Clear-Sighted Statistics: Module 18: Linear Correlation and Regression
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School of Social Sciences Economics Division University of Southampton Southampton SO17 1BJ, UK Discussion Papers in Economics and Econometrics Mathematics in the Statistical Society 1883-1933 John Aldrich No. 0919 This paper is available on our website http://www.southampton.ac.uk/socsci/economics/research/papers ISSN 0966-4246 Mathematics in the Statistical Society 1883-1933* John Aldrich Economics Division School of Social Sciences University of Southampton Southampton SO17 1BJ UK e-mail: [email protected] Abstract This paper considers the place of mathematical methods based on probability in the work of the London (later Royal) Statistical Society in the half-century 1883-1933. The end-points are chosen because mathematical work started to appear regularly in 1883 and 1933 saw the formation of the Industrial and Agricultural Research Section– to promote these particular applications was to encourage mathematical methods. In the period three movements are distinguished, associated with major figures in the history of mathematical statistics–F. Y. Edgeworth, Karl Pearson and R. A. Fisher. The first two movements were based on the conviction that the use of mathematical methods could transform the way the Society did its traditional work in economic/social statistics while the third movement was associated with an enlargement in the scope of statistics. The study tries to synthesise research based on the Society’s archives with research on the wider history of statistics. Key names : Arthur Bowley, F. Y. Edgeworth, R. A. Fisher, Egon Pearson, Karl Pearson, Ernest Snow, John Wishart, G. Udny Yule. Keywords : History of Statistics, Royal Statistical Society, mathematical methods. -
Applications and Issues in Assessment
meeting of the Association of British Neurologists in April vessels: a direct comparison between intravenous and intra-arterial DSA. 1992. We thank Professor Charles Warlow for his helpful Cl/t Radiol 1991;44:402-5. 6 Caplan LR, Wolpert SM. Angtography in patients with occlusive cerebro- comments. vascular disease: siews of a stroke neurologist and neuroradiologist. AmcincalsouraalofNcearoradiologv 1991-12:593-601. 7 Kretschmer G, Pratschner T, Prager M, Wenzl E, Polterauer P, Schemper M, E'uropean Carotid Surgery Trialists' Collaboration Group. MRC European ct al. Antiplatelet treatment prolongs survival after carotid bifurcation carotid surgerv trial: interim results for symptomatic patients with severe endarterectomv. Analysts of the clinical series followed by a controlled trial. (70-90()',) or tvith mild stenosis (0-299',Y) carotid stenosis. Lantcet 1991 ;337: AstniSuirg 1990;211 :317-22. 1 235-43. 8 Antiplatelet Trialists' Collaboration. Secondary prevention of vascular disease 2 North American Svmptotnatic Carotid Endarterectomv Trial Collaborators. by prolonged antiplatelet treatment. BM7 1988;296:320-31. Beneficial effect of carotid endarterectomv in svmptomatic patients with 9 Murie JA, Morris PJ. Carotid endarterectomy in Great Britain and Ireland. high grade carotid stenosis. VEngl 7,1Med 1991;325:445-53. Br7Si(rg 1986;76:867-70. 3 Hankev CJ, Warlows CP. Svmptomatic carotid ischaemic events. Safest and 10 Murie J. The place of surgery in the management of carotid artery disease. most cost effective way of selecting patients for angiography before Hospital Update 1991 July:557-61. endarterectomv. Bt4 1990;300:1485-91. 11 Dennis MS, Bamford JM, Sandercock PAG, Warlow CP. Incidence of 4 Humphrev PRD, Sandercock PAG, Slatterv J. -
Bayesian Inference for Heterogeneous Event Counts
Bayesian Inference for Heterogeneous Event Counts ANDREW D. MARTIN Washington University This article presents an integrated set of Bayesian tools one can use to model hetero- geneous event counts. While models for event count cross sections are now widely used, little has been written about how to model counts when contextual factors introduce heterogeneity. The author begins with a discussion of Bayesian cross-sectional count models and discusses an alternative model for counts with overdispersion. To illustrate the Bayesian framework, the author fits the model to the number of women’s rights cosponsorships for each member of the 83rd to 102nd House of Representatives. The model is generalized to allow for contextual heterogeneity. The hierarchical model allows one to explicitly model contextual factors and test alternative contextual explana- tions, even with a small number of contextual units. The author compares the estimates from this model with traditional approaches and discusses software one can use to easily implement these Bayesian models with little start-up cost. Keywords: event count; Markov chain Monte Carlo; hierarchical Bayes; multilevel models; BUGS INTRODUCTION Heterogeneity is what makes society and, for that matter, statistics interesting. However, until recently, little attention has been paidto modeling count data observed in heterogeneous clusters (a notable exception is Sampson, Raudenbush, and Earls 1997). Explanations AUTHOR’S NOTE: This research is supported by the National Science Foundation Law and Social Sciences and Methodology, Measurement, and Statistics Sections, Grant SES-0135855 to Washing- ton University. I would like to thank Dave Peterson, Kevin Quinn, and Kyle Saunders for their helpful comments. I am particularly indebted to Christina Wolbrecht for sharing her valuable data set with me. -
