实践中体会统计学概念

由于统计学是一门边缘学科,不仅涵盖了数学知识、经济知识,而且还涵盖了管理知识,所以有《社会经济统计学》、《统计学原理》、《数理统计概率论》等教科书,无论是在实践工作中还是在教学中,最难体会的是统计学概念。一、正确理解运用平均数平均数是在同质总体中各单位某一数量标志值的一般水平或代表性水平。从定义上可以看出,平均数具有代表性,但当今是经济飞速发展的社会,这个代表性很难解决实际问题,特别是在经济领域,平均数是为了便于比较而产生,但全世界每个地区经济发展很不均衡,中国内地和沿海地区经济发展也是如此。例如人均收入,…     

关于社会经济统计学三个基本理论问题的再认识

关于社会经济统计学三个基本理论问题的再认识杨立勋目前，理论界又一次展开了关于统计学科体系建设的广泛讨论，其中，对社会经济统计学的讨论成为近期焦点之一。以理论界看，绝大部分学者认为社会经济统计学的体系不完善，部分内容与数理统计相重复，而且有部分内容过于...     

社会经济统计学与数理统计学的比较

多年来,在统计学界关于社会经济统计学与数理统计学的“是非”问题一直争论不休,本文通过对这两种统计学在研究对象、研究范围和内容,以及二者的发展与创新机理和研究理论几个方面的比较,阐述了保持两门统计学并存的必要性。     

中国统计学科问题研究（三）：学科理论体系研究

随着对什么是统计学这一问题认识的不断深入,对于统计学的研究对象、研究内容、研究范围及统计的学科性质的认识也更加清晰,在此基础上如何建立科学的统计学科理论体系就成为研究的当务之急。统计学科理论体系的构建,应注意把握好社会经济统计和数理统计的融合问题,切忌将二者简单拼凑在一起,而是应在明确统计学概念、研究对象的基础上,将符合统计学定义的内容划归统计学理论体系,将不符合的内容划归相应的学科。应该澄清的是学科的合并与结合是完全不同的两回事。实际上,在进一步明确什么是统计学之后,我们研究的目标是建立在此意…     

求本索源 浅探统计学

文章通过对统计学成长历程的回顾,从统计学研究的对象、方法及目的几个方面论述了统计学研究内容上的共性,并据此对统计学的学科特性作了探讨。     

构建大统计学理论促进统计事业发展

中国目前正处在新旧经济体制的变革时期,面对新的经济体制任何学科都需要调整自己的位置,制定自己的发展战略。中国统计学面对市场经济如何调整自己,中国统计和主流学派是怎样一种模式。近年来,我国统计部门提出了大统计的设想,并得到国家技术监督局的认可,在所制定的《中华人民共和国学科分类与代码》中,将原来属于经济学科的统计学提升为与经济学科相平行的学科,显然统计学的学科地位提高了,这无疑是个好事。但是由于两门统计学——社会经济统计学与数理统计学先后形成,所以在不同的国家不同时期存在着偏颇。同时在统计学界,在理论和实际部门由于地位不同,认识也不同。为了研究社会各部门对大统计设想的看法和态度,我们对全国30个市和地区的大专院校和其它统计工作和科研单位的统计工作者进行了问卷调查。     

构建统计学专业课程实验教学体系

我国加入WTO后,对外开放不断扩大,市场经济发展向纵深推进,对统计的要求提高到一个新的高度,统计学专业课程的传统教学模式已经不适应社会经济发展对统计人才培养的要求,构建统计学专业课程实验教学体系已势在必行。本文从构建统计学专业课程实验教学体系的必要性、内容和实施三方面阐述如何构建统计学专业课程实验教学体系。     

中外统计学著作比较分析及对我国统计教育的思考

二十多年来,我国的经济得到了快速的发展,取得了举世瞩目的成就,与此同时,我国的统计学也取得了极大的进步,在理论上吸收并进一步地发扬了优秀的统计思想,在应用中也积累了宝贵的经验。尤其是1992年,统计学作为一级学科地位的确立,极大地提高了统计学的地位,引起了广泛的关注,也进一步推动了统计事业的发展。随着经济、社会的快速发展,势必要求统计学必须与之相适应,加强统计在经济、社会等领域中的应用。     

中国统计学科问题研究：（四）从学科建设看统计学科的生命力

我国统计学科体系的完善，不仅影响着未来统计学的发展，也关系到统计的生命力能否持久、社会效用能否增强的问题。在对统计学科问题研究的同时，引发了我们对与统计学科体系密切相关的统计的生命力及杜会效用问留的思考。统计学科体系研究中一个不可忽视的问题是统计学科是否仍具有旺盛生命力和积极的社会效用。以及如怀使统计学科保持其生命力。因此，我们认为在统计学科建设研究中，应就未来统计学科的生命力及其社会效用进行论证分析。一、从社会发展的需要看统计学科的生命力统计学的发展表明，统计学的内容、方法的形成与社会生产力的…     

统计学的学科分类

统计学的学科分类西安统计学院吴敏统计学作为一门方法论科学，迄今已有三百余年的历史。特别是近二百年来，它的基本理论和方法被广泛地应用于自然、社会和思维等各个领域的科学研究与实践活动中，现代统计学无论是理论还是应用方法．都已形成了枝叶繁茂的分科群体。但是...     

