Statistical Inference & Techniques

  • This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: testing for homogeneity of the odds ratio across strata, test statistics for homogeneity (Wald, score, or likelihood ratio statistics), test statistics for homogeneity with ordinal data, logistic regression, and logit for selected sampling.

    0
    No votes yet
  • 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.

    0
    No votes yet
  • This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: logistic regression on 3-dimensional table; estimating a common odds ratio; the Cochran, Mantel-Haenzel test; and confounding in logistic regression.

    0
    No votes yet
  • 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.

    0
    No votes yet
  • 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.

    0
    No votes yet
  • 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.

    0
    No votes yet
  • 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.

    0
    No votes yet
  • 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.

    0
    No votes yet
  • 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, 

    0
    No votes yet
  • 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.

    0
    No votes yet

Pages

register