• It is a well-known fact that, at least in American colleges and universities, most professors are highly trained in the subject matter of their chosen discipline but receive little or no formal training in how to teach that subject matter. These professors (including most of us) have learned teaching by experience and from informal observation of our own professors when we were students. This is quite a contrast to the training received by elementary and secondary school teachers. We will discuss the "why" and "who" of the training of teachers and then turn our attention to the question of "when" and "how". To this end we will discuss what has been done in the Statistics Department at the University of Missouri-Columbia. We provide versions of handout given to them.

  • It is clear to all of those who are attending this conference that statistics should be an important component in the education of all people and that statistics education should begin with young children and be carried on continuously throughout the years of formal education. However, a rather large percentage of teachers, especially at the school level, have not themselves experienced an adequate statistics education and hence lack knowledge about statistical topics per se as well as what types of statistical activities would be appropriate for their students and how to integrate such activities into their course of study. It is necessary to attack this cycle of ignorance from several directions: 1) the initial training of teachers should have an adequate statistical component; 2) there should be a variety of inservice programs for practicing teachers at colleges, within school systems, and for teacher self-study; 3) curriculum developers and directors should more enthusiastically incorporate adequate statistical activities in their programs and courses of study; 4) there should be more active research into questions of statistical didactics; and 5) there should be developed more effective statistical lessons and learning sequences for students.

  • The Schools Council Project on Statistical Education, working in England and Wales and based at the University of Sheffield, was the first national project in the world to look specifically at the statistical education needs of all pupils in the 11-16 age range and to develop teaching materials to meet these needs. In this paper I have tried to show some of the many things anyone developing new teaching materials has to take into account. I have indicated how they affected the teaching material of the Schools Council Project on Statistical Education and the approach taken by the project team. I hope that this will help any of you who have to develop teaching material in your own countries to identify the areas of concern and produce relevant material for you pupils.

  • This paper asks various questions concerning the relevance of Exploratory Data Analysis (EDA) ideas for teaching 16-19 year old's, and reports on a workshop which considered these questions. Four key questions can be posed which are pertinent to the question of preparing EDA materials for use at school level: (a) are EDA ideas relevant at this level? (b) if so, then which ideas and techniques should be taught? (c) which data sources are sufficiently motivating for this age group? (d) how should or could such material be prepared?

  • In recent years there has been considerable and growing interest in the use of statistics throughout many sectors of our society. The quantitative nature of this discipline has created opportunities for scientists to participate in the new technology of the twentieth century, characterized by its unique methodology, akin to what is termed "the scientific method" in the philosophy of science. This increased demand for statistical knowledge can be met adequately if the universities will produce qualified college teachers of statistics. We look specifically at preparing college teachers of statistics, by providing required competencies via coursework, opportunities for undergraduate teaching and statistical consulting, and in-service training.

  • This paper presents the status of Indian universities with respect to teaching and research in Design of Experiments. In addition, some suggestions to improve the existing courses and research facilities are included.

  • One of the important issues not adequately addressed is the training of teachers of statistics for schools and colleges. In this paper, we propose to provide a framework to enrich the training of statistics teachers at the college level.

  • There does seem to be a coming together in Europe concerning what should be taught in probability and statistics at the school level when compared with 10 to 15 years ago. There is, though, a lot that we can still learn from each other and there are many ways in which we could be of mutual help.

  • The teaching of basic statistics course can be greatly enhanced with the use of widely available statistical software. Pocket calculators can relieve some of the computational drudgery of statistics but are of little help with data plotting, one of the key steps in the statistical analysis process. By using computers in basic statistics courses, we can present students with real data sets and problems and teach them how to approach and analyze data. In this paper, the basic steps in the statistical analysis process are listed, and this paper, the basic steps in the statistical analysis process are listed, and the important role that computers can play in some of these steps is emphasized.

  • Access to a collection of computer programs for the classical distribution functions, particularly if these can be readily used interactively by any student, is an important teaching tool for teachers of probability and statistics. The availability of such a collection allows the teacher and student to concentrate on the concepts and development of probability and statistics with little if any emphasis on the details of determining probabilities from tables. Algorithms are presented that provide a useful array of methods for evaluating the classical distribution functions.