Elementary statistics laboratory

Students in elementary statistics traditionally see experiments and data as words and numbers in a text. They receive little exposure to the important statistical activities of sample selection, data collection, experimental design, development of statistical models, the need for randomization, selection of factors, etc. They often leave the first course without a firm understanding of the role of applied statistics or of the statistician in scientific investigations. In an attempt to improve elementary statistics education, we have developed a statistics laboratory similar to those of other elementary science courses. We will discuss our experiences in teaching a laboratory component with the traditional elementary statistics course. In each lab session, students, working in teams, discuss the design of an experiment, carry out the experiment, and analyze their data using Minitab on a Macintosh or MS-DOS based computer. The students then individually either answer a series of short answer questions or write a formal scientific report. The labs are designed to be relatively inexpensive and portable. They do not require a prior background in science, statistics or computing.     

Some Remarks on Statistical Education

A definition of the subject of statistics is given, and the difference between the chalkboard world of the teacher of statistics and the real world of the experimenter is stressed. An overemphasis on significance testing, hypothesis testing, and decision procedures has led to a de-emphasis of statistical design. The teaching of statistical design theory, statistics teaching in a changing world, the importance of model building, and different approaches to teaching statistics are discussed. Some published materials developed to meet teaching needs and a new type of statistics course are described. Information about special issues in statistical education (teaching and consulting) is presented.     

Teaching Statistics Theory through Applications

This article presents a model for developing case studies, or labs, for use in undergraduate mathematical statistics courses. The model proposed here is to design labs that are more in-depth than most examples in statistical texts by providing rich background material, investigations and analyses in the context of a scientific problem, and detailed theoretical development within the lab. An important goal of this approach is to encourage and develop statistical thinking. It is also advocated that the labs be made the centerpiece of the theoretical course. As a result, the curriculum, lectures, and assignments are significantly restructured. For example, the course work includes written assignments based on open-ended data analyses, and the lectures include group work and discussions of the case-studies.     

Correlation,Regression Lines,and Moments of Inertia

The R language, a freely available environment for statistical computing and graphics is widely used in many fields. This “expert-friendly” system has a powerful command language and programming environment, combined with an active user community. We discuss how R is ideal as a platform to support experimentation in mathematical statistics, both at the undergraduate and graduate levels. Using a series of case studies and activities, we describe how R can be used in a mathematical statistics course as a toolbox for experimentation. Examples include the calculation of a running average, maximization of a nonlinear function, resampling of a statistic, simple Bayesian modeling, sampling from multivariate normal, and estimation of power. These activities, often requiring only a few dozen lines of code, offer students the opportunity to explore statistical concepts and experiment. In addition, they provide an introduction to the framework and idioms available in this rich environment.     

New Developments in Statistical Computing: Short Notices and Reviews of New Program Packages,other Sources of Information,and New Products

Graphs are presented on which the empirical distribution function can be plotted to test the assumption of normality by the Lilliefors test. A second set of graphs is presented for using the Lilliefors test on exponential distributions. The graphs allow for tests at the 10 percent, 5 percent, and 1 percent levels of significance. Use of these graphs makes it easy for students in a first course in statistics to test normal and exponential distributions without having to unravel the mystery associated with putting together a chi-squared goodness-of-fit test.     

Bayes and MCMC for Undergraduates

Students of statistics should be taught the ideas and methods that are widely used in practice and that will help them understand the world of statistics. Today, this means teaching them about Bayesian methods. In this article, I present ideas on teaching an undergraduate Bayesian course that uses Markov chain Monte Carlo and that can be a second course or, for strong students, a first course in statistics.     

What Your Future Doctor Should Know About Statistics: Must-Include Topics for Introductory Undergraduate Biostatistics

The increased emphasis on evidence-based medicine creates a greater need for educating future physicians in the general domain of quantitative reasoning, probability, and statistics. Reflecting this trend, more medical schools now require applicants to have taken an undergraduate course in introductory statistics. Given the breadth of statistical applications, we should cover in that course certain essential topics that may not be covered in the more general introductory statistics course. In selecting and presenting such topics, we should bear in mind that doctors also need to communicate probabilistic concepts of risks and benefits to patients who are increasingly expected to be active participants in their own health care choices despite having no training in medicine or statistics. It is also important that interesting and relevant examples accompany the presentation, because the examples (rather than the details) are what students tend to retain years later. Here, we present a list of topics we cover in the introductory biostatistics course that may not be covered in the general introductory course. We also provide some of our favorite examples for discussing these topics.     

Current challenges in statistics: large-lecture courses

Each semester approximately 80% of the students taught by the Florida State University Department of Statistics are enrolled in STA 3014: Fundamental Business Statistics. During the academic year this course is taught in large lecture sections of 250 students each. It is either the only statistics course or one of two statistics courses taken in their undergraduate career for probably 90% of these students. A similar situation exists in many statistics departments around the nation.

These large introductory courses offer us the opportunity to introduce the power of statistics to a large fraction of our future business leaders. In the past it appears that this opportunity has often been missed. In fact, some suggest that these courses help contribute to the general public's negative attitude toward statistics courses, the discipline of statistics, and statisticians.

Hence, I propose that one of the current challenges in statistics is the challenge of improving the quality of these courses so that statistics may contribute to the improvement of quality and productivity in the United States, a vital national issue. In this paper I report on my experiences in grappling with this challenge in STA 3014.     

