Editorial Highlights Newsfront

Total Page:16

File Type:pdf, Size:1020Kb

Editorial Highlights Newsfront Editorial ¾ The 50th Anniversary of the Treaty of Rome and official statistics ......... 2 Highlights ¾ Euro-IND statistical news............................................................................ 4 ¾ Insight on: Celebrating Europe! A Statistical portrait of the European Union 2007 .................................................................................................. 10 Newsfront ¾ News from the Member States .................................................................. 11 ¾ Forthcoming events.................................................................................... 14 ¾ Cool tools and sites: Celebrating Europe! 50thAnniversary of the treaty of Rome........................................................................................................ 20 ¾ Webtrends................................................................................................... 22 ¾ Contact us.................................................................................................... 22 EUROINDICATORS Editorial ¾ The 50th Anniversary of the Treaty of Rome and official statistics The 50th anniversary of the Treaty of Rome is a good reason to look back at what official European statisticians have achieved over the last fifty years. In the early days they put together those national statistics that helped to shape specific EU policies, initially for coal and steel and later for agriculture. For the customs union they harmonised external trade statistics, and for the Common Market they evolved the basis for European production statistics. In the 1970s they began to develop Community classifications and to lay the foundations for the harmonisation of encompassing data sets such as national accounts and the balance of payments. Work on environment statistics started in the 1980s. Considerable progress has also been achieved in the harmonisation of labour market statistics in the 1990s. EMU with its convergence criteria and other statistical requirements, however, has led in the 1990s to a real quantum leap in Community statistics. The European System of Accounts was adopted in 1996. Special attention was devoted to general government accounts in the context of the Growth and Stability Pact. Short-term statistics improved from 1998 onwards, the Harmonised Consumer Price Index became fully operational in time for the introduction of the Euro and Euroindicators are supporting now the monetary policy conduct. Over the last couple of years new methods have been tried (e.g. European sampling, flash estimation) and new policy relevant indicator families have been put together (structural indicators, sustainability indicators, etc.) supporting the conduct of Community policies (e.g. in the context of the Lisbon programme). These are just a few examples of Community statistics, but the embedding of official statistics into a Community framework is far from complete. After all, we are not just seeing new areas of policy for which official statistics are to be made available: at European level we are also seeing new forms of cooperation developing for which official statistics are required. With official statistics becoming ever more European over the last 50 years adequate political, organisational and operational structures were put in place1. In the late 1980s the Council established a planning committee2 to discuss Multi-annual Statistical Programmes3 proposed by the European Commission and to be adopted later by a decision of the European Parliament and the Council. These EU programmes have an integrative impact that should not be underestimated, since the national statistical institutes involved are now enhancing their 1 For an encompassing history of Eurostat see: Alberto De Michelis and Alain Chantraine: MEMOIRS OF EUROSTAT, fifty years serving Europe; Office for Official Publications of the European Communities, Luxembourg, 2003 2 COUNCIL DECISION OF 19 JUNE 1989 ESTABLISHING A COMMITTEE ON THE STATISTICAL PROGRAMMES OF THE EUROPEAN COMMUNITIES (89/382/EEC, EURATOM), OJ L 181, 28.6.1989. 3 The current Community statistical programme — the fourth (see DECISION No 2367/2002/EC OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 16 December 2002) — relates to the period 2003 to 2007 and focuses on economic and monetary union, enlargement, competitiveness, sustainable development and the social agenda. Newsletter –March 2007 2 EUROINDICATORS statistical services in the same fields. The European statistical law4 has established an organisational and legislative framework for the production of official statistics for European Union purposes. Finally in 2005 the European Statistics Code of Practice has been adopted5. All these rules and principles ensure that Community statistics can be drawn up for assisting the formulation, application, monitoring and assessment of Community