Discrete

  • Gives a very brief explanation of Spearman' rho and how it differs from Pearson's r.
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  • The online "Engineering Statistics Handbook" provides a section (4.6 Case Studies in Process Modeling) using detailed realistic examples from physical science and engineering applications. Examples in Load Cell Calibration, Alaska Pipeline Ultrasonic Calibration, Ultrasonic Reference Block Study, and Thermal Expansion of Copper Case Study are presented in a step-by-step manner.
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  • This site provides case studies which cover subject areas including: analysis of variance, boxplots, confidence intervals, contrast among means, correlated t-test, correlation, histograms, independent groups t-test, regression, repeated measures ANOVA, and t-tests.
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  • This case study explores statistics on divorce rates using Markov chains. Two closely related statistics are presented: the chance of divorcing in a given year and the chance of divorcing over the lifetime of a marriage. Accompanying teacher instructions are found at http://ublib.buffalo.edu/libraries/projects/cases/markov/markov_notes.html
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  • Teacher instructions to accompany "Markov vs. Markov" case study found at http://ublib.buffalo.edu/libraries/projects/cases/markov/markov.html.
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  • This applet allows you to manipulate the starting population, age-class survival rates, and age-class fecundity rates over 10 generations for up to 6 age classes. The default gives you the same population size for each age class as well as the same fecundity rate and survival rates. Move the sliders for each age class to manipulate each of these factors. You will see the relative proportions of each age class will change over time, but will eventually reach a stable age distribution.
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  • This page will generate a graphic and numerical display of the properties of a binomial sampling distribution, for any values of p and q, and for values of n between 1 and 40, inclusive.

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  • For a situation in which independent binomial events are randomly sampled in sequence, this page will calculate (a) the probability that you will end up with exactly k instances of the outcome in question, with the final (kth) instance occurring on trial N; and (b) the probability that you will have to sample at least N events before finding the kth instance of the outcome.

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  • This page calculates the Poisson distribution that most closely fits an observed frequency distribution, as determined by the method of least squares. Users enter observed frequencies, and the page returns the fitted Poisson frequencies, the mean and variance of the observed distribution and the fitted Poisson distribution, and R-squared.

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  • This lesson on the Poisson distribution explains the theory, history, and applications of the distribution and gives examples and a multiple choice test.
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