Teaching Regressions an Exploratory Approach

An applied statistics and data-analysis course designed for students of public management and policy analysis, but suitable as an introductory graduate-level applied course in other contexts, is discussed. The course, Quantitative Methods for Public Management (QMPM), is a departure from traditional instruction in statistics. It uses subject-matter hierarchies to schedule the presentation of substantive material, and it integrates exploratory data analysis (EDA) and standard classical techniques. This integration is accomplished by using exploratory methods to clarify and evaluate analyses performed with classical procedures. The course, taught since 1975 at Carnegie-Mellon University's School of Urban and Public Affairs, has been evaluated experimentally through a randomized assignment of students to either a traditional introductory statistics course or QMPM. We concentrate here on the QMPM approach to teaching regression.     

Teaching Statistical Consulting Before Statistical Methodology

This paper outlines and discusses the advantages of an ‘Introduction to Statistical Consulting’ course (ISC) that exposes students to statistical consulting early in their studies. The course is intended for students before, or while, they study their units in statistical techniques, and assumes only a first‐year introductory statistics unit. The course exposes undergraduate students to the application of statistics and helps develop statistical thinking. An important goal is to introduce students to work as a statistician early in their studies because this motivates some students to study statistics further and provides a framework to motivate the learning of further statistical techniques. The ISC has proved popular with students, and this paper discusses the reasons for this popularity and the benefits of an ISC to statistical education and the statistics profession.     

Teaching Introductory Statistics Courses So That Nonstatisticians Experience Statistical Reasoning

The most critical point in developing dynamic introductory statistics courses for nonstatisticians is deciding whether to teach statistical reasoning (concepts and thinking), statistical methods (computations), or both. Statistical reasoning should precede statistical methods. Workshop-based courses effectively provide situated learning and an intimate teaching environment. Use real (or realistic) data, graphics, and teach exploratory data analysis before classical methods. Evaluate the students' levels of statistical anxiety prior to and during the course.     

Teaching Elementary Bayesian Statistics with Real Applications in Science

University courses in elementary statistics are usually taught from a frequentist perspective. In this paper I suggest how such courses can be taught using a Bayesian approach, and I indicate why beginning students are well served by a Bayesian course. A principal focus of any good elementary course is the application of statistics to real and important scientific problems. The Bayesian approach fits neatly with a scientific focus. Bayesians take a larger view, and one not limited to data analysis. In particular, the Bayesian approach is subjective, and requires assessing prior probabilities. This requirement forces users to relate current experimental evidence to other available information–-including previous experiments of a related nature, where “related” is judged subjectively. I discuss difficulties faced by instructors and students in elementary Bayesian courses, and provide a sample syllabus for an elementary Bayesian course.     

财经院校统计学双语教学实践研究

本文通过对统计学双语教学实践的研究和探讨,指出在高等财经院校开展统计学双语,有利于培养符合经济发展的高素质人才,并有助于统计学本学科自身的发展。结合教学的实践,提出了统计学双语教学的实践方法。     

On the Non-attainability of Chebyshev Bounds

In this department The American Statistician publishes articles, reviews, and notes of interest to teachers of the first mathematical statistics course and of applied statistics courses. The department includes the Accent on Teaching Materials section; suitable contents for the section are described under the section heading. Articles and notes for the department, but not intended specifically for the section, should be useful to a substantial number of teachers of the indicated types of courses or should have the potential for fundamentally affecting the way in which a course is taught.     

Rényi Statistics in Directed Families of Exponential Experiments*

Rényi statistics are considered in a directed family of general exponential models. These statistics are defined as Rényi distances between estimated and hypothetical model. An asymptotically quadratic approximation to the Rényi statistics is established, leading to similar asymptotic distribution results as established in the literature for the likelihood ratio statistics. Some arguments in favour of the Rényi statistics are discussed, and a numerical comparison of the Rényi goodness-of-fit tests with the likelihood ratio test is presented.     

数据大集中环境下的统计生产流程再造

随着中国经济结构的不断调整和经济现象的日益复杂化,现有政府统计面临极大的挑战。基于此,以制度领域、技术领域和管理领域的数据大集中为核心架构,构建一种新型的政府统计数据大集中环境,并依托该环境对中国政府统计生产流程进行再造。     

组织支持感对合作社成员满意度的影响研究——以成员参与为调节变量

以农民专业合作社的成员为研究对象,以成员参与为调节变量,情感支持和生产支持为自变量构建理论模型,检验这些变量对合作社成员满意度的影响。以SPSS18.0软件对收集到的387份有效问卷进行了描述性统计、相关性分析、信度和效度检验,应用阶层回归分析方法对各变量之间的关系进行了实证分析。研究表明:情感支持和生产支持对成员满意度均具有显著正向影响;成员参与对组织支持感与成员满意度之间关系的调节效应存在一定的差异。业务参与对生产支持与成员满意度之间关系起到显著调节作用,对情感支持与成员满意度之间关系没有起到显著调节作用;经济参与对组织支持感(情感支持和生产支持)与成员满意度之间关系没有起到显著调节作用;管理参与对组织支持感(情感支持和生产支持)与成员满意度之间关系均起到显著调节作用。     

Comparison among non parametric prediction intervals of order statistics

Abstract

In this article, we are interested in conducting a comparison study between different non parametric prediction intervals of order statistics from a future sample based on an observed order statistics. Typically, coverage probabilities of well-known non parametric prediction intervals may not reach the preassigned probability levels. Moreover, prediction intervals for predicting future order statistics are no longer available in some cases. For this, we propose different methods involving random indices and fractional order statistics. In each case, we find the optimal prediction intervals. Numerical computations are presented to assess the performances of the so-obtained intervals. Finally, a real-life data set is presented and analyzed for illustrative purposes.     

Statistical Computing Packages: Some Words of Caution

Three situations are cited when caution is needed in using statistical computing packages: (a) when analyzing data and having insufficient statistical knowledge to completely understand the output; (b) when teaching the use of packages in a statistics course, to the exclusion of teaching statistics; and (c) when using packages in subject-matter teaching, without teaching the statistical methods underlying the packages.