policies. The production of official statistics is therefore no longer a purely national matter. Official statisticians throughout the European Union (and even beyond) are now working within the context of a European Statistical System that integrates both the official national and European statistical authorities. This system is now solidly based on (1) the subsidiarity and proportionality principle, (2) guidelines, priorities and objectives enshrined in a common programme, (3) common standards and definitions as far as possible, as well as uniform sources and harmonised methods if need be or where appropriate, (4) common legal bases for Community statistics, and sometimes also further-reaching voluntary agreements, (5) data supply programmes established by law and respecting the confidentiality of individual data, and (6) the respect of fifteen principles of the European Statistics Code of Practice. Official statisticians in the European Union have lived up to the European challenge and can thus look back with pride at an impressive record, but the European challenge will continue for them. More countries will have to be integrated into the system and the many countries will have to learn to speak with one voice at world level. New topics will have to be dealt with by everybody and costs will have to be brought down together. New methods will have to be applied and new sources exploited. In the end a truly European System will have to emerge that provides a robust perception framework for the European Union as a whole and all its components by doing more than summing up national statistics. Hervé CARRÉ Eurostat General Director 4 COUNCIL REGULATION (EC) NO 322/97 OF 17 FEBRUARY 1997 ON COMMUNITY STATISTICS, OJ L 52, 22.2.1997. 5 The European Statistics Code of Practice has been adopted by the Statistical Programme Committee on 24 February 2005 and promulgated in the Commission Recommendation of 25 May 2005 on the independence, integrity and accountability of the national and Community statistical authorities. Newsletter –March 2007 3 EUROINDICATORS Highlights ¾ Euro-IND statistical news Balance of Payments EU25 current account deficit 79.4 bn euro - 65.8 bn euro surplus on trade in services The EU25 external current account recorded a deficit of 6.9 billion euro in the fourth quarter of 2006, compared with a deficit of 26.3 billion euro in the fourth quarter of 2005 and a deficit of 21.7 billion euro in the third quarter of 2006. In the fourth quarter of 2006 the EU25 external balance of trade in services recorded a surplus of 16.8 billion euro compared with a surplus of 16.3 billion euro in the fourth quarter of 2005 and a surplus of 16.6 billion euro in the third quarter of 2006. Preliminary results for 2006, compared with 2005, show an increase in the EU25 external current account deficit (-79.4 bn euro compared with -63.8 bn euro) and an increase in the surplus of the services account (65.8 bn euro compared with 56.3 bn euro). These provisional data, issued by Eurostat, the Statistical Office of the European Communities, will be subject to revision. Preliminary results for 2006 – News release N°35/2007 – 9 March 2007 Consumer prices Euro area annual inflation stable at 1.8% - EU stable at 2.1% Euro area annual inflation was 1.8% in February 2007, unchanged compared with January. A year earlier the rate was 2.3%. Monthly inflation was 0.3% in February 2007. EU annual inflation was 2.1% in February 2007, unchanged compared with January. A year earlier the rate was 2.2%. Monthly inflation was 0.3% in February 2007. These figures come from Eurostat, the Statistical Office of the European Communities. Inflation in the EU Member States In February 2007, the lowest annual rates were observed in Malta (0.8%), France, Cyprus and Finland (all 1.2%), and the highest rates in Hungary (9.0%), Latvia (7.2%), Bulgaria and Estonia (both 4.6%). Compared with January, annual inflation rose in thirteen Member States, remained stable in one and fell in twelve. The lowest 12-month averages up to February 2007 were in Finland (1.3%), Poland (1.4%), the Netherlands and Sweden (1.6% each); the highest were in Bulgaria (6.9%), Latvia (6.6%) and Romania (5.8%). Euro area The main components with the highest annual rates in February 2007 were alcohol & tobacco (4.1%), education (3.3%) and housing (3.1%), while the lowest annual rates were observed for communications (-1.7%), recreation & culture (0.2%) and transport (1.0%). Concerning the detailed sub-indices, restaurants & cafés and tobacco (+0.10 percentage points each) had the largest upward impacts on the headline rate, while fuels for transport (-0.22) and telecommunications (-0.11) had the biggest
Recommended publications
  • April 2, 2015 Probabilistic Voting in Models of Electoral Competition By