The need for new statistical legislation for the UK

Summary. The paper is written to inform public discussion on whether or not statistical legislation for the UK is needed and, if so, on its nature and content. A brief account of the background to the current position is given. The Government's stated intention is to create an 'independent statistical service' and a discussion of the meaning of independence in the context of official stat- istics and governance arrangements is provided. Recent international experience is described and the Statistics Acts of some other countries are used to distil the key features of Statistics Acts in other countries. The arguments for and against possible legislation are described. Whether or not a Statistics Act is desirable for the UK depends strongly on the legislation being well framed. There are several key issues on which Parliament would need to develop an informed view and these are set out towards the end of the paper.     

Advanced Placement Statistics: Expanding the Scope of Statistics Education

A list of consequential developments in the field of statistics for the past quarter-century must include the creation and implementation of the Advanced Placement (AP) program in Statistics. This program has introduced millions of high school students to our discipline over the past 18 years, contributing to the large increase in the number of undergraduate students pursuing statistics as their major in college. ASA members and leaders have played a substantial role in shaping this program and furthering its success.     

A Festschrift for Adrian Baddeley

This article introduces a special issue of the Australian and New Zealand Journal of Statistics, being a Festschrift for Adrian Baddeley on the occasion of his 65th birthday.     

A conversation with Elja Arjas (Helsinki,November 2021 and March 2022)

Statistics as an independent scientific discipline is relatively young in Finland. Its active history stretches back roughly a century, with the past 50 years signifying a period of growth. Few other academics such as Elja Arjas, now professor emeritus at University of Helsinki, have played a prominent role in establishing statistics in Finland. This conversation tries to illuminate how this came to happen and what was needed to push statistics as a discipline to a firmer ground. We do not have a looking glass at our disposal but will nevertheless also try make some predictions about the future.     

Developing “A Framework for Planning U.S. Federal Statistics, 1978–1989”

The Statistical Policy Division of the Office of Management and Budget has the overall responsibility for the planning and coordination of U.S. government statistics. The present staff of the Statistical Policy Division is attempting, through an integrated publication entitled “A Framework for Planning U.S. Federal Statistics, 1978–1989,” to state its perspective on necessary developments in the coming years. This article illustrates the character of the Framework materials and outlines the process for public review and comment on this undertaking.     

机遇与挑战——写在统计学成为一级学科之际

 简要回顾了中国统计学科发展的百年历史,重点讨论了统计学科与统计教育的现状,特别是统计学完全成为一级学科后面对的大好发展机遇,以及在做大的基础上如何做强统计学科需要我们重视、研究和解决的主要问题。     

Confidence intervals for n in the exponential order statistics problem

A simplified proof of the basic properties of the estimators in the Exponential Order Statistics (Jelinski-Moranda) Model is given. The method of constructing confidence intervals from hypothesis tests is applied to find conservative confidence intervals for the unknown parameters in the model.     

In Memoriam: Emeritus Professor Henry Oliver Lancaster, AO FAA 1 February 1913 – 2 December 2001

The death, in Sydney, of Oliver Lancaster marks the end of an era in the histories of the Statistical Society of Australia, which (in its previous existence as the Statistical Society of New South Wales) he helped found in 1947, and of the Australian Journal of Statistics of which he was founding editor (1959–1971). Oliver Lancaster was Foundation Professor of Mathematical Statistics at the University of Sydney (1959–1978), where he spent his life as student and academic. During his academic career, he achieved scholarly distinction in at least four fields: mathematical statistics, medical and public health statistics, the history of medicine and of statistics, and statistical bibliography. With E.J.G. Pitman (1897–1993), M.H. Belz (1897–1975), E.A. Cornish (1909–1973) and P.A.P. Moran (1917–1988) he was part of a cohort of renowned Australian mathematical statisticians who laid the foundation of the glory days of Australian mathematical statistics. This obituary and tribute focuses on some of these aspects, within a broader historical picture.     

A Survey of the Teaching of Statistics in M.B.A. Programs

A survey of business schools was conducted to obtain information about the current state of the teaching of business statistics to students enrolled in M.B.A. degree programs. The survey was undertaken for and presented at a June 1986 conference on “Making Statistics More Effective in Schools of Business,” held at the University of Chicago's Graduate School of Business. Information was elicited concerning both the required statistics sequence and elective statistics courses for M.B.A. students, as well as computer usage in these courses. This article summarizes the information obtained from the survey.     

MAOSA: A new procedure for detection of differential gene expression

Gene expression data analysis provides scientists with a wealth of information about gene relationships, particularly the identification of significantly differentially expressed genes. However, there is no consensus on the analysis technique that will solve the inherent multiplicity problem (thousands of genes to be tested) and yield a reasonable and statistically justifiable number of differentially expressed genes. We propose the Multiplicity-Adjusted Order Statistics Analysis (MAOSA) to identify differentially expressed genes while adjusting for the multiple testing. The multiplicity problem will be eased by performing a Bonferroni correction on a small number of effects, since the majority of genes are not differentially expressed.