Using web-based tools for teaching statistical concepts and experimentation skills
This paper describes a number of generally accessible web-based tools the authors developed over the last couple of years and used in several statistics courses, ranging from the introductory statistics course to a specialized design of experiments course. The first set of tools (VESTAC) illustrates statistical concepts, the second one (VIRTEX) consists of virtual experiments. We discuss some of our experiences with regard to the development and maintenance of these tools and their use in ex cathedra teaching sessions, in guided practice sessions and in student projects. Finally we discuss a third type of tools: distance experiments.
A structured overview on the use of computational simulators for teaching statistical methods
ABSTRACT Modern technology is affecting what is taught and how statistics is taught. Computer intensive methods like bootstrapping, generalized regression, coordinate exchange algorithms and computer experiment emulators are now part of the statistical toolkit. In parallel with these new methods and tools, a similar evolution appears in statistical education. Simulators offer opportunities for hands-on experience in the classroom, bridging the gap between theory and practice. In this article, we map a wide range of simulation based tools used in teaching statistical methods, putting them in the context of a quality ladder representing various maturity levels of organizations and course level sophistication. The mapping is designed to help educators assess and integrate options related to the incorporation of simulators as teaching aids in an educational and pedagogical roadmap. The mapping is applied to specific simulation products but provides a general framework that can be expanded and used in a generic way.
Predicting Harvesting Date of Processing Tomatoes by a Simulation Model

A computer program was developed for predicting the times of emergence, flowering, turning stage, and harvesting of processing tomatoes. The program was validated and calibrated by using 1972-1980 tomato data from 44 fields at 2 locations in Israel. Predictions are based on accumulation of heat units defined in terms of “physiological days”, where 1 physiological day is equivalent to a calendar day with a constant temperature of 26°C. The growing season was divided into 4 stages: from sowing to emergence, from emergence to flowering, from flowering to turning stage, and from turning stage to harvesting. Accumulation of physiological days during the first 2 stages is based on a linear function. During the last 2 stages, a quadratic function is used to calculate daytime heat units wherever the daily average temperature is above 20°. The maximum rate of development is at 26°. In the last stage, soil stress index also is taken into account. Use of the model makes it possible to predict the day of harvest with a precision of ±3 days, as compared with ±9 days when a daily mean systems is employed.