    April 2, 2015 Probabilistic Voting in Models of Electoral Competition by Peter Coughlin Department of Economics University of Maryland College Park, MD 20742 Abstract The pioneering model of electoral competition was developed by Harold Hotelling and Anthony Downs. The model developed by Hotelling and Downs and many subsequent models in the literature about electoral competition have assumed that candidates embody policies and, if a voter is not indifferent between the policies embodied by two candidates, then the voter’s choices are fully determined by his preferences on possible polices. More specifically, those models have assumed that if a voter prefers the policies embodied by one candidate then the voter will definitely vote for that candidate. Various authors have argued that i) factors other than policy can affect a voter’s decision and ii) those other factors cause candidates to be uncertain about who a voter will vote for. These authors have modeled the candidates’ uncertainty by using a probabilistic description of the voters’ choice behavior. This paper provides a framework that is useful for discussing the model developed by Hotelling and Downs and for discussing other models of electoral competition. Using that framework, the paper discusses work that has been done on the implications of candidates being uncertain about whom the individual voters in the electorate will vote for. 1. An overview The initial step toward the development of the first model of electoral competition was taken by Hotelling (1929), who developed a model of duopolists in which each firm chooses a location for its store. Near the end of his paper, he briefly described how his duopoly model could be reinterpreted as a model of competition between two political parties.
    [Show full text]
  • Harold Hotelling 1895–1973
    NATIONAL ACADEMY OF SCIENCES HAROLD HOTELLING 1895–1973 A Biographical Memoir by K. J. ARROW AND E. L. LEHMANN Any opinions expressed in this memoir are those of the authors and do not necessarily reflect the views of the National Academy of Sciences. Biographical Memoirs, VOLUME 87 PUBLISHED 2005 BY THE NATIONAL ACADEMIES PRESS WASHINGTON, D.C. HAROLD HOTELLING September 29, 1895–December 26, 1973 BY K. J. ARROW AND E. L. LEHMANN AROLD HOTELLING WAS A man of many interests and talents. HAfter majoring in journalism at the University of Washington and obtaining his B.A in that field in 1919, he did his graduate work in mathematics at Princeton, where he received his Ph.D. in 1924 with a thesis on topology. Upon leaving Princeton, he took a position as research associate at the Food Research Institute of Stanford Univer- sity, from where he moved to the Stanford Mathematics Department as an associate professor in 1927. It was during his Stanford period that he began to focus on the two fields— statistics and economics—in which he would do his life’s work. He was one of the few Americans who in the 1920s realized the revolution that R. A. Fisher had brought about in statistics and he spent six months in 1929 at the Rothamstead (United Kingdom) agricultural research station to work with Fisher. In 1931 Hotelling accepted a professorship in the Eco- nomics Department of Columbia University. He taught a course in mathematical economics, but most of his energy during his 15 years there was spent developing the first program in the modern (Fisherian) theory of statistics.
    [Show full text]
  • Introduction to Social Statistics
    SOCY 3400: INTRODUCTION TO SOCIAL STATISTICS MWF 11-12:00; Lab PGH 492 (sec. 13748) M 2-4 or (sec. 13749)W 2-4 Professor: Jarron M. Saint Onge, Ph.D. Office: PGH 489 Phone: (713) 743-3962 Email: [email protected] Office Hours: MW 10-11 (Please email) or by appointment Teaching Assistant: TA Email: Office Hours: TTh 10-12 or by appointment Required Text: McLendon, M. K. 2004. Statistical Analysis in the Social Sciences. Additional materials will be distributed through WebCT COURSE DESCRIPTION: Sociological research relies on experience with both qualitative (e.g. interviews, participant observation) and quantitative methods (e.g., statistical analyses) to investigate social phenomena. This class focuses on learning quantitative methods for furthering our knowledge about the world around us. This class will help students in the social sciences to gain a basic understanding of statistics, whether to understand, critique, or conduct social research. The course is divided into three main sections: (1) Descriptive Statistics; (2) Inferential Statistics; and (3) Applied Techniques. Descriptive statistics will allow you to summarize and describe data. Inferential Statistics will allow you to make estimates about a population (e.g., this entire class) based on a sample (e.g., 10 or 12 students in the class). The third section of the course will help you understand and interpret commonly used social science techniques that will help you to understand sociological research. In this class, you will learn concepts associated with social statistics. You will learn to understand and grasp the concepts, rather than only focusing on getting the correct answers.
