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Drill and Practice

  • The two worksheets enable instructors to demonstrate how changes in the magnitude of the treatment effects and of the standard deviation of the error term will impact significance in a One-Way ANOVA model. The user specifies three input values that influence the simulation of random observations. ANOVA calculations are provided for the student, leaving the focus on the interpretation of the results. The mirror site (found at http://misnt.indstate.edu/cmclaren/ANOVA_Note.doc) contains an article that can serve as a teaching note to accompany the worksheets.
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  • The t-distribution activity is a student-based in-class activity to illustrate the conceptual reason for the t-distribution. Students use TI-83/84 calculators to conduct a simulation of random samples. The students calculate standard scores with both the population standard deviation and the sample standard deviation. The resulting values are pooled over the entire class to give the simulation a reasonable number of iterations. This document provides the instructor with learning objectives, context, mechanics, follow-up, and evidence from use associated with the in-class activity.
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  • This activity will allow students to familiarize themselves with technology and its use in calculating marginal, conditional, and joint distributions, as well as making conclusions from these tabular and graphical displays. The corresponding data set 'Pizza Data' is located at the following web address: http://www.causeweb.org/repository/ACT/PIZZA.TXT
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  • This activity will allow students to learn the difference between observational studies and experiments, with emphasis on the importance of cause-and-effect relationships. The activity will also familiarize students with key terms such as factors, treatments, retrospective and prospective studies, etc.
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  • This activity enables students to learn about confidence intervals and hypothesis tests for a population mean. It focuses on the t-distribution, the assumptions for using it, and graphical displays. The activity also focuses on how to interpretations a confidence interval, a p-value, and a hypothesis test.

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  • This activity provides practice for constructing confidence intervals and performing hypothesis tests. In addition, it stresses interpretation of confidence intervals and comparison and application of results in context.
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  • This activity explains the important features of a distribution: shape, center, spread, and unusual features. It also covers how to determine the difference between mean and median, and their respective measures of spread, as well as when to apply them to a particular distribution. Graphical displays such as: histograms and boxplots are also introduced in this activity. The corresponding data set for this activity is found at the following web address: http://www.causeweb.org/repository/ACT/food.txt

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  • This activity focuses on basic ideas of linear regression. It covers creating scatterplots from data, describing the association between two variables, and correlation as a measure of linear association. After this activity students will have the knowledge to create output that yields R-square, the slope and intercept, as well as their interpretations. This activity also covers some of the basics about residual analysis and the fit of the linear regression model in certain settings. The corresponding data set for this activity, 'BAC data', can be found at the following web address: http://www.causeweb.org/repository/ACT/BAC.txt

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  • This activity stresses the importance of writing clear, unbiased survey questions. It explore the types of bias present in surveys and ways to reduce these biases. In addition, the activity covers some basics of surveys: population, sample, sampling frame, and sampling method.
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  • This tutorial explains the theory and use of Student's t-test for matched pairs and demonstrates it with an example on project quality. Data is given as well as SPSS and Minitab code.
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