATISTICIAN Median Salary: $84,760 UNIVERSITY PROFESSORMedian Salary: $76,000 MATHEMATICIAN Median Salary: $84,760 OPERATIONS RESEARCH ANALYST Median Salary: $81,390 ACTUARY – Median Salary: $101,560 #4 AWESOME AT STANDARDIZED TESTS STANDARDIZED TESTS CAREERS FOR MATH MAJORS BUSINESS & FINANCE – Help determine a company’s financial performance by analyzing balance sheets and income and cash flow statements Actuary Underwriting Accounting Economist Financial Analyst Top Employers PwC, Deloitte, KPMG, EY, Bank of America, Cigna, Ernst & Young, Merrill Lynch, TRW, Goldman Sachs, TD Waterhouse, Charles Schwab, JP Morgan Chase, Citigroup, Morgan Stanley CAREERS FOR MATH MAJORS Data Analytics– Collect, organize and analyze raw data in order to effect positive change and increase efficiency.
    [Show full text]
  • When You Consult a Statistician … What to Expect
    WHEN YOU CONSULT A STATISTICIAN . WHAT TO EXPECT SECTION ON STATISTICAL CONSULTING AMERICAN STATISTICAL ASSOCIATION 2003, 2007, 2013 When you consult a statistician, you enlist the help of a professional who is particularly skilled at solving research problems. The statistician will try to guide you to the best way to obtain an answer to your research questions. The aim of this brochure is to provide you, as a potential client or employer of a statistical consultant, with an overview of what is involved in a consultation. The sections address such topics as when it is useful to consult a statistician, the responsibilities of statistician and client, and what to expect as you initiate and continue a working relationship with a statistician, including a brief summary of the ethical principles that guide statistical work CONTENTS I. WHAT IS STATISTICAL CONSULTATION? A. Do you need a statistical consultant? B. Roles of a Statistical Consultant C. How to Involve a Statistical Consultant Prior to data collection After data have been collected Once the data are analyzed. D. What To Look For in a Statistical Consultant II. THE FIRST CONSULTING SESSION A. Introducing the consultant to the problem B. What materials are needed? C. What do you expect from the statistician? D. Preserving confidentiality is important E. Financial terms F. Schedule for payment G. Other considerations III. RESPONSIBILITIES OF A STATISTICAL CONSULTANT IV. RESPONSIBILITIES OF A CLIENT V. ETHICS IN STATISTICAL CONSULTING I. What is Statistical Consultation? In a statistical consultation, you seek the help of a statistician to select and use the best methods for obtaining and analyzing data for some data-related objective.
    [Show full text]
  • The Inimitable F. N. David: a Renaissance Statistician Amanda L
    The Inimitable F. N. David: A Renaissance Statistician Amanda L. Golbeck and Craig A. Molgaard 1. Highlights Florence Nightingale David (1909–1993) was known to readers of her scholarly publications as “F. N. David” and to her colleagues as “David” or “FND.” David has been recognized as the leading, most accomplished and most memorable British woman statistician of the mid-20th cen- tury ([11], [14]). She was a professor at University College London (UCL), and then at the University of California (UC). When we were graduate students in the early 1980s at UC-Berkeley, where David had an affiliation before and after her retirement from UC-Riverside, she was already a legend in statistics. “Enormous energy” and “prolific output.” These are words that statistician D. E. Barton wrote in an obituary to describe his close UCL colleague. Barton added that these qualities were part of what made David “an exciting colleague to work with” [1]. These qualities are also what make it difficult to pigeonhole David’s illustrious 60-year- long career, which was packed with probability and statis- tical ideas. The numbers alone make clear the extent of her immense energy and output: She wrote nine books, over one-hundred published articles, over fifteen secret (classi- fied) war reports, and various forest service white papers. She was working on a tenth book and other articles and papers when she died. David’s life as a statistician began at age 22, when she walked into Karl Pearson’s office at UCL in 1931 and asked him for a job. Pearson (1857–1936) was in his mid 70s and an enormous intellectual figure, a protégé of Sir Francis Galton (1822–1911).
    [Show full text]
  • Snehalata Huzurbazar Joins SAMSI As Deputy Director
    Volume 5 o nf .i .info Issue 1 Spring 2012 e Institut Sciences Mathematical Applied and Statistical the of Newsletter The The Newsletter of the Statistical and Applied Mathematical Sciences Institute Snehalata Huzurbazar Joins SAMSI as Deputy Director Snehalata Huzurbazar has accepted the position of deputy director of SAMSI, starting July 9. She will be at SAMSI for two years, while taking a leave of absence from the University of Wyoming. Snehalata is replacing Pierre Gremaud, who is finish- ing his fourth year at SAMSI. In her new position, Snehalata will help administer the SAMSI programs and will help to develop future programs. She will also be involved with the education and outreach efforts and will work on staff and personnel issues. Huzurbazar will be a part of the directorate, which comprises the director, three part-time associate directors and the deputy director. Many people may remember Snehalata who was at SAMSI last spring when she was participating in the Analysis of Object- Oriented Data (AOD) program. Her infectious laugh and beauti- ful smile brightened up the hallways of SAMSI. She was very in- volved with the Trees and Dynamics working groups from AOD. Snehalata said she comes from a statistics family. “My father (V. S. Huzurbazar) was a statistician, and Harold Jeffreys’ only statistics student, who started the Mathematics and Statistics Department at the University of Poona in India,” she noted. He Snehalata and her daughter, Asha, taking a hike. was a Fulbright scholar visiting professor at Iowa State when she was born. Her sister, Aparna Huzurbazar is a statistician at Los Institute of Arctic and Alpine Research in Boulder, Colorado.