    [Show full text]
  • THE HISTORY and DEVELOPMENT of STATISTICS in BELGIUM by Dr
    THE HISTORY AND DEVELOPMENT OF STATISTICS IN BELGIUM By Dr. Armand Julin Director-General of the Belgian Labor Bureau, Member of the International Statistical Institute Chapter I. Historical Survey A vigorous interest in statistical researches has been both created and facilitated in Belgium by her restricted terri- tory, very dense population, prosperous agriculture, and the variety and vitality of her manufacturing interests. Nor need it surprise us that the successive governments of Bel- gium have given statistics a prominent place in their affairs. Baron de Reiffenberg, who published a bibliography of the ancient statistics of Belgium,* has given a long list of docu- ments relating to the population, agriculture, industry, commerce, transportation facilities, finance, army, etc. It was, however, chiefly the Austrian government which in- creased the number of such investigations and reports. The royal archives are filled to overflowing with documents from that period of our history and their very over-abun- dance forms even for the historian a most diflScult task.f With the French domination (1794-1814), the interest for statistics did not diminish. Lucien Bonaparte, Minister of the Interior from 1799-1800, organized in France the first Bureau of Statistics, while his successor, Chaptal, undertook to compile the statistics of the departments. As far as Belgium is concerned, there were published in Paris seven statistical memoirs prepared under the direction of the prefects. An eighth issue was not finished and a ninth one * Nouveaux mimoires de I'Acadimie royale des sciences et belles lettres de Bruxelles, t. VII. t The Archives of the kingdom and the catalogue of the van Hulthem library, preserved in the Biblioth^que Royale at Brussells, offer valuable information on this head.
    [Show full text]
  • Precept 8: Some Review, Heteroskedasticity, and Causal Inference Soc 500: Applied Social Statistics
    Precept 8: Some review, heteroskedasticity, and causal inference Soc 500: Applied Social Statistics Alex Kindel Princeton University November 15, 2018 Alex Kindel (Princeton) Precept 8 November 15, 2018 1 / 27 Learning Objectives 1 Review 1 Calculating error variance 2 Interaction terms (common support, main effects) 3 Model interpretation ("increase", "intuitively") 4 Heteroskedasticity 2 Causal inference with potential outcomes 0Thanks to Ian Lundberg and Xinyi Duan for material and ideas. Alex Kindel (Princeton) Precept 8 November 15, 2018 2 / 27 Calculating error variance We have some data: Y, X, Z. We think the correct model is Y = X + Z + u. We estimate this conditional expectation using OLS: Y = β0 + β1X + β2Z We want to know the standard error of β1. Standard error of β1 r ^2 ^ 1 σu 2 2 SE(βj ) = 2 Pn 2 , where Rj is the R of a regression of 1−Rj i=1(xij −x¯j ) variable j on all others. ^2 Question: What is σu? Alex Kindel (Princeton) Precept 8 November 15, 2018 3 / 27 Calculating error variance P 2 ^2 i u^i σu = DFresid You can adjust this in finite samples by u¯^ (why?) Alex Kindel (Princeton) Precept 8 November 15, 2018 4 / 27 Interaction terms Y = β0 + β1X + β2Z + β3XZ Assume X ∼ N (?; ?) and Z 2 f0; 1g Alex Kindel (Princeton) Precept 8 November 15, 2018 5 / 27 Interaction terms Y = β0 + β1X + β2Z + β3XZ Scenario 1 When Z = 0, X ∼ N (3; 4) When Z = 1, X ∼ N (−3; 2) Do you think an interaction term is justified here? Alex Kindel (Princeton) Precept 8 November 15, 2018 6 / 27 Interaction terms Y = β0 + β1X + β2Z + β3XZ Scenario
    [Show full text]
  • A Meta-Analysis Examining the Impact of Computer-Assisted Instruction on Postsecondary Statistics Education: 40 Years of Research JRTE | Vol
