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  • This assignment has students investigate whether the risk of having a child with a low birth weight is higher when the mother drinks and smokes during pregnancy. The data set represents a random sample of 1450 births from the state of North Carolina.
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  • The assignment begins with creating a summary of and tables for the data, then walks the student through the steps of creating a hypothesis testing report. It uses the data set ncbirth200.xls, which is a random sample of 200 births from the data set ncbirth1450.xls.
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  • This outline, appropriate for an introductory statistics course, describes steps in a statistical study including identifying the question, designing a study, collecting data, analyzing data and making conclusions.
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  • Tips for helping students to take more effective notes during lecture.
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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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  • DataCounts! is an interactive website designed to help integrate social statistics into the classroom setting. Each collection contains a wide variety of datasets that can be viewed online with WebCHIP. DataCounts! also houses a collection of teaching modules that have been created by teachers across the country to integrate social science data into their classes.
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  • This site lists a set of case studies that cover regression topics, random number calculations of pi, as well as limit theorems. On the individual case study pages are the descriptions and/or instructions.
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  • This page is a guide to writing and using statistics in the field of science. It is aimed at biology students. It contains information on formatting and the use of tables as well as links to pages about frequency analysis, t-tests, and regression.
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  • This applet shows balls falling through a grid of posts to show the central limit theorem in action.
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  • This chapter of the NIST Engineering Statistics handbook presents information on the statistical modeling of an engineering process. It contains an introduction, discussion of the assumptions, information about data collection and analysis, a discussion of what can be concluded from different process models, and case studies.
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