Literature Index

Using real life examples in Introductory Statistics courses is now accepted as very desirable and even necessary. There are many resources available on the Internet and elsewhere that makes this particularly easy. In this paper we discuss some advantages of using homegrown examples, obtained mainly from consulting work. However, to be able to use these examples in undergraduate courses they must be framed in such a way that the students can understand them. We also give a number of examples where we have done this.

Simulating quickly a large number of repetitions of an experiment is a powerful tool in statistics. Simulations can be performed and explored individually by students in the classroom with graphics calculators like the TI83(Plus). Applications are estimating the probability of an event, predicting the profit in a game of chance, approximating the probability distribution of a random variable, drawing samples from a population, testing the quality of the random number generator. These applications will be illustrated with examples.

Statistical reasoning is often presented as a variety of problems that are explained through a series of "tests" - usually leaving many students bewildered. One of the key elements that is missing from such treatment is building a foundation for understanding what statistical reasoning is and how it works. Simulations, made possible by technology such as graphing calculators, can provide students a conceptual basis for inference. Generating sampling distributions can help them analyze the behavior of a given statistic, explore whether a given observation is unlikely, investigate how changing sample size changes the distribution, explore different kinds of distributions and what makes them different, and give them a sense of how to reason from data. Examples from the world outside of the classroom illustrate how simulation can be a tool in making sensible decisions and give students opportunities to see why statistics is important.

This article presents an activity which simulates the linear regression model in order to verify the probabilistic behaviour of the resulting least-squares statistics in practice.