    A Meta-Analysis Examining the Impact of Computer-Assisted Instruction on Postsecondary Statistics Education: 40 Years of Research JRTE | Vol. 43, No. 3, pp. 253–278 | ©2011 ISTE | iste.org A Meta-Analysis Examining the Impact of Computer-Assisted Instruction on Postsecondary Statistics Education: 40 Years of Research Karen Larwin Youngstown State University David Larwin Kent State University at Salem Abstract The present meta-analysis is a comprehensive investigation of the effectiveness of computer-assisted instruction (CAI) on student achievement in postsec- ondary statistics education across a forty year period of time. The researchers calculated an overall effect size of 0.566 from 70 studies, for a total of 219 effect-size measures from a sample of n = 40,125 participants. These results suggest that the typical student moved from the 50th percentile to the 73rd percentile when technology was used as part of the curriculum. This study demonstrates that subcategories can further the understanding of how the use of CAI in statistics education might be maximized. The study discusses im- plications and limitations. (Keywords: statistics education, computer-assisted instruction, meta-analysis) iscovering how students learn most effectively is one of the major goals of research in education. During the last 30 years, many re- Dsearchers and educators have called for reform in the area of statistics education in an effort to more successfully reach the growing population of students, across an expansive variety of disciplines, who are required to complete coursework in statistics (e.g., Cobb, 1993, 2007; Garfield, 1993, 1995, 2002; Giraud, 1997; Hogg, 1991; Lindsay, Kettering, & Siegmund, 2004; Moore, 1997; Roiter, & Petocz, 1996; Snee, 1993;Yilmaz, 1996).
    [Show full text]
  • Report on Exact and Statistical Matching Techniques
    Statistical Policy Working Papers are a series of technical documents prepared under the auspices of the Office of Federal Statistical Policy and Standards. These documents are the product of working groups or task forces, as noted in the Preface to each report. These Statistical Policy Working Papers are published for the purpose of encouraging further discussion of the technical issues and to stimulate policy actions which flow from the technical findings and recommendations. Readers of Statistical Policy Working Papers are encouraged to communicate directly with the Office of Federal Statistical Policy and Standards with additional views, suggestions, or technical concerns. Office of Joseph W. Duncan Federal Statistical Director Policy Standards For sale by the Superintendent of Documents, U.S. Government Printing Office Washington, D.C. 20402 Statistical Policy Working Paper 5 Report on Exact and Statistical Matching Techniques Prepared by Subcommittee on Matching Techniques Federal Committee on Statistical Methodology DEPARTMENT OF COMMERCE UNITED STATES OF AMERICA U.S. DEPARTMENT OF COMMERCE Philip M. Klutznick Courtenay M. Slater, Chief Economist Office of Federal Statistical Policy and Standards Joseph W. Duncan, Director Issued: June 1980 Office of Federal Statistical Policy and Standards Joseph W. Duncan, Director Katherine K. Wallman, Deputy Director, Social Statistics Gaylord E. Worden, Deputy Director, Economic Statistics Maria E. Gonzalez, Chairperson, Federal Committee on Statistical Methodology Preface This working paper was prepared by the Subcommittee on Matching Techniques, Federal Committee on Statistical Methodology. The Subcommittee was chaired by Daniel B. Radner, Office of Research and Statistics, Social Security Administration, Department of Health and Human Services. Members of the Subcommittee include Rich Allen, Economics, Statistics, and Cooperatives Service (USDA); Thomas B.
