Discusses the use of the computer software MINITAB in teaching statistics to explore concepts, simulate games of chance, transform the normal variable into a z-score, and stimulate small and large group discussions. (MDH)
Discusses the use of the computer software MINITAB in teaching statistics to explore concepts, simulate games of chance, transform the normal variable into a z-score, and stimulate small and large group discussions. (MDH)
Classroom demonstrations can help students gain insights into statistical concepts and phenomena. After discussing four kinds of demonstrations, the authors present three possible approaches for determining how much data are needed for the demonstration to have a reasonable probability for success. (Author/LMO)
One of the many difficulties facing a teacher of Statistics is to keep herself or himself informed about materials that are currently available, and requests have been made for an annotated bibliography of books that might prove useful to those teaching and using statistics in Schools. The ASA/NCTM Joint Committee has therefore started to compile such a bibliography, of which this is the first edition.
According to the author, there are five main components of teaching a statistics class. First, the importance of the subject matter must be demonstrated and conveyed to students by illustrating its application to the real world. It is suggested that teachers start the lecture with a few remarks about some real world problem that uses the specific method to be taught in the day's lecture. Second, a demonstration must be worked out prior to class presentation for a higher rate of success. Teachers should get students involved by having them gather and analyze their own data in class. Third, teachers should provide students with concrete real life problems that use the technique to be taught in the lesson. The author suggests that applications or activities are drawn from the students' own interests. To an extent, components 1-3 are similar to "examples training" and components 4 & 5 are similar to "formal training" (see Fong, Krantz, Nisbett; 1986). The fourth and fifth components refer to the actual lesson or lecture beginning with instruction of statistical and probability principles and ending with presentation of proofs or plausibility arguments.
The late introduction of conditional probability in the curriculum statements is probably related to the automatic association of conditional probability with Bayes's theorem and the complicated analyses involving Venn diagrams or tree diagrams to work out the inverse probabilities in conditional problems. No doubt Bayes's theorem is a complex topic that is likely to be mastered by most students only at the senior secondary level. However, several other more straightforward uses can be made of conditional probability earlier in the curriculum. The examples that follow illustrate how conditional statement can be used to introduce conditional probabilities, how early applications can be found in sporting data, how independence can be introduced naturally, and how conditional probability can be related to data collection and presentation in two-way tables. These four topics can be introduced in grades 8 through 10.
It is argued that a nonparametric framework for the introductory statistics course is both mathematically and conceptually simpler than the customary normal-theory framework. Two examples are considered: the Kendall rank correlation coefficient versus the Pearson product-moment correlation coefficient, and the confidence interval for the median versus the confidence interval based on the one sample t statistic.
The authors stress that graphical techniques are powerful analytic tools. To illustrate this point, the following techniques are presented in the context of regression analysis: a) stem-and-leaf plots; b) schematic plots; c) time-order plots; d) scatterplots; and e) probability plots. According to the authors, it is important that analysis be tailored to the data in instruction. This provides learners with opportunities to see that real life data analysis has its unique aspects and problems. To illustrate this point, the authors used data about "Old Faithful" in Yellowstone Park. Various plotting techniques were used in order to detect anomalies and to outline causes. This process allowed the researchers to determine the appropriate model for the data- autoregressive model.
This is an introduction to the book on how to teach statistics to high school students. The authors suggest using your own data collection in weather charts and business stories. Another suggestion is to have students find erroneous uses of statistics and statistical reasoning in newspaper articles and to bring them into class for analysis.
The authors state that athletics in general can be used to demonstrate probabilistic concepts and that these may be reinforced due to the motivational aspects of the examples. In this article, the planification of the NCAA Regional Basketball tournaments is examined as a probability problem (e.g. what is the probability of team #3 winning the regionals?). In calculating the probability of any team winning, the students must: 1) evaluate the probability of each game played; 2) assume independence of each contest; and 3) incorporate the relative strength of each team in the model. Three probability models are considered, the third being the most adequate. It seems to provide a good fit to the data and is considered interesting to students without requiring a strong mathematical background. The key point in this article is that these models demonstrate the multiplication principle and the additive property of mutually exclusive events in addition to the motivation provided by the topic itself.
This paper simply contains a long list of teaching aids that are available for sale from various companies such as transparencies, audio tapes, film strips, slides, films, video tapes, course packages, and probability devices.