Out-of-class

  • This online application allows the user to import data from online resources such as Facebook, Google Analytics, GitHub, as well as spreadsheets on their own computers. They can then drag-and-drop variables to make graphs automatically. The basic version is free, but you can upgrade to a paid version which allows combining data across services and, if the data come from an online resource, the user has the choice to have Data Hub keep the graphs updated as the data changes.
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  • This online booklet comes out of the Mosaic project. It is a guide aimed at students in an introductory statistics class. After a chapter on getting started, the chapters are grouped around what kind of variable is being analyzed. One quantitative variable; one categorical variable; two quantitative variables; two categorical variables; quantitative response, categorical predictor; categorical response, quantitative predictor; and survival time outcomes.
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  • This site shows the code you would use to replicate the examples in Applied Survival Analysis, by Hosmer and Lemeshow. It has code in Stata, R, and SAS.
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  • This site has the data and shows the code you would use to replicate the examples in Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence, by Judith D. Singer and John B. Willett. It has code in SAS, R, Stata, SPSS, HLM, MLwiN, and Mplus.
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  • This site is an interactive, online tutorial for R. It asks the user to type in commands at an R prompt, which are then evaluated. Typing the right thing allows the user to continue on, typing the wrong thing yields an error. The user cannot skip the easier lessons. Lessons are: Using R; Vectors; Matrices; Summary Statistics; Factors; Data Frames; Real-World Data; and What’s Next.
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  • This is an e-book tutorial for R. It is organized according to the topics usually taught in an Introductory Statistics course. Topics include: Qualitative Data; Quantitative Data; Numerical Measures; Probability Distributions; Interval Estimation; Hypothesis Testing; Type II Error; Inference about Two Populations; Goodness of Fit; Analysis of Variance; Non-parametric methods; Linear Regression; and Logistic Regression.
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  • The Comprehensive Epidemiologic Data Resource is a collection of data sets. It includes definitions of each variable in the data set. It requires a login to retrieve the data sets. Registering involves giving your name and address and the name of the study and a detailed description of the intended use of the data.
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  • This complete lesson plan, which includes assessments, is based upon a data set partially discussed in the article "Female Hurricanes are Deadlier than Male Hurricanes." The data set contains archival data on actual fatalities caused by hurricanes in the United States between 1950 and 2012. Students analyze and explore this hurricane data in order to formulate a question, design and implement a plan to collect data, analyze the data by measures and graphs, and interpret the results in the context of the original question.
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  • A statistical analysis, properly conducted, is a delicate dissection of uncertainties, a surgery of suppositions. is a quote from Statistician Michael J. Moroney (1940 - ). The quote appears in his 1951 book "Facts from Figures".
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  • This is a song suitable for middle school level statistics in reinforcing key elements of the scientific method. College-level use might include playing before a lecture to lighten the mood while setting up. The song's lyrics and music were composed by Jeff Hall audio file is a performance by the scientific jam band (see www.scientificjam.com/scijam2.htm)
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