    [Show full text]
  • Committee on National Statistics
    January 20, 2010 News from the Committee on National Statistics PEOPLE NEWS >We note with great sadness the untimely death on December 17, 2010, from complications of cancer, of Dr. Phyllis Kaniss, executive director of the American Academy of Political and Social Science (AAPSS) and a longtime teaching faculty member at the Annenberg School for Communication at the University of Pennsylvania (see http://www.asc.upenn.edu/News/NewsDetail.aspx?nid=816&ntype=faculty). She was the author of Making Local News (University of Chicago Press, 1991) and The Media and the Mayor’s Race: The Failure of Urban Political Reporting (Indiana University Press, 1995), which won the 1995 Bart Richards Award for media criticism. In 1999, she created the Student Voices Project, a youth civic engagement initiative of the Annenberg Public Policy Center that worked with school systems in cities throughout the country. Dr. Kaniss received a B.A. degree from the University of Pennsylvania and a Ph.D. in regional science from Cornell University. Her connection with CNSTAT is that she worked tirelessly and enthusiastically with our staff and members to organize the very successful joint CNSTAT-AAPSS Symposium on the Federal Statistical System—Recognizing Its Contributions; Moving It Forward that was held at the National Academies on May 8, 2009, and resulted in a special volume of the Annals of the AAPSS, edited by Ken Prewitt, ―The Federal Statistical System: Its Vulnerability Matters More than You Think‖ (http://www.sagepub.com/books/Book235999). We will miss Phyllis’s good cheer and extraordinary skills in furthering the use of social science to address important social problems.
    [Show full text]
  • Methodology, Survey, Statistics, Demography, Economics, History, Political Science, Sociology, Information and Communication
    EQUIPEX Acronym CALL FOR PROPOSALS DIME-SHS 2010 SCIENTIFIC SUBMISSION FORM B Acronym of the project DIME-SHS Titre du projet en Données, Infrastructure, Méthodes d'Enquêtes en Sciences français humaines et sociales Data, Infrastructure, Methods of investigation in the social sciences Project title in English and humanities Name: Laurent Lesnard Institution: Sciences Po Coordinator of the Laboratory: CDSP – Centre de données socio-politiques / Centre for project socio-political data Unit number: UMS 828 Tranche 1/Phase 1 Tranche 2/Phase 2 Requested funding 16 811 197 € 552 407 € □ health, well-being, nutrition and biotechnologies □ environmental urgency, eco-technologies Disciplinary field □ information, communication and nanotechnologies social sciences and humanities □ other disciplinary area Methodology, survey, statistics, demography, economics, Scientific areas history, political science, sociology, information and communication Organization of the coordinating partner Laboratory/Institution(s) Unit number Research organization CDSP / Sciences Po UMS 828 Sciences Po / CNRS Affiliations des partenaires au projet/Organization of the partner(s) Laboratory/Institution(s) Unit number Research organization GENES SES / Ined Université Paris CERLIS / Université Paris Descartes UMR 8070 Descartes/CNRS/Université Paris 3 Telecom ParisTech UMR 5141 Telecom ParisTech/CNRS CNRS/EHESS/INED/ Université GIS Réseau Quetelet de Caen Company Staff size Economic sector EDF R&D Energy 2000 1/66 EQUIPEX Acronym CALL FOR PROPOSALS DIME-SHS 2010 SCIENTIFIC SUBMISSION FORM B 1. SCIENTIFIC ENVIRONMENT AND POSITIONING OF THE EQUIPMENT PROJECT .......... 5 2. TECHNICAL AND SCIENTIFIC DESCRIPTION OF THE ACTIVITIES ......................... 9 2.1. Originality and innovative features of the equipment project..............................9 2.2. Description of the project....................................................................................11 2.2.1 Scientific programme 11 2.2.2 Structure and building of the equipment 16 2.2.3 Technical environment 18 3.