Bk Brll 010665.Pdf
12 THE BOOK OF WHY Figure I. How an “inference engine” combines data with causal knowl- edge to produce answers to queries of interest. The dashed box is not part of the engine but is required for building it. Arrows could also be drawn from boxes 4 and 9 to box 1, but I have opted to keep the diagram simple. the Data input, it will use the recipe to produce an actual Estimate for the answer, along with statistical estimates of the amount of uncertainty in that estimate. This uncertainty reflects the limited size of the data set as well as possible measurement errors or miss- ing data. To dig more deeply into the chart, I have labeled the boxes 1 through 9, which I will annotate in the context of the query “What is the effect of Drug D on Lifespan L?” 1. “Knowledge” stands for traces of experience the reasoning agent has had in the past, including past observations, past actions, education, and cultural mores, that are deemed relevant to the query of interest. The dotted box around “Knowledge” indicates that it remains implicit in the mind of the agent and is not explicated formally in the model. 2. Scientific research always requires simplifying assumptions, that is, statements which the researcher deems worthy of making explicit on the basis of the available Knowledge. While most of the researcher’s knowledge remains implicit in his or her brain, only Assumptions see the light of day and 9780465097609-text.indd 12 3/14/18 10:42 AM 26 THE BOOK OF WHY the direction from which to approach the mammoth; in short, by imagining and comparing the consequences of several hunting strategies. -
JSM 2017 in Baltimore the 2017 Joint Statistical Meetings in Baltimore, Maryland, Which Included the CONTENTS IMS Annual Meeting, Took Place from July 29 to August 3
Volume 46 • Issue 6 IMS Bulletin September 2017 JSM 2017 in Baltimore The 2017 Joint Statistical Meetings in Baltimore, Maryland, which included the CONTENTS IMS Annual Meeting, took place from July 29 to August 3. There were over 6,000 1 JSM round-up participants from 52 countries, and more than 600 sessions. Among the IMS program highlights were the three Wald Lectures given by Emmanuel Candès, and the Blackwell 2–3 Members’ News: ASA Fellows; ICM speakers; David Allison; Lecture by Martin Wainwright—Xiao-Li Meng writes about how inspirational these Mike Cohen; David Cox lectures (among others) were, on page 10. There were also five Medallion lectures, from Edoardo Airoldi, Emery Brown, Subhashis Ghoshal, Mark Girolami and Judith 4 COPSS Awards winners and nominations Rousseau. Next year’s IMS lectures 6 JSM photos At the IMS Presidential Address and Awards session (you can read Jon Wellner’s 8 Anirban’s Angle: The State of address in the next issue), the IMS lecturers for 2018 were announced. The Wald the World, in a few lines lecturer will be Luc Devroye, the Le Cam lecturer will be Ruth Williams, the Neyman Peter Bühlmann Yuval Peres 10 Obituary: Joseph Hilbe lecture will be given by , and the Schramm lecture by . The Medallion lecturers are: Jean Bertoin, Anthony Davison, Anna De Masi, Svante Student Puzzle Corner; 11 Janson, Davar Khoshnevisan, Thomas Mikosch, Sonia Petrone, Richard Samworth Loève Prize and Ming Yuan. 12 XL-Files: The IMS Style— Next year’s JSM invited sessions Inspirational, Mathematical If you’re feeling inspired by what you heard at JSM, you can help to create the 2018 and Statistical invited program for the meeting in Vancouver (July 28–August 2, 2018). -
The American Statistician
This article was downloaded by: [T&F Internal Users], [Rob Calver] On: 01 September 2015, At: 02:24 Publisher: Taylor & Francis Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: 5 Howick Place, London, SW1P 1WG The American Statistician Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/utas20 Reviews of Books and Teaching Materials Published online: 27 Aug 2015. Click for updates To cite this article: (2015) Reviews of Books and Teaching Materials, The American Statistician, 69:3, 244-252, DOI: 10.1080/00031305.2015.1068616 To link to this article: http://dx.doi.org/10.1080/00031305.2015.1068616 PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. -
You May Be (Stuck) Here! and Here Are Some Potential Reasons Why
