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Statistical Inference & Techniques

  • This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: iterative solutions to non-linear equations, score equations for exponential class variables, Newton-Raphson vs. Fisher’s Scoring, Logistic Regression for an R × 2 tables, saturated model, odds ratios when rows are not ordinal, goodness of fit, likelihood ratio statistic for nested models, and residuals.

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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: the poisson log-linear model, poisson regression, estimated rate ratio, and negative binomial distribution.

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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: linear probability model, non-constant variance, logistic model, logit transformation, and probit link.

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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: linear regression, generalized linear models, link function, deviance, and modeling.

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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: Pearson's residuals and rules for partitioning an I x J contingency tables as ways to determine association between variables.

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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: linear association, correlation coefficient, ridits/modified ridits, nonparametric methods, Cochran-Armitage Trend test, 

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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: uncertainty coefficient, ordinal trends, the gamma statistic and linear association, conditional independence, marginal independence, and Simpson's Paradox.

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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: generalized IxJ contingency tables, degrees of freedom, Fisher's exact test, and generalized odds ratio.

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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: Pearson's chi-square; the empirical logit; and prospective, case-control, and cross-sectional studies.

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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: Pearson's chi-square; the empirical logit; and prospective, case-control, and cross-sectional studies.

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