    [Show full text]
  • Overview of Current Population Survey Methodology
    Section on Survey Research Methods – JSM 2012 Overview of Current Population Survey Methodology Yang Cheng Demographic Statistical Methods Division U.S. Census Bureau1, Washington, D.C. 20233-0001 Abstract: The Current Population Survey (CPS) is one of the oldest, largest, and most well recognized surveys in the United States. It produces monthly household information about employment, unemployment, and other characteristics of the civilian non- institutionalized population. In this paper, we will evaluate the CPS sample design and estimation procedure, with specific attention to research problems in the areas of rotating panel design, sample size, systematic-sampling interval, AK composite estimate, and replication variance estimates. We will provide some comments and suggestions for future research, and suggest some ways to improve current CPS methods. Key Words: Current Population Survey, Rotation Panel Design, AK Composite Estimate, Replication Variance Estimate 1. Background The Current Population Survey (CPS), a household sample survey sponsored jointly by the U.S. Census Bureau and the U.S. Bureau of Labor Statistics (BLS), is the primary source of labor force statistics (LFS) for the population of the United States. The CPS is the source of numerous high-profile economic statistics, including the monthly national unemployment rate, and provides data on a wide range of issues relating to employment and earnings. The CPS also collects extensive demographic data that complement and enhance our understanding of labor market conditions in the nation overall, among many different population groups, in the states and in substate areas. The CPS is a source of information not only for economic and social science research, but also for the study of survey methodology.
    [Show full text]
  • 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 .
    [Show full text]
  • INTRODUCTION, HISTORY SUBJECT and TASK of STATISTICS, CENTRAL STATISTICAL OFFICE ¢ SZTE Mezőgazdasági Kar, Hódmezővásárhely, Andrássy Út 15
    STATISTISTATISTICSCS INTRODUCTION, HISTORY SUBJECT AND TASK OF STATISTICS, CENTRAL STATISTICAL OFFICE SZTE Mezőgazdasági Kar, Hódmezővásárhely, Andrássy út 15. GPS coordinates (according to GOOGLE map): 46.414908, 20.323209 AimAim ofof thethe subjectsubject Name of the subject: Statistics Curriculum codes: EMA15121 lect , EMA151211 pract Weekly hours (lecture/seminar): (2 x 45’ lectures + 2 x 45’ seminars) / week Semester closing requirements: Lecture: exam (2 written); seminar: 2 written Credit: Lecture: 2; seminar: 1 Suggested semester : 2nd semester Pre-study requirements: − Fields of training: For foreign students Objective: Students learn and utilize basic statistical techniques in their engineering work. The course is designed to acquaint students with the basic knowledge of the rules of probability theory and statistical calculations. The course helps students to be able to apply them in practice. The areas to be acquired: data collection, information compressing, comparison, time series analysis and correlation study, reviewing the overall statistical services, land use, crop production, production statistics, price statistics and the current system of structural business statistics. Suggested literature: Abonyiné Palotás, J., 1999: Általános statisztika alkalmazása a társadalmi- gazdasági földrajzban. Use of general statistics in socio-economic geography.) JATEPress, Szeged, 123 p. Szűcs, I., 2002: Alkalmazott Statisztika. (Applied statistics.) Agroinform Kiadó, Budapest, 551 p. Reiczigel J., Harnos, A., Solymosi, N., 2007: Biostatisztika nem statisztikusoknak. (Biostatistics for non-statisticians.) Pars Kft. Nagykovácsi Rappai, G., 2001: Üzleti statisztika Excellel. (Business statistics with excel.) KSH Hunyadi, L., Vita L., 2008: Statisztika I. (Statistics I.) Aula Kiadó, Budapest, 348 p. Hunyadi, L., Vita, L., 2008: Statisztika II. (Statistics II.) Aula Kiadó, Budapest, 300 p. Hunyadi, L., Vita, L., 2008: Statisztikai képletek és táblázatok (oktatási segédlet).
    [Show full text]