Literature Index

Displaying 2481 - 2490 of 3326
  • Author(s):
    Howley, P. P.
    Year:
    2003
    Abstract:
    As part of the University of Newcastle's Total Quality Management (TQM) course, students study Experimental Design (ED) and Statistical Process Control (SPC) within the framework of the scientific approach to process improvement. A sufficient balance of theory and application is required to keep Business and Management students, most with a largely non-quantitative background, interested and aware of the need and method to correctly implement ED and SPC in industry. Tools to facilitate a basic understanding of the importance of the 3Rs, namely, Randomization, Replication, and Blocking, as well as highlighting the potential for mistakes or inefficient calibration techniques are essential in the learning process. This paper describes the use of a particular tool, called the "Ballistat," to illustrate TQM concepts, which enables students to obtain the hands-on experience needed to control processes in industry.
  • Author(s):
    Eckert, S.
    Year:
    1994
    Abstract:
    In elementary statistics courses, students often have difficulty understanding the principles of hypothesis testing. This paper discusses a simple yet effective demonstration using playing cards. The demonstration has been very useful in teaching basic concepts of hypothesis testing, including formulation of a null hypothesis, using data as evidence against the null hypothesis, and determining the strength of the evidence against the null hypothesis, i.e., the p-value.
  • Author(s):
    Cordani, L. K., & Wechsler, S.
    Editors:
    Rossman, A., & Chance, B.
    Year:
    2006
    Abstract:
    Most of the statistical curricula, mainly that written at the elementary level, is based on the classical (frequentist) approach. The Bayesian school, even if originated in the 18th century, has only recently seen a strong development of its tools. This development, however, has not been seen in a basic level. The discipline, as well as the teachers, reflect the classical dominance, which reinforces the current paradigm. Although they have different starting points, both approaches, classical and Bayesian, have tools to analyze data, and we should offer the choice to the student. This article deals with two important concepts, one very useful from the classical point of view, which is the concept of independence, and the other related to the Bayesian thought, the concept of exchangeability. Definitions and simple examples are presented to relate both approaches, from an elementary point of view.
  • Author(s):
    Albert, J.
    Year:
    1995
    Abstract:
    Teaching elementary statistical inference from a traditional viewpoint can be hard, due to the difficulty in teaching sampling distributions and the correct interpretation of statistical confidence. Bayesian methods have the attractive feature that statistical conclusions can be stated using the language of subjective probability. Simple methods of teaching Bayes' rule are described, and these methods are illustrated for inference and prediction problems for one and two proportions. We discuss the advantages and disadvantages of traditional and Bayesian approaches in teaching inference and give texts that provide examples and software for implementing Bayesian methods in an elementary class.
  • Author(s):
    Michael D. Ernst
    Year:
    2009
    Abstract:
    Nearly all introductory statistics textbooks include a chapter on data collection methods that includes a detailed discussion of both random sampling methods and randomized experiments. But when statistical inference is introduced in subsequent chapters, its justification is nearly always based on principles of random sampling methods. From the language and notation that is used to the conditions that students are told to check, there is usually no mention of randomized experiments until an example that is a randomized experiment is encountered, at which point the author(s) may offer a statement to the effect of "the randomization allows us to view the groups as independent random samples." But a good student (or even an average one) should ask, "Why?"<br><br>This paper shows, in a way easily accessible to students, why the usual inference procedures that are taught in an introductory course are often an appropriate approximation for randomized experiments even though the justification (the Central Limit Theorem) is based entirely on a random sampling model.
  • Author(s):
    Kenney, P. A.
    Abstract:
    This paper presentation contains a variety of useful materials for introductory statistics instructors. Kenney discusses the tool box metaphor that she often uses in her class, and she also shares activites and thoughts about issues such as metacognition and portfolio assessment. She includes a survey that she gives to her students in order to gather feedback about study strategies and teaching techniques that seemed to be most helpful to the students. Finally, she includes an anecdotal report from one of the students in her class.
  • Author(s):
    Martin, P.
    Editors:
    Vere-Jones, D., Carlyle, S., &amp; Dawkins, B. P.
    Year:
    1991
    Abstract:
    This paper describes the need for a shift in emphasis away from the development of operations as described by Leontiev, towards the provision of increased experience of statistical activity.
  • Author(s):
    Albert, J.
    Editors:
    Phillips, B.
    Year:
    2002
    Abstract:
    This paper reviews the past and current interest in using Bayesian thinking to introduce statistical inference. Rationale for using a Bayesian approach is described and particular methods are described that make it easier to understand Bayes' rule. Several older and modern introductory statistics books are reviewed that use a Bayesian perspective. It is argued that a Bayesian perspective is very helpful in teaching a course in statistical literacy.
  • Author(s):
    Tudor, G. E.
    Editors:
    Stephenson, W. R.
    Year:
    2006
    Abstract:
    This paper describes the components of a successful, online, introductory statistics course and shares students' comments and evaluations of each component. Past studies have shown that quality interaction with the professor is lacking in many online courses. While students want a course that is well organized and easy to follow, they also want to interact with the professor and other students. Interactions in this course took place through small group discussions, emails, weekly announcements and graded exams. The course also contained lecture slides with audio prepared by the professor. As the variety and quantity of interaction increased, student satisfaction with the amount of interaction with the professor increased from 75% the first year of the course to 99% the fifth year. Overall satisfaction with the online course increased from 93% the first year to 100% the fifth year.
  • Author(s):
    Brewer, J. K.
    Editors:
    Vere-Jones, D., Carlyle, S., &amp; Dawkins, B. P.
    Year:
    1991
    Abstract:
    These instructional methods are not to be construed as a guide to how introductory graduate statistics should be taught, but rather as an example of how one instructor tries to teach adult learners in an elementary statistics environment.

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The CAUSE Research Group is supported in part by a member initiative grant from the American Statistical Association’s Section on Statistics and Data Science Education

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