Statistical Inference & Techniques

  • This probability activity discusses the differences among various kinds of studies and which types of inferences can legitimately be drawn from each, as well as how sample statistics reflect the values of population parameters and use sampling distributions as the basis for informal inference. The procedure and assessment are provided.
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  • This Java applet demonstrates confidence intervals for the mean. It allows the user to alter sample size, samples taken, intervals, and the option of standard error. The applet displays sample values, such as average, standard deviation, and percent covered.

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  • The BUGS (Bayesian inference Using Gibbs Sampling) project is concerned with flexible software for the Bayesian analysis of complex statistical models using Markov chain Monte Carlo (MCMC) methods. This site is primarily concerned with the stand-alone WinBUGS 1.4.1 package, which has a graphical user interface and on-line monitoring and convergence diagnostics. This program can be downloaded for free from the site.

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  • This program allows the student to explore the nature of sampling distributions of sample means and sample proportions. The software provides separate windows for building population distributions, drawing and viewing random samples from the population, exploring the behavior of sampling distributions of sample means, and exploring the behavior of confidence intervals.
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  • This powerpoint presentation from INFORMS Applied Probability Society provides a brief overview of switches, routers, input-queued crossbars, combined input- and output-queued switches, buffered crossbars, and algorithms for bandwidth partitioning, security, encryption, and deep packet inspection.
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  • This tutorial presentation from INFORMS Applied Probability Society covers the long range dependence (or long memory)property of certain stationary stochastic processes.
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  • This introductory probability textbook, freely available here in pdf format, emphasizes the use of computing to simulate experiments and make computations. A set of programs that go with the book and the answers to the odd-numbered problems are also available from this site. Chapter headings include: Discrete Probability, Continuous Probability Densities, Combinatorics, Conditional Probability, Distributions and Densities, Expected Value and Variance, Sums of Random Variables, Law of Large Numbers, Central Limit Theorem, Generating Functions, Markov Chains, and Random Walks.
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  • The Decision Bonsai are a hybrid of concept maps and decision trees. They were originally developed to give introductory statistics students a map to inference procedures but have evolved to be used for other topics. The tree is 'grown' during the semester so that students build a picture of the relationships in their mind. Recent work is moving toward the development of more complete concept maps for introductory statistics, statistical quality methods and probability and stochastic processes courses. These Decision Bonsai would be then pointed to at appropriate points in the concept maps.
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  • This website serves as an online textbook for introductory statistics, covering topics such as summarizing and presenting data, producing data, variation and probability, statistical inference, and control charts.
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  • I am not accustomed to saying anything with certainty after only one or two observations. is a quote by Flemish anatomist Andreas Vesalius (1514-1564). The quote is found in "Epistola rationem modumque propinandi radicis Chynae decocti".
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