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  • December 12, 2006 webinar presented by Michelle Everson, University of Minnesota, and hosted by Jackie Miller, The Ohio State University. This webinar focuses on describing an introductory statistics course that is taught completely online. The structure of this course is described, and samples of different student assignments and activities are presented. Assessment data and student feedback about the course are also presented. Discussion focuses on issues that must be considered when developing and administering an online course, such as the instructor's role in the online course and ways to create an active learning environment in an online course.

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  • March 13, 2007 webinar presented by Andrew Zieffler, University of Minnesota, and hosted by Jackie Miller, The Ohio State University. The interdisciplinary field of inquiry that is statistics education research spans a diverse set of disciplines and methodologies. A recent review of a subset of this literature, the research on teaching and learning statistics at the college level, was used to raise some practical issues and pose some challenges to the field of statistics education. These are addressed in this CAUSE webinar. In addition, a recent doctoral dissertation study is used to illustrate some of these challenges and offer suggestions for how to deal with them.

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  • ...experiment without mathematics will neither sufficiently discipline the mind or sufficiently extend our knowledge... is a quote by Scottish geophysicist Balfour Stewart (1828 - 1887). The quote is found in a letter from Stewart to Henry Roscoe of Owens College on June 2, 1870.

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  • A poem useful in teaching aspects about hypothesis testing, especially the caveat that unimportant differences may be deemed significant with a large sample size. The poem was written by Mariam Hermiz, a student at University of Toronto, Mississauga in Fall 2010 as part of an assignment in a biometrics class taught by Helene Wagner. The poem was awarded first place in the poetry category of the 2011 CAUSE A-Mu-sing contest.

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  • How can we accurately model the unpredictable world around us? How can we reason precisely about randomness? This course will guide you through the most important and enjoyable ideas in probability to help you cultivate a more quantitative worldview.

    By the end of this course, you’ll master the fundamentals of probability and random variables, and you’ll apply them to a wide array of problems, from games and sports to economics and science.  This course includes 62 interactive quizzes and more than 400 probabilty-based problems with solutions.  Access to this course requires users to sign up for a free account.

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  • This presentation is a part of a series of lessons on the Analysis of Categorical Data.  This lecture provides a review of probability and statistical concepts such as conditional probabilities, Bayes Theorem, sensitivity and specificity, and binomial and poisson distributions.

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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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  • The emphasis in this course will be understanding statistical testing and estimation in the context of "omics" data so that you can appropriately design and analyze a high-throughput study. Since the measurement technologies are evolving rapidly, important objectives of the course are for students to gain a basic understanding of statistical principles and familiarity with flexible software tools so that you can continue to assess and use new statistical methodology as it is developed for new types of data.

    By the end of the course, you should be able to tailor the analysis of your data to your needs while maintaining statistical validity.  You should come out of the course with insight so that you can assess the validity of new statistical methodologies as they are introduced as well as understand appropriate statistical analyses for data types not discussed in the class. 

    Perfect for students and teachers wanting to learn/acquire materials for this topic.

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  • The objective of this course is to learn and apply statistical methods for the analysis of data that have been observed over time.  Our challenge in this course is to account for the correlation between measurements that are close in time. Perfect for students and teachers wanting to learn/acquire materials for this topic.

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