Journal Article

  • Both teaching and learning are increasingly becoming technology-oriented processes, and teachers are struggling to keep up with rapid technological advances. The Internet, one of the most popular media of communication, provides fast access to vast amounts of information. There are many web sites that contain information useful for Advanced Placement Statistics teachers. This paper provides information about Internet resources available for project ideas, datasets, conferences, technical support, class notes, and much more.

  • This study investigated the relationship between a constructivist learning environment and students' attitudes toward statistics. The Constructivist Learning Environment Survey (CLES) and the Attitude Toward Statistics scale (ATS) were used to measure the environment and attitudes respectively. Participants were undergraduate students of an introductory college statistics course. They were drawn from Seattle Pacific University in the US and the University of Zimbabwe.<br>The study had two components. One component addressed hypotheses examining potential differences between groups and the other explored relationships between variables. The environment was not manipulated and the data was collected from courses that already existed in the form studied. For this reason, the overall design of the study had causal comparative and correlational elements. A constructivist learning environment was found to be significantly related to students' attitude toward statistics. Furthermore, there were significant differences between the groups based on location.<br>The study examined the similarities and differences in perceptions and attitudes of students from two very different learning milieus. Cross-cultural comparisons have the potential to generate new insights into statistical pedagogy and the role noncognitive socio cultural variables play in teaching statistics to college-age students.

  • Spreadsheet software is widely used and now includes statistical functionality. This paper discusses the issues raised in teaching statistics with spreadsheet software. The principal concerns relate to aspects of the spreadsheet view of computation that make it difficult to keep track of what calculations have actually been carried out or to control the spreadsheet by means of a script. We also discuss a number of other advantages and deficiencies of spreadsheets for teaching statistics.

  • This study examined the extent to which statistics and mathematics anxiety, attitudes toward mathematics and statistics, motivation and mathematical aptitude can explain the achievement of Arabic speaking pre-service teachers in introductory statistics. Complete data were collected from 162 pre-service teachers enrolled in an academic teacher-training program for elementary and middle schools in Israel. The data, except for the two achievement tests, were collected during statistics classes prior to the midterm examination. The majority (96%) of participants were female students with a mean age of 21. As regards variables examined in this study, only the hypothesized effect of mathematical aptitude on achievement in statistics was relatively large. The results also indicated that mathematical aptitude, mathematics anxiety, attitudes toward mathematics and statistics, and motivation, together accounted for 36% of the variance in achievement in introductory statistics for the current sample.

  • Scores of 1997 Big Ten Conference men's basketball games involving the University of Iowa Hawkeyes are analyzed with a series of scatterplots accompanied by formal bivariate statistical inference. The analyses reveal that the Hawkeyes' defensive performance is largely unaffected by the site of the game, while offensive performance dips significantly in games played on opposing teams' courts.

  • In this article, a very simple and yet useful feature of Excel called the SPIN BUTTON is used to illustrate two concepts associated with attribute acceptance sampling plans. The first concept is calculating the probability of lot acceptance based on which the operating characteristic (OC) curve of an attribute sampling plan is drawn. The SPIN BUTTON can show, visually, that the exact probability of lot acceptance calculated using the Hypergeometric distribution can be approximated by the Binomial distribution. The second concept is how the probability of lot acceptance changes when either one of the three parameters N, n, c of a sampling plan changes. The SPIN BUTTON can also visually show us how the shape of the OC curve of a sampling plan changes when the parameters vary.

  • The purposes of this paper are to illustrate the use of several assessment strategies in an advanced course in statistics, and to present the results of student ratings for each assessment strategy in terms of difficulty, appropriateness, level of learning achieved, and preference. The assessment strategies used include structured data analysis assignments, open-ended data analysis assignments, reviews of applied research articles, and annotating computer output from multivariate software procedures. Findings indicate that students "prefer" instructor-directed or structured assignments overall, but feel they learn the most when the assessment is unstructured and requires greater self-direction. Suggestions for incorporating these assessment strategies into the multivariate classroom, as well as examples of each strategy, are included in this study.

  • In Indian Universities, courses titled 'Statistics Practical' usually involve only numerical evaluation. There is very little scope for independent thinking and decision making on the part of the students. We report here our experience of teaching a practical course on sampling techniques in a different way. On the whole, it was an encouraging exercise.

  • There is a potential misuse of the power function under the logical extreme when the null hypothesis is true. The power function is defined to measure the probability of rejecting the null given any value of the parameter being tested. It can be used to obtain the power and the beta values only under the alternative hypothesis. When the null is true, the power function can be used to obtain the size of the test. The power and the probability of committing a Type II error are, however, undefined and, hence, the power function should not be used to obtain these values.

  • Teaching prediction intervals to introductory audiences presents unique opportunities. In this article I present a strategy for involving students in the development of a nonparametric prediction interval. Properties of the resulting procedure, as well as related concepts and similar procedures that appear throughout statistics, may be illustrated and investigated within the concrete context of the data. I suggest a generalization of the usual normal theory prediction interval. This generalization, in tandem with the nonparametric method, results in an approach to prediction that may be systematically deployed throughout a course in introductory statistics.

Pages