Literature Index

This paper is an attempt to suggest features of future statistics education in the light of the emerging paradigms of science, education and mathematics. The paper consists of three sections; an introduction clarifying the context and limitations of the present paper, an explanation of the included concepts and the conclusions about some features of the future statistics education in the light of the studied emerging paradigms. These features included the integration of statistics education through an "applied" approach to problems, rejecting linearity, concentrating on conceptual frameworks and "conditional prediction", and emphasizing the study of probability and the way to deal with the results of statistical analyses.

The paper reports some initiatives to freshen up the typical undergraduate business forecasting course. These include (1) students doing research and presentations on contemporary tools and industry practices such as neural networks and collaborative forecasting (2) insertion of Logistic Regression in the curriculum (3) productive use of applets available on the Internet to convey abstract concepts underlying ARIMA models and (4) showcasing forecasting tools in timely or familiar applications. These initiatives align with the best practices framed across the "Making Statistics More Effective in Schools of Business" (MSMESB) conferences. Course experiences and student feedback are also discussed.

This article highlights some of the differences between statistics and mathematics and suggests some implications of these differences for teachers and students. Our aim is to provoke thought and to presents ideas that will guide classroom practice.

In spite of the apparent simpicity of averages, many researchers have described difficulties in its understanding by students at different educational levels. In this work we present an assessment of these difficulties for future primary teachers, with the aim of adquately guiding the taching of this topic.<br><br>The analysis of the answers shows that these future teachers have difficulties in understanding the following points: Dealing with zero and atypical values when computing averages, relative position of mean, median and mode in asymmetrical distributions, choosing an adequate mesure of central value and using averages to compare distributions.<br><br>We conclude that the traditional approach to studying averages in context-free data collections, does not allow pupils to fully understand the meaning of the concept, what must include the following: a) relationships of averages with other central position values, b) representativeness of mean in symmetrical distributions; b) the mean as expected value in random sampling processes; c) the mean as fair quantitiy to distribute for obtaining uniform distributions in finite populations.