This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture overs the following: covariance patterns and generalized estimating equations (GEE).
This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture overs the following: covariance patterns and generalized estimating equations (GEE).
This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture overs the following: conditional logistic regression, conditional likelihood for matched pairs, the non-central hypergeometric, the conditional maximum likelihood estimator (CMLE), conditional confidence interval for odds ratios, and McNemar's statistic.
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.
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.
This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: 2x2 contingency tables, fixing columns and rows, MLE, and previous topics within the context of contingency tables (variance, confidence intervals, standard error approximation, likelihood ratio, etc.).
This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: Wald test, score test, likelihood-ratio test, large sample confidence intervals, and the F distribution.
Includes detailed PowerPoints for 20 lectures for topics including generalized linear models, logistic regression, and random effects models.
This is a graduate level introduction to statistics including topics such as probabilty/sampling distributions, confidence intervals, hypothesis testing, ANOVA, and regression. Perfect for students and teachers wanting to learn/acquire materials for this topic.
The aim of this course is to cover sampling design and analysis methods that would be useful for research and management in many field. A well designed sampling procedure ensures that we can summarize and analyze data with a minimum of assumptions and complications. Perfect for both students and teachers wanting to learn/acquire materials for this topic.
This is a graduate level survey course that stresses the concepts of statistical design and analysis in biomedical research, with special emphasis on clinical trials. Perfect for students and teachers wanting to learn/acquire materials for this topic.