You may be (stuck) here! And here are some potential reasons why. As published in Benchmarks RSS Matters, May 2015 http://web3.unt.edu/benchmarks/issues/2015/05/rss-matters Jon Starkweather, PhD 1 Jon Starkweather, PhD [email protected] Consultant Research and Statistical Support http://www.unt.edu http://www.unt.edu/rss RSS hosts a number of “Short Courses”. A list of them is available at: http://www.unt.edu/rss/Instructional.htm Those interested in learning more about R, or how to use it, can find information here: http://www.unt.edu/rss/class/Jon/R_SC 2 You may be (stuck) here! And here are some potential reasons why. I often read R-bloggers (Galili, 2015) to see new and exciting things users are doing in the wonderful world of R. Recently I came across Norm Matloff’s (2014) blog post with the title “Why are we still teaching t-tests?” To be honest, many RSS personnel have echoed Norm’s sentiments over the years. There do seem to be some fields which are perpetually stuck in decades long past — in terms of the statistical methods they teach and use. Reading Norm’s post got me thinking it might be good to offer some explanations, or at least opinions, on why some fields tend to be stubbornly behind the analytic times. This month’s article will offer some of my own thoughts on the matter. I offer these opinions having been academically raised in one such Rip Van Winkle (Washington, 1819) field and subsequently realized how much of what I was taught has very little practical utility with real world research problems and data. -
Statistical Tips for Interpreting Scientific Claims
Research Skills Seminar Series 2019 CAHS Research Education Program Statistical Tips for Interpreting Scientific Claims Mark Jones Statistician, Telethon Kids Institute 18 October 2019 Research Skills Seminar Series | CAHS Research Education Program Department of Child Health Research | Child and Adolescent Health Service ResearchEducationProgram.org © CAHS Research Education Program, Department of Child Health Research, Child and Adolescent Health Service, WA 2019 Copyright to this material produced by the CAHS Research Education Program, Department of Child Health Research, Child and Adolescent Health Service, Western Australia, under the provisions of the Copyright Act 1968 (C’wth Australia). Apart from any fair dealing for personal, academic, research or non-commercial use, no part may be reproduced without written permission. The Department of Child Health Research is under no obligation to grant this permission. Please acknowledge the CAHS Research Education Program, Department of Child Health Research, Child and Adolescent Health Service when reproducing or quoting material from this source. Statistical Tips for Interpreting Scientific Claims CONTENTS: 1 PRESENTATION ............................................................................................................................... 1 2 ARTICLE: TWENTY TIPS FOR INTERPRETING SCIENTIFIC CLAIMS, SUTHERLAND, SPIEGELHALTER & BURGMAN, 2013 .................................................................................................................................. 15 3 -
Statistics Making an Impact
John Pullinger J. R. Statist. Soc. A (2013) 176, Part 4, pp. 819–839 Statistics making an impact John Pullinger House of Commons Library, London, UK [The address of the President, delivered to The Royal Statistical Society on Wednesday, June 26th, 2013] Summary. Statistics provides a special kind of understanding that enables well-informed deci- sions. As citizens and consumers we are faced with an array of choices. Statistics can help us to choose well. Our statistical brains need to be nurtured: we can all learn and practise some simple rules of statistical thinking. To understand how statistics can play a bigger part in our lives today we can draw inspiration from the founders of the Royal Statistical Society. Although in today’s world the information landscape is confused, there is an opportunity for statistics that is there to be seized.This calls for us to celebrate the discipline of statistics, to show confidence in our profession, to use statistics in the public interest and to champion statistical education. The Royal Statistical Society has a vital role to play. Keywords: Chartered Statistician; Citizenship; Economic growth; Evidence; ‘getstats’; Justice; Open data; Public good; The state; Wise choices 1. Introduction Dictionaries trace the source of the word statistics from the Latin ‘status’, the state, to the Italian ‘statista’, one skilled in statecraft, and on to the German ‘Statistik’, the science dealing with data about the condition of a state or community. The Oxford English Dictionary brings ‘statistics’ into English in 1787. Florence Nightingale held that ‘the thoughts and purpose of the Deity are only to be discovered by the statistical study of natural phenomena:::the application of the results of such study [is] the religious duty of man’ (Pearson, 1924). -
School of Social Sciences Economics Division University of Southampton Southampton SO17 1BJ, UK
