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  • This self-test provides a review/assessment of the Probability section of this module. At the bottom, there is a grading button to rate the users' understanding of the material.
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  • This tutorial on Multiple Regression helps students understand the definition, use the standard error of estimate, use rank correlation, and solve exercise problems using multiple regression.
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  • This interactive tutorial on Linear Regression helps the user understand the definition of linear regression, understand the meaning of correlation, use scatter plots, recognize and calculate errors in linear regression, use simple linear regression analysis, use residual analysis of the regression equation, understand the significance of the correlation coefficient and the regression coefficient in linear regression, and solve exercise problems using linear regression.
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  • This is an interactive tutorial on Data Analysis topics including representations of data, understand the definition of mean, understand the definition of variance, recognize a few other useful concepts, recognize various sampling techniques, and solve exercise problems using data analysis.
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  • This tutorial includes using, finding, weighting, and solving problems with Moving Averages.
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  • This interactive tutorial on Exponential Smoothing helps learners understand the use of exponential smoothing, define exponential smoothing, cite the merits and demerits of exponential smoothing, and solve exercise problems using exponential smoothing.
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  • This interactive module helps students to understand the definition of and uses for clustering algorithms. Students will learn to categorize the types of clustering algorithms, to use the minimal spanning tree and the k-means clustering algorithm, and to solve exercise problems using clustering algorithms.
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  • This module is a short quiz which gives a review/assessment of the main concepts for this refresher course. At the bottom, there is a grading button to rate the understanding of the material.
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  • This free online video program "shows how to improve the accuracy of a survey by using stratified random sampling and how to avoid sampling errors such as bias. While surveys are becoming increasingly important tools in shaping public policy, a 1936 Gallup poll provides a striking illustration of the perils of undercoverage."
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  • In this free online video program, "students will learn the distinction between deterministic phenomena and random sampling. This program introduces the concepts of sample space, events, and outcomes, and demonstrates how to use them to create a probability model. A discussion of statistician Persi Diaconis's work with probability theory covers many of the central ideas about randomness and probability."
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