Categorical Methods

  • This set of pages is an introduction to Maximum Likelihood Estimation. It discusses the likelihood and log-likelihood functions and the process of optimizing.
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  • This site gives an explanation, a definition of, and an example using comparison of two means. Topics include confidence intervals and significance tests, z and t statistics, and pooled t procedures.
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  • This site gives an explanation, a definition and an example of inference for categorical data. Topics include confidence intervals and significance tests for a single proportion, as well as comparison of two proportions.
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  • This site gives an explanation, a definition and an example of chi-square goodness of fit test. Topics include chi-square test statistics, tests for discrete and continuous distributions.
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  • This site gives an explanation, a definition and an example of two-way tables and the chi-square test. Topics include: categorical data analysis for two variables, and tests of association.
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  • This online, interactive lesson on special distributions provides examples, exercises, and applets covering normal, gamma, chi-square, student t, F, bivariate normal, multivariate normal, beta, weibull, zeta, pareto, logistic, lognormal, and extreme value distributions.
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  • This collection of applets simulate many different statistical concepts such as: distributions, correlation, hypothesis testing, regression, and ANOVA.
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  • This page performs a Kolmogorov-Smirnov "Goodness of Fit" test for categorical data. Users enter observed frequencies and expected frequencies for up to 8 mutually exclusive categories. The applet returns the critical values for the .05 and .01 levels of significance.

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  • The seventh chapter of an online Introduction to Biostatistics course. Two sets of lecture notes are provided (only the first one works). Additionally, links for additional reading and exercises with solutions are provided.
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  • The larger the degrees of freedom, the closer the t-density is to the normal density. This reflects the fact that the standard deviation s approaches for large sample size n. You can visualize this in the given applet by moving the sliders.
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