Literature Index

Displaying 3161 - 3170 of 3326

  • Using Simulation in Statistics Courses

    Author(s):
    Dambolena, I. G.
    Year:
    1986
    Abstract:
    Provides examples of the use of computer simulation to help beginners develop a grasp for difficult statistical concepts. The implementation of this teaching methodology is discussed and analysis of simulation output through use of standard statistical software packages is illustrated. (Author/MBR)

  • Using simulation to learn about inference.

    Author(s):
    Erickson, T.
    Editors:
    Rossman, A., & Chance, B.
    Year:
    2006
    Abstract:
    Many statistics educators use simulation to help students better understand inference. Simulations make the link between statistics and probability explicit through simulating the conditions of the null hypothesis, and then looking at sampling distributions of an appropriate measure. In this paper we review how we use simulation to help understand hypothesis testing, and lay out the relevant steps. We illustrate how using simulation and technology can make these difficult ideas more visible and understandable, through making processes more concrete, through unifying apparently disparate tests, and through letting the learners construct their own measures to study phenomena.
    Location:
    http://www.stat.auckland.ac.nz/~iase/publications/17/7G2_ERIC.pdf

  • Using simulation to model real world problems

    Author(s):
    Bryan, B. H.
    Editors:
    Davidson, R., & Swift, J.
    Year:
    1986
    Abstract:
    The primary source of the material used in this presentation is The Art and Techniques of Simulation, a book from the Quantitative Literacy Series. These techniques are designed for use in middle school through senior high school. They feature statistical topics that are important to students, a wealth of hands-on activities, real data sets and active experiments which motivate student participation, and graphical methods instead of complicated formulas or abstract mathematical concepts. In particular, simulation is introduced as a technique for solving probability and statistics problems.

  • Using Simulation to Study Estimation

    Author(s):
    Perry, M., & Kader, G.
    Year:
    1995
    Abstract:
    Illustrates how to use computer simulation models in statistics to study the quality of an estimation procedure and concurrently the subtle concepts of randomness and convergence. Special emphasis is given to the use of graphical representations. (MKR)

  • Using simulation to teach and learn statistics.

    Author(s):
    Chance, B., & Rossman, A.
    Editors:
    Rossman, A., & Chance, B.
    Year:
    2006
    Abstract:
    Technology, and simulation in particular, can be a very powerful tool in helping students learn statistics, particularly the ideas of long-run patterns and randomness, in a concrete, interactive environment. This talk will provide examples of the integration of simulation to enhance topics throughout an introductory statistics course through a combination of Minitab macros and specifically designed applets. Topics will include randomization tests for comparing groups, and sampling distributions of proportions, odds ratios, and regression coefficients. We will also highlight how simulation can motivate students to learn the more mathematical derivations. Feedback and sample work from students will be presented, as well as issues in designing effective simulation investigations.
    Location:
    http://www.stat.auckland.ac.nz/~iase/publications/17/7E1_CHAN.pdf

  • Using Simulation to Teach Distributions

    Author(s):
    Doane, D. P.
    Year:
    2004
    Abstract:
    Many students doubt that statistical distributions are of practical value. Simulation makes it possible for students to tackle challenging, understandable projects that illustrate how distributions can be used to answer "what-if" questions of the type often posed by analysts. Course materials that have been developed over two years of classroom trials will be shared, including (1) overviews of distributions and simulation; (2) basic capabilities of @RISK software; (3) simulation spreadsheets suitable for analysis by teams; and (4) exercises to guide the teams. These revisable materials could also be used as in-class demonstrations. Concepts illustrated include expected value, k-tiles (e.g., quartiles), empirical distributions, distribution parameters, and the law of large numbers. For those who don't have @Risk, spreadsheets are provided which demonstrate elementary risk-modeling concepts using only Excel. All materials can be downloaded from www.sba.oakland.edu/Faculty/DOANE/downloads.htm.
    Location:
    http://www.amstat.org/publications/jse/v12n1/doane.html

  • Using small groups to promote active learning in the introductory statistics course: A report from the field

    Author(s):
    Keeler, C. M., & Steinhorst, R. K.
    Year:
    1995
    Abstract:
    Over several semesters, we changed form the traditional lecture approach to cooperative learning. After some initial difficulty, we found procedures that work in classes of 40 to 100 students. Data consist of final grade distributions, the number of students retained in the class, and responses on a questionnaire that asked students' attitudes towards the group activities. Working in cooperative groups resulted in higher final scores in two experimental sections than in a comparison course section. A higher percentage of students successfully completed the course in the experimental sections, and student attitudes toward the cooperative group experience were positive.
    Location:
    http://www.amstat.org/publications/jse/v3n2/keeler.html

  • Using StatCrunch as a computational tool in introductory statistics

    Author(s):
    Webster West
    Year:
    2006
    Abstract:
    StatCrunch (www.statcrunch.com) is a freely available Web-based data analysis package. StatCrunch has all of the routines required for introductory statistics and many more. The software also offers pedagogical features such as interactive graphics. Many of these capabilities will be discussed and demonstrated.
    Location:
    http://www.causeweb.org/webinar/2006-02/

  • Using statistical packages and calculators in the classroom

    Author(s):
    McKenzie, J. D. Jr., & Furman, W. D.
    Editors:
    Vere-Jones, D., Carlyle, S., & Dawkins, B. P.
    Year:
    1991
    Abstract:
    For over twenty years statistical software has been employed in introductory applied statistics courses. It has been used to calculate descriptive statistics, probabilities, confidence intervals, hypothesis test statistics, and linear regressions. Recently, statistical calculators with many of the capabilities of existing software have appeared. In this paper the authors explained the pedagogical advantages and disadvantages of each tool. They compared the capabilities of MINITAB, a widely-used general-purpose data analysis system, with the HP-21S, a relatively inexpensive Hewlett-Packard stat/math calculator. They also presented the preliminary results of an experiment comparing the use of these two computing tools in the classroom.

  • Using Statistics As A Tool In Political Research. The Case Of Electoral Behavior

    Author(s):
    Chadjipadelis, T.
    Editors:
    Phillips, B.
    Year:
    2002
    Abstract:
    In this paper an extensive reference to the problems of using statistical techniques in the political sciences is taken place. Statistics today, in the era of information outbreak can be generally defined as the "Science that is concerned with the gathering, evaluation and processing of information". Society's demand for qualitatively controlled information absolved from "noises" which intentionally or not are included, is especially obvious in a returning, from time to time discussion, about the control of the public measurements related to the Mass Media audience, political parties and persons, educational parameters, economical and social indexes and factors, etc. By following and commenting the phases of observation of electoral behavior, we will refer to some problems.
    Location:
    http://www.stat.auckland.ac.nz/~iase/publications/1/4h1_chad.pdf

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