Teaching

  • When instruction in statistical concepts can be tied to practical sports issues, students are motivated to understand the statistical concepts. In this paper we describe an issue that would be relevant to discussions of many different sports leagues, and would also be a vehicle for teaching statistical concepts such as simulation, graphical displays, illusions of randomness, measurement of variability, and the logic of hypothesis testing. In addition to motivating a keen interest in the effects of random variation, these examples provide students with a way to verbalize what they learn in statistics classes to their lay acquaintances. Moreover, examples like these have the potential for engaging instructors who have been focused on more traditional approaches. Programs in the software language R are provided and their use with introductory classes is discussed.

  • We discuss our initial experience with offering a version of our standard introductory statistics course that focused primarily on sports related examples, rather than a more traditional selection of applications. This special sports section was offered in parallel with a regular section, covering the same statistical topics, with the same instructor, at the same pace. We examine how the students enrolling in the sports section might differ from the regular, illustrate how we converted material from the regular section to the sports equivalent, compare the performance of students between the two sections and reflect on the effectiveness of the sports-based approach.

  • Sports data are commonly used to present topics from introductory statistics, such as exploratory data analysis and probability. They also can illustrate more subtle and complex statistical issues, such as selecting appropriate variables, making casual inferences from observational data, and specifying appropriate inferential populations. In this paper, I discuss how sports data can be used to engage students on such fundamental aspects of data analysis. I frame the discussion around the question posed in the title, a question which has generated much debate among baseball enthusiasts.

  • Statistics plays a leading role in finance. The explosive development of increasingly complex markets makes it more and more difficult for practitioners to correctly value financial asset. Statistical analysis has become a powerful tool for a better market valuation, taking a leading role in the development of new financial products that try to hedge the increasing amount of risks that an investor has to take. Statistics knowledge demand is steadily increasing in Hedge Funds, Investment Banking and Financial Institutions in general, where statistics students could developed a professional career. Finance can be seen as a way to motivate students on the applications of almost any statistical tool we would like to teach them, since we could always find an example where these techniques are put into practice.

  • The Actuarial profession appeals to many with excellent quantitative skills who aspire to "make financial sense of the future." However the road to qualification as an Actuary through the Institute or Faculty of Actuaries in the UK (or the Society of Actuaries or Casualty Actuary Society in the United States) is not an easy one, and a series of very challenging exams must be passed to qualify as a Fellow Actuary. These exams test many skills, and in particular demand a good knowledge of probability and statistics. The main areas of work for actuaries are traditionally life assurance, actuarial consultancy, general insurance, and investment. Although statistical skills are required in all of these areas, they are particularly important in general insurance. In this paper we discuss the basic tools and techniques in probability and statistics that are essential for an actuary who intends to work in general insurance.

  • In this paper, we present a brief history of our efforts to incorporate civic learning into our statistics curriculum, highlighting our most recent approach, media reports. We discuss implementation issues, educational objectives, and give examples of student projects. Learning objectives, expected outcomes, and our assessment process are also given. An important aspect of this effort is the use of technology in report generation and dissemination. We discuss the development of these tools and how they have been used. We conclude with remarks on sustainability and possible future directions.

  • Carnegie Mellon University was funded to develop a "stand-alone" web-based introductory statistics course, openly and freely available to individual learners online. The goal of this project is to develop statistical literacy among people who do not have access to academic institutions because of remote locations, financial difficulties or social barriers. In order to achieve this goal, the design of the course has been a collaboration among statistics faculty, cognitive scientists and experts in human computer interaction. This paper discusses the challenges in developing such a learning environment and ways in which the course tries to address them. We also describe the design and results of a pilot study where the degree to which the course is successful in developing statistical literacy has been examined.

  • Although the Statistics Education community has advocated using real data to teach introductory statistics for quite some time, often these data sets are not recognizably real to statisticians since the students' limited experience with "real" statistical software and data management techniques precludes the use of truly messy data. But grappling with messy and complex data sets is important for teaching Statistical Thinking (broadly defined as "thinking like a statistician") and is appropriate for an introductory statistics course. We describe our experience collecting rich data sets and developing computer lab assignments using STATA to teach statistical thinking to first-year university students using these data sets. Collecting useable, real, data sets turns out to be fairly difficult for several reasons, and teaching data management and analysis without resorting to rote-based rules is quite challenging.

  • The advancement of computer technology creates unlimited opportunities for teaching and learning statistical concepts. A significant impact is the paradigm shift from a passive teaching-centered to an active learning-centered environment. Although one should not make a paradigm shift solely for the sake of technology, there is no doubt that technology will play a crucial role in this transformation. Research has suggested that meaningful learning takes place when students are actively involved in constructing knowledge themselves through their own experiences and active participation. This article proposes an active learning environment for introductory statistics courses using an online real-time database created by students. The experience of implementing the active learning activities using the real-time online database will be shared. Some strengths and weaknesses will be discussed.

  • During the past decade, national and international organisations have been steadily increasing the number of web-based statistical databases available to general public. While often user-unfriendly (mostly due to poor design and organisation as well as lack of navigation options) in their pioneer years, many of these databases have been gradually transformed into well-managed expansive resources that can greatly enhance the teaching and learning of statistics. A number of illustrative examples are presented in this paper along with discussion of our experiences and identification of future challenges pertaining to their use in statistics courses.

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