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Logistic Regression

  • 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.

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

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  • 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.

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  • 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.

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  • This graduate level course offers an introduction into regression analysis. A researcher is often interested in using sample data to investigate relationships, with an ultimate goal of creating a model to predict a future value for some dependent variable. The process of finding this mathematical model that best fits the data involves regression analysis.  STAT 501 is an applied linear regression course that emphasizes data analysis and interpretation and is perfect for both students and teachers of statistics courses.

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  • The goal of this text is to provide a broad set of topics and methods that will give students a solid foundation in understanding how to make decisions with data. This text presents workbook-style, project-based material that emphasizes real world applications and conceptual understanding. Each chapter contains:

    • An introductory case study focusing on a particular statistical method in order to encourage students to experience data analysis as it is actually practiced.
    • guided research project that walks students through the entire process of data analysis, reinforcing statistical thinking and conceptual understanding.
    • Optional extended activities that provide more in-depth coverage in diverse contexts and theoretical backgrounds. These sections are particularly useful for more advanced courses that discuss the material in more detail. Some Advanced Lab sections that require a stronger background in mathematics are clearly marked throughout the text.
    • Data sets from multiple disciplines and software instructions for Minitab and R.

    The text is highly adaptable in that the various chapters/parts can be taken out of order or even skipped to customize the course to your audience. Depending on the level of in-class active learning, group work, and discussion that you prefer in your course, some of this work might occur during class time and some outside of class. 

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  • A joke to use when teaching about choices of binary response data models like the Logistic or Probit models by University of Texas at El Paso professor of Mathematical Sciences, Lawrence Mark Lesser (1964-).

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  • If we have data, let's look at data. If all we have are opinions, let's go with mine. is a quote by American entrepreneur James Love Barksdale (1943 - ) former president and CEO of Netscape Communications.
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  • If the experiments be quite simple the question may be without great importance; but when their requirements as to time or expenditure come into account the problem arises, how the observations should be chosen in order that a limited number of them may give the maximum amount of knowledge. is a quote by Danish Statistician Kirstine Smith (1878 - 1939). The quote appears in the introduction to her 1918 article on optimal experimental design in the journal Biometrika (the first such article in the literature).
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