School of Social Sciences Economics Division University of Southampton Southampton SO17 1BJ, UK Discussion Papers in Economics and Econometrics Professor A L Bowley’s Theory of the Representative Method John Aldrich No. 0801 This paper is available on our website http://www.socsci.soton.ac.uk/economics/Research/Discussion_Papers ISSN 0966-4246 Key names: Arthur L. Bowley, F. Y. Edgeworth, , R. A. Fisher, Adolph Jensen, J. M. Keynes, Jerzy Neyman, Karl Pearson, G. U. Yule. Keywords: History of Statistics, Sampling theory, Bayesian inference. Professor A. L. Bowley’s Theory of the Representative Method * John Aldrich Economics Division School of Social Sciences University of Southampton Southampton SO17 1BJ UK e-mail: [email protected] Abstract Arthur. L. Bowley (1869-1957) first advocated the use of surveys–the “representative method”–in 1906 and started to conduct surveys of economic and social conditions in 1912. Bowley’s 1926 memorandum for the International Statistical Institute on the “Measurement of the precision attained in sampling” was the first large-scale theoretical treatment of sample surveys as he conducted them. This paper examines Bowley’s arguments in the context of the statistical inference theory of the time. The great influence on Bowley’s conception of statistical inference was F. Y. Edgeworth but by 1926 R. A. Fisher was on the scene and was attacking Bayesian methods and promoting a replacement of his own. Bowley defended his Bayesian method against Fisher and against Jerzy Neyman when the latter put forward his concept of a confidence interval and applied it to the representative method. * Based on a talk given at the Sample Surveys and Bayesian Statistics Conference, Southampton, August 2008. -
The Political Methodologist Newsletter of the Political Methodology Section American Political Science Association Volume 10, Number 2, Spring, 2002
The Political Methodologist Newsletter of the Political Methodology Section American Political Science Association Volume 10, Number 2, Spring, 2002 Editor: Suzanna De Boef, Pennsylvania State University [email protected] Contents Summer 2002 at ICPSR . 27 Notes on the 35th Essex Summer Program . 29 Notes from the Editor 1 EITM Summer Training Institute Announcement 29 Note from the Editor of PA . 31 Teaching and Learning Graduate Methods 2 Michael Herron: Teaching Introductory Proba- Notes From the Editor bility Theory . 2 Eric Plutzer: First Things First . 4 This issue of TPM features articles on teaching the first Lawrence J. Grossback: Reflections from a Small course in the graduate methods sequence and on testing and Diverse Program . 6 theory with empirical methods. The first course presents Charles Tien: A Stealth Approach . 8 unique challenges: what to expect of students and where to begin. The contributions suggest that the first gradu- Christopher H. Achen: Advice for Students . 10 ate methods course varies greatly with respect to goals, content, and rigor across programs. Whatever the na- Testing Theory 12 ture of the course and whether you teach or are taking John Londregan: Political Theory and Political your first methods course, Chris Achen’s advice for stu- Reality . 12 dents beginning the methods sequence will be excellent reading. Rebecca Morton: EITM*: Experimental Impli- cations of Theoretical Models . 14 With the support of the National Science Foun- dation, the empirical implications of theoretical models (EITM) are the subject of increased attention. Given the Articles 16 empirical nature of the endeavor, EITM should inspire us. Andrew D. Martin: LATEX For the Rest of Us . -
The Contrasting Strategies of Almroth Wright and Bradford Hill to Capture the Nomenclature of Controlled Trials
6 Whose Words are they Anyway? The Contrasting Strategies of Almroth Wright and Bradford Hill to Capture the Nomenclature of Controlled Trials When Almroth Wright (1861–1947) began to endure the experience common to many new fathers, of sleepless nights caused by his crying infant, he characteristically applied his physiological knowledge to the problem in a deductive fashion. Reasoning that the child’s distress was the result of milk clotting in its stomach, he added citric acid to the baby’s bottle to prevent the formation of clots. The intervention appeared to succeed; Wright published his conclusions,1 and citrated milk was eventually advocated by many paediatricians in the management of crying babies.2 By contrast, Austin Bradford Hill (1897–1991) approached his wife’s insomnia in an empirical, inductive manner. He proposed persuading his wife to take hot milk before retiring, on randomly determined nights, and recording her subsequent sleeping patterns. Fortunately for domestic harmony, he described this experiment merely as an example – actually to perform it would, he considered, be ‘exceedingly rash’.3 These anecdotes serve to illustrate the very different approaches to therapeutic experiment maintained by the two men. Hill has widely been credited as the main progenitor of the modern randomised controlled trial (RCT).4 He was principally responsible for the design of the first two published British RCTs, to assess the efficacy of streptomycin in tuberculosis5 and the effectiveness of whooping cough vaccine6 and, in particular, was responsible for the introduction of randomisation into their design.7 Almroth Wright remained, throughout his life, an implacable opponent of this ‘statistical method’, preferring what he perceived as the greater certainty of a ‘crucial experiment’ performed in the laboratory.