Literature Index

Displaying 3061 - 3070 of 3326
  • Author(s):
    Konold, C., Well, A., Lohmeier, J., & Pollatsek, A.
    Editors:
    Becker, J. R., & Pence, B. J.
    Year:
    1993
    Abstract:
    Twelve students answered questions involving the distribution of sample means both before and after an instructional intervention. Correct performance improved on these problems but dropped on problems having to do with the distribution of samples.
  • Author(s):
    O'Connell, A. A.
    Year:
    1999
    Abstract:
    This study provides an investigation of relationships among different types of errors occurring during probability problem-solving. Fifty non-mathematically sophisticated graduate student subjects enrolled in an introductory probability and statistics course were asked to solve a set of probability problems, and their attempts at solution were analyzed for presence and type of errors. The errors contained within these solutions were categorized according to a coding scheme which identifies 110 specific kinds of errors in four categories: text comprehension errors, conceptual errors, procedural errors, and arithmetic/computation errors. Relationships among types of errors included in each category were investigated using hierarchical clustering via additive trees. Implications of these relationships for the teaching and learning of probability problem-solving are discussed.
  • Author(s):
    Lipson, K.
    Editors:
    The National Organizing Committee of the ICOTS 4
    Year:
    1994
    Abstract:
    Traditional courses in statistics generally approach inference from a theoretical probability based perspective. Since the mathematical backgrounds of students is often not strong, many courses use computer based simulations to empirically justify ideas which are too complex or too abstract for most students. However, eventually, students must move from the empirical to the theoretical understanding of the concept if they are to apply these ideas to traditional inference methodologies. This paper questions the effectiveness of some of these strategies, and discusses how computer based technologies may be used effectively to bring together these theoretical and empirical perspectives.
  • Author(s):
    Kunte, S.
    Editors:
    Phillips, B.
    Year:
    2002
    Abstract:
    In many books on Statistics, it is often stated that correlation between two variables X and Y is positive if, as X increases Y also increase. Equivalently, correlation between X and Y is positive, if large values of X most often correspond to the large values of Y and small values X, most often correspond to small values of Y. The correlation is negative if large values of X most often correspond to small values of Y and visa versa. With an example we show that this statement in not always correct. We also give the correct interpretation for the sign of the correlation and its relation to the behavior of the two random variables.
  • Author(s):
    Murray Black
    Year:
    2008
  • Author(s):
    Lawrence M. Leemis, Daniel J. Luckett, Austin G. Powell, and Peter E. Vermeer
    Year:
    2012
    Abstract:
    We describe a web-based interactive graphic that can be used as a resource in introductory classes in mathematical statistics. This interactive graphic presents 76 common univariate distributions and gives details on (a) various features of the distribution such as the functional form of the probability density function and cumulative distribution function, graphs of the probability density function for various parameter settings, and values of population moments; (b) properties that the distribution possesses, for example, linear combinations of independent random variables from a particular distribution family also belong to the same distribution family; and (c) relationships between the various distributions, including special cases, transformations, limiting distributions, and Bayesian relationships. The interactive graphic went online on 11/30/12 at the URL www.math.wm.edu/ leemis/chart/UDR/ UDR.html.
  • Author(s):
    Gardner, P. L., & Hudson, I.
    Year:
    1999
    Abstract:
    Statistics educators have previously noted that university students experience some difficulty in knowing when to use statistical concepts they have encountered in their courses. In this study, statistics educators rated the importance of various descriptive and inferential statistical procedures for inclusion in an introductory statistics course. Items describing research situations were written (each item representing a different procedure) and presented to a sample of undergraduate and postgraduate statistics students. Students were asked to decide which procedure was appropriate for addressing each research situation. Results revealed that identifying appropriate statistical procedures in new situations is indeed difficult.
  • Author(s):
    Carmen Díaz & Carmen Batanero
    Year:
    2009
    Abstract:
    The research question in this study was assessing possible relationships between formal<br>knowledge of conditional probability as well as biases related to conditional probability reasoning: fallacy of<br>the transposed conditional; fallacy of the time axis; base rate fallacy; synchronic and diachronic situations;<br>conjunction fallacy; and confusing independence and mutually exclusiveness. Two samples of university<br>students majoring in psychology and following the same introductory statistics course were given the CPR<br>test before (n = 177) and after (n = 206) formal teaching of conditional probability. Results indicate a<br>systematic improvement in formal understanding of conditional probability and in problem solving capacity<br>but little change in those items related to psychological biases
  • Author(s):
    Hubbard, R.
    Editors:
    Vere-Jones, D., Carlyle, S., &amp; Dawkins, B. P.
    Year:
    1991
    Abstract:
    This paper is concerned with the process of designing and implementing a new statistics course.
  • Author(s):
    Manouchehri, A., Lapp, D. A.
    Year:
    2003
    Abstract:
    We next reexamine the content of questions posed in the previous section. The first question in example 1 is stated in a closed form. At the outset it has the potential to determine the number of people who claim understanding of a piece of mathematics. The same is true for the first question in example 2, stated in closed form. The contrasting questions in each example, however, ask students to analyze and evaluate various methods.

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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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