Statistical Inference & Techniques

  • I think the essential thing if you want to be a good statistician, as opposed to being a mathematician, is to talk to people and find out what they're doing and why they're doing it. is a quote from Florence Nightingale David (1909 - 1993). The quote appears at the end of an interview published in "Statistical Science" in 1989 (p. 235-246) in response to a question from Nan Laird asking for advice for practicing statisticians.

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  • This joke can be used to motivate class discussions on the assumptions underlying drawing conclusions from data (especially the assumption of stationarity). The joke is a revision of a story in "The Angel's Dictionary: A modern tribute to Ambrose Bierce" by Edmund Volkart - also quoted in "Statistically Speaking: A dictionary of Quotations" by Carl Gaither and Alma Cavazos-Gaither (page 62). The revision (to make the story suitable for classroom use) was written by Dennis Pearl, The Ohio State University.

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  • A cartoon to teach ideas of elementary probability. Cartoon by John Landers (www.landers.co.uk) based on an idea from Dennis Pearl (The Ohio State University) in 2008. Free to use in the classroom and on course web sites.

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  • A cartoon to teach about confidence intervals. Cartoon by John Landers (www.landers.co.uk) based on an idea from Dennis Pearl (The Ohio State University) in 2008. Free to use in the classroom and on course web sites.

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  • A cartoon to teach ideas of probability ad the Law of Large Numbers. Cartoon by John Landers (www.landers.co.uk) based on an idea from Dennis Pearl (The Ohio State University). Free to use in the classroom and on course web sites.

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  • This activity is an example of Cooperative Learning in Statistics. It uses student's own data to introduce bivariate relationship using hand size to predict height. Students enter their data through a real-time online database. Data from different classes are stored and accumulated in the database. This real-time database approach speeds up the data gathering process and shifts the data entry and cleansing from instructor to engaging students in the process of data production. Key words: Regression, correlation data collection, body measurements
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  • This activity makes use of a campus-based resource to develop a "capstone" project for a survey sampling course. Students work in small groups and use a complex sampling design to estimate the number of new books in the university library given a budget for data collection. They will conduct a pilot study using some of their budget, receive feedback from the instructor, then complete data collection and write a final report.
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  • In this activity, students explore calculations with simple rates and proportions, and basic time series data, in the context of news coverage of an important statistical study. From 1973 to 1995, a total of 4578 US death penalty cases went through the full course of appeals, with the result that 68% of the sentences were overturned! Reports of the study in various newspapers and magazines fueled public debate about capital punishment.
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  • In this activity, students learn the true nature of the chi-square and F distributions in lecture notes (PowerPoint file) and an Excel simulation. This leads to a discussion of the properties of the two distributions. Once the sum of squares aspect is understood, it is only a short logical step to explain why a sample variance has a chi-square distribution and a ratio of two variances has an F-distribution. In a subsequent activity, instances of when the chi-square and F-distributions are related to the normal or t-distributions (e.g. Chi-square = z2, F = t2) will be illustrated. Finally, the activity will conclude with a brief overview of important applications of chi-square and F distributions, such as goodness-of-fit tests and analysis of variance.
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  • This group activity illustrates the concepts of size and power of a test through simulation. Students simulate binomial data by repeatedly rolling a ten-sided die, and they use their simulated data to estimate the size of a binomial test. They carry out further simulations to estimate the power of the test. After pooling their data with that of other groups, they construct a power curve. A theoretical power curve is also constructed, and the students discuss why there are differences between the expected and estimated curves. Key words: Power, size, hypothesis testing, binomial distribution
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