High School

  • This case study compares a low-fat diet to a "Mediterranean diet" to see which led to better health. Concept: Chi Square test of independence
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  • This case study aims to answer the question, "How does one select employees to perform physically demanding jobs?" It examines the relationship between isometric strength tests and job performance for 147 workers. Concepts: correlation, linear regression, multiple regression.
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  • This case study covers the following concepts: confidence intervals for proportions and the normal approximation to the binomial. It also assesses the question: "What proportion of the iMac purchasers are new computer owners?"
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  • This case study assesses the question, "Is it easier to learn to use computer software that uses natural language commands?" Concepts: analysis of covariance, adjusted means, boxplots
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  • This case study assesses the question, "Do physicians discriminate against overweight patients?" This study indicates that, at least in one respect, they do. Concepts: t-test, means, boxplots
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  • In this free online video program, "students will understand inference for simple linear regression, emphasizing slope, and prediction. This unit presents the two most important kinds of inference: inference about the slope of the population line and prediction of the response for a given x. Although the formulas are more complicated, the ideas are similar to t procedures for the mean sigma of a population."

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  • This free online video program "marks a transition in the series: from a focus on inference about the mean of a population to exploring inferences about a different kind of parameter, the proportion or percent of a population that has a certain characteristic. Students will observe the use of confidence intervals and tests for comparing proportions applied in government estimates of unemployment rates."
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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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