    [Show full text]
  • Section 4B – Understanding the Median Average
    Section 4B – Understanding the Median Average In the last section, we said that we should use the median as our center and average, when data is skewed or not bell shaped, but what is the median? Why is it more accurate than the mean for skewed data? Let us see if we can get a better understanding of the median average. Definition of the Median average: The median average is the center of the data when the values are put in order from smallest to largest. The median is also called the “50th percentile” since approximately 50% of the numbers in the data set will be greater than the median and 50% of the numbers in the data set will be less than the median. Think of the median as a marker that divides the data in half. That is why it is often called the true center of the data. Skewed data tends to have extremely unusual values. These unusual values (outliers) are very far from the mean. That is why the mean and standard deviation (typical distance from the mean) are not accurate for skewed data. The median is based on how many numbers are in the data set (frequency) and the order of the numbers. If the highest value were 40 or 4000, it would not change the median. How to Calculate the Median Average As with all statistics, rely on technology to calculate. No statistician calculates the median by hand, especially for large data sets. All of them use statistics software or computer software programs. To get a better understanding of the median, we will look at a couple examples where we calculate the median with small data sets.
    [Show full text]
  • PROBABILITY and STATISTICAL INFERENCE Tenth Edition
    Trim Size: 8in x 10in October 5, 2018 A01_HOGG3271_09_SE_FM page i PROBABILITY AND STATISTICAL INFERENCE Tenth Edition ROBERT V. H OGG ELLIOT A. TANIS DALE L. ZIMMERMAN Trim Size: 8in x 10in October 5, 2018 A01_HOGG3271_09_SE_FM page ii Director Portfolio Manager: Deirdre Lynch Senior Portfolio Manager: Patrick Barbara Portfolio Management Assistant: Morgan Danna Content Producer: Tamela Ambush Managing Producer: Karen Wernholm Senior Producer: Aimee Thorne Product Marketing Manager: Yvonne Vannatta Field Marketing Manager: Evan St. Cyr Marketing Assistant: Jon Bryant Senior Author Support/Technology Specialist: Joe Vetere Manager, Rights and Permissions: Gina Cheselka Manufacturing Buyer: Carol Melville, LSC Communications Cover Design, Composition, and Full Service Vendor: Pearson CSC Full Service Project Management: Prince John - Pearson CSC Cover Image: Alexandra Jursova/Moment/Getty Images Copyright © 2020, 2015, 2010 by Pearson Education, Inc. 221 River Street, Hoboken, NJ 07030. All Rights Reserved. Printed in the United States of America. This publication is protected by copyright, and permission should be obtained from the publisher prior to any prohibited reproduction, storage in a retrieval system, or transmission in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise. For information regarding permissions, request forms and the appropriate contacts within the Pearson Education Global Rights & Permissions department, please visit www.pearsoned.com/permissions/. PEARSON, ALWAYS LEARNING are exclusive trademarks owned by Pearson Education, Inc. or its affiliates in the U.S. and/or other countries. Unless otherwise indicated herein, any third-party trademarks that may appear in this work are the property of their respective owners and any references to third-party trademarks, logos or other trade dress are for demonstrative or descriptive purposes only.
    [Show full text]
  • History of Health Statistics
    certain gambling problems [e.g. Pacioli (1494), Car- Health Statistics, History dano (1539), and Forestani (1603)] which were first of solved definitively by Pierre de Fermat (1601–1665) and Blaise Pascal (1623–1662). These efforts gave rise to the mathematical basis of probability theory, The field of statistics in the twentieth century statistical distribution functions (see Sampling Dis- (see Statistics, Overview) encompasses four major tributions), and statistical inference. areas; (i) the theory of probability and mathematical The analysis of uncertainty and errors of mea- statistics; (ii) the analysis of uncertainty and errors surement had its foundations in the field of astron- of measurement (see Measurement Error in omy which, from antiquity until the eighteenth cen- Epidemiologic Studies); (iii) design of experiments tury, was the dominant area for use of numerical (see Experimental Design) and sample surveys; information based on the most precise measure- and (iv) the collection, summarization, display, ments that the technologies of the times permit- and interpretation of observational data (see ted. The fallibility of their observations was evident Observational Study). These four areas are clearly to early astronomers, who took the “best” obser- interrelated and have evolved interactively over the vation when several were taken, the “best” being centuries. The first two areas are well covered assessed by such criteria as the quality of obser- in many histories of mathematical statistics while vational conditions, the fame of the observer, etc. the third area, being essentially a twentieth- But, gradually an appreciation for averaging observa- century development, has not yet been adequately tions developed and various techniques for fitting the summarized.
    [Show full text]