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Multivariate Techniques

  • This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: maximum likelihood estimation for logistic regression, sample size requirements for approximate normality of the MLE’s, confidence intervals, likelihood ratio statistic, score test statistic, deviance, Hosmer-Lemeshow goodness-of-fit statistic, the Hosmer-Lemeshow statistic, parameter estimates, scaled/unscaled estimates, residuals, grouped binomials, and model building strategies.

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  • This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: Mantel-Haenszel estimator of common odds ratio, confounding in logistic regression, univariate/multivariate analysis, bias vs. variance, and simulations.

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  • This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: absolute/relative measures, number needed to treat (NNT), relative risk, odds ratio, the delta method (with a multivariate extension), and a variance covariance matrix. 

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  • Those who complete this course will be able to select appropriate methods of multivariate data analysis, given multivariate data and study objectives; write SAS and/or Minitab programs to carry out multivariate data analyses; and interpret results of multivariate data analyses.  Perfect for students and teachers wanting to learn/acquire materials for this topic.

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  • "The purpose of this electronic service is to provide access to a collection of datasets suitable for teaching statistics. The datasets are stored either locally or on other computers throughout the world. The datasets have been organized by statistical technique to make it easier for you to find a dataset appropriate for your pedagogical needs. When a dataset is appropriate for several statistical techniques, it will appear under several categories. Each dataset consists of three files: one is a description of the data; the others are an ascii (text) file of the data and an Excel file of the data."
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  • This is an e-book tutorial for R. It is organized according to the topics usually taught in an Introductory Statistics course. Topics include: Qualitative Data; Quantitative Data; Numerical Measures; Probability Distributions; Interval Estimation; Hypothesis Testing; Type II Error; Inference about Two Populations; Goodness of Fit; Analysis of Variance; Non-parametric methods; Linear Regression; and Logistic Regression.
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  • A song about Multivariate Analysis of Variance (MANOVA). The lyrics were written by Alan Reifman of Texas Tech University and may be sung to the tune of the 1982 hit "Maneater" by Daryl Hall, John Oates, and Sara Allen. Musical accompaniment realization and vocals are by Joshua Lintz from University of Texas at El Paso.
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  • A sketch by Anastasia Mandel reinterpreting "Sunset, Eagle Cliff, New Hampshire" by Jasper Francis Cropsey (1867) with the statistical caption "Regression tree, still standing after the trials." This is part of a collection of sketches by Anastasia Mandel and their accompanying statistical captions written by Stan Lipovetsky and Igor Mandel that took first place in the cartoon & art category of the 2009 A-Mu-sing contest sponsored by CAUSE. The collection and their accompanying statistical captions discussed in the paper "How art helps to understand statistics" (Model Assisted Statistics and Applications, 2009) by Stan Lipovetsky and Igor Mandel in volume 4 pages 313-324. Free to use in classrooms and on course websites.
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  • Care must be taken in planning experiments so that the differences to be examined for significance should be those which furnish an answer to the question which we are asking. is a quote from British statistician William Sealy Gosset (a.k.a. Student: 1876 - 1937). The quote appears in a 1931 letter to "Biometrika" in which he was addressing some criticism of his work by Karl Pearson.
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  • ...no good statistician existed unless he, or she, had been so involved in practical experimentation that they appreciated and understood the problems of the experimenter, the process worker, the farmer and the laboratory assistant. is a quote of British applied statistician Stella V. Cunliffe (1917 - 2012). The quote comes from her Presidential address on November 12, 1975 to the Royal Statistical Society (she was the first women to hold the position). The full presentation can be found in "JRSS series A" vol 139 p. 1-19 and contains many interesting examples from her years working at Guiness Brewery and for the government at the Home Office.
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