• The introduction of statistics into local mathematics syllabuses in 1984 has stimulated much activity in the field of teacher education, in both pre- and in-service courses. Bearing in mind that "the teaching of statistics is substantially more difficult than many other branches of mathematics", that most of our teachers have an inadequate background in statistics and that few of them have taken a methods course covering the teaching of the subject, it was clear that there was a need for some form of in-service education and training to help teachers at the junior high school level to implement the new syllabus. Thus a short in-service course was designed to meet the needs of these teachers.

  • The theoretical basis of this paper is the modeling of students' conceptions about a specific topic as a qualitative and systemic construct. Following therefrom, a discussion about the role of multivariate analysis for studying the structure of these conceptions and for building explanatory models relating this structure to task, cognitive and instructional variables. An empirical study of students' intuitive conceptions referring to statistical association is used as an example.

  • Time Series Analysis is a particular area of Statistics that has seen remarkable progress in the last 20 years. There has been increased activity and interest in both the theory and practice of the subject that has led in some sense to a unification of methodologies that existed previously. It is clearly not pure coincidence that the growth in Time Series Analysis has occurred at the same time as the growth of computing availability. Software of suitable quality was a little slow in appearing at first. But recent years have seen the introduction of many new and re-vamped statistical packages, and most of these nowadays contain quite extensive Time Series routines.

  • In this paper we consider the nature of a statistics course and discuss the role of the computer in it. In particular, we discuss the course for the first-year students at the university level.

  • The present paper is essentially a preliminary report on the author's experience of teaching a course in Data Analysis to students at the Naval Postgraduate School, Monterey, California. The main emphasis in this paper is on the use of graphics packages as a teaching tool, and, in particular, on how these packages can assist the student (and the teacher) to achieve greater insight into both the data analytic and methodological aspects of our discipline.

  • This paper begins with a brief discussion of the role of the statistician and how this is changing, particularly in view of the microcomputer revolution. Historically the training of the professional statistician has been undertaken within academic institutions and has often incorporated little practical training. The advent of relatively cheap and accessible computer power has allowed more applied elements to be incorporated into statistical education, in particular larger and more realistic data sets may be used, models fitted (and compared) with greater ease, and so on. There are numerous ways in which this computing power may be exploited in the education of statisticians and this paper outlines a number of these and discusses their usefulness.

  • The set of computer programs described in the present paper have been developed since the summer of 1984 in support of a teacher outreach program administered by the Woodrow Wilson Foundation in Princeton, New Jersey, and the Quantitative Literacy project sponsored by the American Statistical Association/National Council of Teachers of Mathematics Joint Committee on the Curriculum in Statistics and Probability. In general, the statistical techniques supported by the Nightingale programs are the Exploratory Data Analysis techniques which have appeared in Exploring Data (Landwehr & Watkins, 1986). From inception, the Nightingale Programs have been designed to support teachers who would bring statistics and EDA techniques into the classroom. The present writer has used the programs in his own statistics class at the high school level in the United States and has supported other teachers' use in science and social studies classes. They have been field tested over the past two years by students and teachers, and myriad "perfecting amendments" have been offered and taken advantage of.

  • This paper discusses five illustrations of computer enriched instruction. These illustrations are: - Computer generated assignments - Decision making in an advanced regression course - Computer drawn transparencies - Computer graphics for demonstrating statistical ideas - Lecture graphic support

  • Those familiar with a statistical package are often surprised at how long it takes a novice to accomplish even a simple task. In a college environment, students often complain about the countless hours spent on a simple assignment. An examination of their computer printouts and written work often fails to reveal where the students spent their hours. Unfortunately, it is possible for students to understand conceptually what to do but still not be able to do it. In an effort to learn where students actually ran into difficulty and discover more about the types of mistakes they were making, we undertook a series of experiments at Babson College. These experiments involved two instructors each teaching two sections of an applied statistics course. There were 154 undergraduate students in these four sections. an extensive set of references dealing with this area may be found in Kopcso, McKenzie, and Rybolt (1985). References to our past work and another recent article are present at the end of this paper.

  • I have picked out four areas in which computers are changing the teaching and learning of statistics and I have illustrated them with screen dumps from programs that are currently available in the UK. The four areas are: Content, Approach, Emphasis and Understanding.