Linear Models

  • 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
  • Includes detailed PowerPoints for 20 lectures for topics including generalized linear models, logistic regression, and random effects models.

    0
    No votes yet
  • 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.

    5
    Average: 5 (1 vote)
  • This course covers methodology, major software tools and applications in data mining. By introducing principal ideas in statistical learning, the course will help students to understand conceptual underpinnings of methods in data mining. It focuses more on usage of existing software packages (mainly in R) than developing the algorithms by the students. The topics include statistical learning; resampling methods; linear regression; variable selection; regression shrinkage; dimension reduction; non-linear methods; logistic regression, discriminant analysis; nearest-neighbors; decision trees; bagging; boosting; support vector machines; principal components analysis; clustering. Perfect for students and teachers wanting to learn/acquire materials for this topic.

    0
    No votes yet
  • Statistics is often taught as though the design of the data collection and the data cleaning have already been done in advance.  However, as most practicing statisticians quickly learn, typically problems that arise at the analysis stage, could have been avoided if the experimenter had consulted a statistician before the experiment was done and the data were conducted.  This course is created to provide an understanding of how experiments should be designed so that when the data are collected, these shortcomings are avoided.  Perfect for students and teachers wanting to learn/acquire materials for this topic.

    5
    Average: 5 (1 vote)

Pages

register