Thomas H. Short

Conditional Probability and Education Reform: Are They Compatible?

We demonstrate that one can teach conditional probability in a manner consistent with many features of the statistics education reform movement. Presenting a variety of applications of conditional probability to realistic problems, we propose that interactive activities and the use of technology make conditional probability understandable, interactive, and interesting for students at a wide range of levels of mathematical ability. Along with specific examples, we provide guidelines for implementation of the activities in the classroom and instructional cues for promoting curiosity and discussion among students.

Protocols and Pilot Studies: Taking Data Collection Projects Seriously

Although there is consensus among statistics educators that student data collection projects are of substantial value, we feel that the planning and piloting phases of data collection are often neg...

Bayes Estimators for the Continuous Uniform Distribution

Classical estimators for the parameter of a uniform distribution on the interval (0, θ) are often discussed in mathematical statistics courses, but students are frequently left wondering how to dis...

Readability of Educational Materials for Patients with Cancer

The American Cancer Society and the National Cancer Institute both develop pamphlets and booklets to inform patients with cancer and their families about the nature and treatment of the illness. Wr...

Stats: Data and Models

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Applied Bayesian Modelling

Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content.

Applied Statistics With Microsoft Excel

deviance. Observed and model predicted probabilities are examined graphically and by grouping to compare estimated and observed relative frequencies. Finally, Chapter 12, “Survival Analysis,” introduces the basics of censored data, survival and hazard functions, and the resulting specialized R data structures and modeling procedures. The Kaplan–Meier product limit estimate is introduced, and survival plots are generated. The log-rank test and the Cox proportional hazards model are illustrated. I can recommend Introductory Statistics With R to its target audience. The author provides an excellent overview of R. I found the wealth of clear examples educational and a practical way to preview both R and S.