Statistical Inference & Techniques

  • 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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  • What is correct, what is incorrect, and why?
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  • A quote to initiate discussions of model building By British Statistician and Epidemiologist Hilda Mary Woods (1892-1971). The quote is from her paper "The influence of external factors on the mortality from pneumonia in childhood and later adult life" in the Journal of Hygiene 1927 pages 36-43 (quote is on page 42).
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  • A quote that can be used in discussing how data are more convincing to people if they align with current beliefs. The quote is by American mathematician Mary Gray (1938 - ) and comes from the title of her 1993 (v. 8, page 144) Statistical Science "Can Statistics Tell Us What We Do Not Want to Hear? The Case of Complex Salary Structures." The paper, the commentaries on the paper, and Dr. Gray’s rejoinder to the commentaries include discussions of many statistical issues and is very approachable for undergraduate students.
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  • A quote to use in discussions of the value of meta-analyses. The quote is by American Biostatistician Olive Dunn (1915-2008) from chapter 1 of her 1977 textbook Basic Statistics: A Primer for the Biomedical Sciences. Note that one aspect of meta-analysis is the selection and weighting of different studies to be included in the overall analysis. Thus, this quote might be most relevant to stimulate a discussion of that aspect.
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  • A cartoon to help students learn not to "accept" the null hypothesis. The cartoon was drawn by British cartoonist John Landers (www.landers.co.uk) based on an idea in 2016 from Dennis Pearl from Penn State University.
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