Literature Index

Displaying 2521 - 2530 of 3326

  • Teaching Statistical Concepts With Simulated Data

    Author(s):
    Blejec, A.
    Editors:
    Phillips, B.
    Year:
    2002
    Abstract:
    Different kinds of data are used in teaching statistics. In applied statistics courses we usually use real life data related to the main subject matter of our students. Such data are interesting for students and motivate final interpretation of statistical results. For demonstration of statistical concepts, computer simulated data with known statistical properties can be used. The advantage of such data is that results of analysis can be compared with known and pre-defined properties of data. Many important statistical concepts and procedures can be obviously shown with computer simulations and dynamic graphics. Such simulations can sometimes be more convincing than proofs and are appreciated by students.
    Location:
    http://www.stat.auckland.ac.nz/~iase/publications/1/7b3_blej.pdf

  • Teaching Statistical Concepts With Student-Specific Datasets

    Author(s):
    Vaughan, T. S.
    Year:
    2003
    Abstract:
    The advent of electronic communication between students and teachers facilitates a number of new techniques in the teaching of statistics. This article presents the author's experiences with providing each student in a large, multi-section class with a unique dataset for homework and in-class exercises throughout the semester. Each student's sample is pseudo-randomly generated from the same underlying distribution (in the case of hypothesis tests and confidence intervals involving ), or the same underlying linear relationship (in the case of simple linear regression). This approach initially leads students to identify with their individual summary statistics, test results, and fitted models, as "the answer" they would have come up with in an applied setting, while subsequently forcing them to recognize their answers as representing a single observation from some larger sampling distribution.
    Location:
    http://www.amstat.org/publications/jse/v11n1/vaughan.html

  • Teaching Statistical Concepts, Fundamentals and Modelling

    Author(s):
    Timothy E. O'Brien
    Year:
    2008
    Abstract:
    This article considers some issues in designing a course focusing on statistical concepts rather than memorizing formulae.
    Location:
    http://www3.interscience.wiley.com/journal/121376342/abstract

  • Teaching Statistical Inference via Simulation using R

    Author(s):
    Daniel Kaplan
    Year:
    2008
    Abstract:
    George Cobb describes the core logic of statistical inference in terms of the three Rs: Randomize, Repeat, Reject. (See repositories.cdlib.org/uclastat/cts/tise/vol1/iss1/art1) Note that all three Rs involve process or action. Teaching this core logic is more effective when students are able to carry out these actions on real data.<br><br>In this webinar, I'll show how to use computers effectively with introductory-level students to teach them the three Rs of inference. To do this, I will use a another R: the statistical software package.<br><br>The simulations that will be carried out involve constructing confidence intervals, demonstrating the idea of "coverage," hypothesis testing, and confounding and covariation.
    Location:
    http://www.causeweb.org/webinar/2008-10/

  • Teaching statistical inference within applied statistics

    Author(s):
    Mead, R., &amp; Whitehead, J.
    Editors:
    Vere-Jones, D., Carlyle, S., &amp; Dawkins, B. P.
    Year:
    1991
    Abstract:
    This paper is based on our attempts to develop an inference course at Reading in which students learn about concepts primarily through project work.

  • Teaching statistical literacy.

    Author(s):
    Cerrito, P. B.
    Year:
    1999
    Abstract:
    Teachers can use variety of strategies to instruct students in stat literacy. One such stratedy is using articles that contatin statistics which display commonly held beliefs. These encourage students to seek weaknesses and di ** strengths of factual information. Using essay questions that appear to have a answer are another means to engage students in statistical learning.<br>University students are best taught statistical literacy through a general education course. The first step is to explore an issue that has been taken for granted and is incontrovertible in the students' minds, such as the safety and effectiveness of immunizations. Students begin by evaluating an article with a negative slant on immunizations and by examining their own preconceived ideas. Students then receive a comment sheet in which the teacher responds generally to their arguments, provides factual support for their beliefs, and mentions some of the strengths of the article. Students are then asked to consider how a study can be designed to examine a particular question and to discuss why such a definitive study cannot actually be performed. An example of how the student discussion can be translated to a consideration of immunizations for Hepatitis B is presented.

  • Teaching Statistical Methods For The Health Sciences In The Post-Genomic Era

    Author(s):
    Aires, N. &amp; Thelle, D.
    Editors:
    Phillips, B.
    Year:
    2002
    Abstract:
    In many complex diseases researchers have observed that neither genetic factors nor environmental factors alone determine the disease. This observation generates the hypothesis that human disease is caused by both genetic and environmental factors that act together. This leads to the concept multifactorial causes of disease. On the other hand, the recent compilation of the draft human genome sequence opened the possibility to detect candidate genes for complex diseases and even to study these in relation with environmental factors. The gene-environmental interaction may not be easy to analyze due to the complex structure that the involved factors may have. These factors have different nature that should be treated at different stages of the study. Particular attention should be paid to the study size and design. Epidemiological studies with particular interest in identifying candidate genes that contribute to complex diseases as well as detection of intergenic or gene-environment interactions require large sample sizes because many variables are studied simultaneously. The larger patient populations ensure that individual subgroups retain adequate power to detect significant results with narrow confidence intervals. In the paper we focus on the advantages/disadvantages of classic multifactorial statistical methods applied to the health sciences and the genome scan.
    Location:
    http://www.stat.auckland.ac.nz/~iase/publications/1/5g2_nair.pdf

  • Teaching statistical regression: Simplifying a principle that is hard to understand

    Author(s):
    McMillan, J. H.
    Year:
    1989
    Abstract:
    Statistical regression to the mean is not an easy concept to grasp, especially by students whose background in statistics and probability is limited. As a source of internal invalidity, however, it is an important concept to understand. It is perplexing to students to simply indicate that extreme scores regress to the mean; it is beyond most students to use a more complex mathematical approach. The procedure described in this paper is sufficiently detailed to show students how regression occurs without presenting complicated math. The procedure is based on earlier suggestions by Cutter and Levin.

  • Teaching Statistical Thinking

    Author(s):
    Kugler, C., Hagen, J., Singer, F.
    Year:
    2003
    Abstract:
    Describes the importance of statistical reasoning in understanding modern science and critically evaluating information. Uses spreadsheet programs and the black box approach to teach basic concepts of statistical reasoning. Presents examples of the role of statistical thinking in the disciplines of biology, chemistry, physics, and geology.

  • Teaching Statisticians And Applied Researchers Statistical Methods For Analysis Of Data From Rating Scales. Experiences From Joint Research Courses In Rating Scale Data Analysis

    Author(s):
    Svensson, E.
    Editors:
    Phillips, B.
    Year:
    2002
    Abstract:
    The widespread use of rating scales in applied research fields implies need of statistical methods applicable to various types of studies involving ordinal response data. The aim of this paper is to present a teaching model of joining research courses in rating scale data analysis for statisticians and applied scientists together in order to stimulate inter-disciplinary communication. The participants experienced the complexity of applied research problems that involve subjective assessments on scales and also some of the possibilities and limitations of novel and classical statistical methods of analysis.
    Location:
    http://www.stat.auckland.ac.nz/~iase/publications/1/3n2_sven.pdf

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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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