Algebra level symbolic math

  • This laboratory introduces students to the basics of the Minitab software. Students make use of a basic example (water consumption and temperature) to introduce students to manipulation of data, calculation of descriptive statistics, creation of histograms, boxplots and scatterplots. Students are asked to hand in the results they have produced. Accompanying documents give model solutions.
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  • In this free online program, "students will see how the computer can graph multivariate data and various ways of presenting it. The program concludes with an example of a study of environmental stresses in the Chesapeake Bay." This individual video is accessed by scrolling down to the "Individual Program Descriptions - 10. Multidimensional Data Analysis" and click the "VOD" icon at the top-right of the description.
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  • In this free online video program, students will learn that "causation is only one of many possible explanations for an observed association. This program defines the concepts of common response and confounding, explains the use of two-way tables of percents to calculate marginal distribution, uses a segmented bar to show how to visually compare sets of conditional distributions, and presents a case of Simpson's Paradox. The relationship between smoking and lung cancer provides a clear example."
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  • A searchable database of more than 1000 test questions for introductory statistics concepts. The user is prompted to select subject material and learning outcome expectations from a variety of question formats and then downloads the items and can edit the test with a word processor.
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  • This site describes and explains the use of students projects and their assessment capabilities and purposes. It lists a few student projects by authors and also scoring rubrics and samples of students work.
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  • This section of ARTIST contains suggestions for implementing student journals, writing assignments, and minute papers in statistics classes. Links to general references for writing assessments are included.
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  • This collection of free, interactive Java applets provides a graphical interface for studying the power of the most commonly encountered experimental designs. Intended to be useful in planning statistical studies, these applets cover confidence intervals for means or proportions, one and two sample hypothesis tests for means or proportions, linear regression, balanced ANOVA designs, and tests of multiple correlation, Chi-square, and Poisson. Each applet opens in its own window with sliders, which are convertible to number-entry fields, for manipulating associated parameters. Controlling for the other parameters, users can change sample size, standard deviation, type I error (alpha) and effect size one at a time to see how each affects power. Conversely, users can manipulate the power for the test to determine the necessary sample size or margin of error. Additional features include a graph option by which the program plots a dependent variable (i.e. power) over a range of parameter values; the graph is automatically updated as the parameters are changed. Each dialog window also offers a Help menu which provides instructions for using the applet. The applets can be used over the Internet or downloaded onto the user's own computer.
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  • One of the goals for the development of the Electronic Encyclopedia of Statistical Examples and Exercises (EESEE) was to provide a wide variety of timely, real examples with real data for use in statistics classes. With each story in EESEE, several thought provoking questions were designed to make students think carefully about statistical issues raised by these applications.
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  • Comparison of important aspects of five statistical analysis programs with helpful summary characteristic tables for those interested in finding the best program for their personal use.
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  • This site is an index of modules which cover probability and statistics topics including basic probability, random variables, moments, distributions, data analysis including regression, moving averages, exponential smoothing, and clustering.
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