Sorry, you need to enable JavaScript to visit this website.

Resource Library

Statistical Topic

Advanced Search | Displaying 61 - 70 of 663
  • This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: conditional independence, log-linear models for 2x2 tables, expected counts, logistic regression, odds ratio, parameters of interest for different designs and the MLEs, poisson log-linear model, double dichotomy, the multinomial, and the multinomial log-linear model.

    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: ordinal regression models, cumulative probabilities, non-proportional odds, score stat for proportionl odds, MLEs, the adjacent categories logit, and proportional odds model.

    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: generalized odds ratio, collapsed categories, polytomous (or multinomial) logistic regression, and maximum likelihood using the multinomial.  

    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: unconditional likelihood, elimination of nuisance parameters, and Mantel-Haenzsel estimate.

    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: 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.

    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: 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

Pages