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Statistical Topic

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  • July 8, 2008 Teaching and Learning webinar presented by Shonda Kuiper, Grinnell College and hosted by Jackie Miller, The Ohio State University. Many instructors use projects to ensure that students experience the challenge of synthesizing key elements learned throughout a course. However, students can often have difficulty adjusting from traditional homework to a true research project that requires searching the literature, transitioning from a research question to a statistical model, preparing a proposal for analysis, collecting data, determine an appropriate technique for analysis, and presenting the results. This webinar presents multi-day lab modules that bridge the gap between smaller, focused textbook problems to large projects that help students experience the role of a research scientist. These labs can be combined to form a second statistics course, individually incorporated into an introductory statistics course, used to form the basis of an individual research project, or used to help students and researchers in other disciplines better understand how statisticians approach data analysis.

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  • February 10, 2009 Teaching and Learning webinar presented by Andrew Zieffler, Bob delMas, and Joan Garfield, University of Minnesota, and hosted by Jackie Miller, The Ohio State University. This webinar presents an overview of the materials and research-based pedagogical approach to helping students reason about important statistical concepts. The materials presented were developed by the NSF-funded AIMS (adapting and Implementing Innovative Materials in Statistics) project at the University of Minnesota (www.tc.umn.edu/~aims).

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  • May 25, 2010 Activity webinar presented by Ivan Ramler, St. Lawrence University and hosted by Leigh Slauson, Capital University. This webinar discusses an undergraduate Mathematical Statistics course project based on the popular video game Guitar Hero. The project included: 1) developing an estimator to address the research objective "Are notes missed at random?", 2) learning bootstrapping techniques and R programming skills to conduct hypothesis tests and 3) evaluating the quality of the estimator(s) under certain sets of scenarios.

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  • This applet shades the graph and computes the probability of X, when X is between two parameters x1 and x2. The user inputs the mean, standard deviation, x1 and x2. This applet should be resized for optimal viewing.

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  • This applet shows the normal or Gaussian distribution. The distribution has two parameters, the mean and the standard deviation. Click the draw button after filling in new values for the mean and the standard deviation to obtain a new diagram of the normal distribution.

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  • This calculator determines the level of significance for the Wilcoxon-Mann-Whitney U-statistic. Users can enter N1, N2, and U or simply enter the raw data.

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  • A searchable database of approximately 600 applets for teaching introductory statistics topics, including graphical displays, descriptive statistics, probability concepts, random variables, sampling and sampling distributions, confidence intervals, hypothesis testing, ANOVA, chi-square tests, correlation and regression, time series and forecasting, decision analysis, and quality control charts. Applets are arranged by topic and intended use. Information on each applet includes source and url as well as a brief description.

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  • DataFerrett is a unique data analysis and extraction tool -- with recoding capabilities -- to customize federal, state, and local data to suit your requirements. Using DataFerrett, you can develop an unlimited array of customized spreadsheets that are as versatile and complex as your usage demands. The DataFerrett helps you locate and retrieve the data you need across the Internet to your desktop or system, regardless of where the data resides. You can then develop and customize tables. Selecting your results in your table you can create a chart or graph for a visual presentation into an html page. Save your data in the databasket and save your table for continued reuse. The DataFerrett is a Beta testing version that will incorporate the latest bug fixes, enhancements, and new functionality that will be rolled into the DataFerrett after testing has been completed.

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  • This page of Statistical Java describes 11 different probability distributions including the Binomial, Poisson, Negative Binomial, Geometric, T, Chi-squared, Gamma, Weibull, Log-Normal, Beta, and F. Each distribution has its own applet in which users can manipulate the parameters to see how the distribution changes. The parameters are described on the main page as well as situations that would use each distribution. The equations of the distributions are not given. To select between the different applets you can click on Statistical Theory, Probability Distributions and then the Main Page. At the bottom of this page you can make your applet selection. This page was formerly located at http://www.stat.vt.edu/~sundar/java/applets/

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  • (Uses JAVA)  Some basic statistical analysis tools that allow the user to input their own data or use the pre-existing data and perform the desired test (e.g ANOVA, Descriptive, t-test, chi-square, correlation and regression).

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