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  • A cartoon to be used in discussing correlation, spurious versus causal relationships, and the meaning of residuals (humorously depicting a relationship between residual over historical enrollment levels in grad school and residual levels in the unemployment rate). The cartoon is #1078 in the web comic Piled Higher and Deeper by Panamanian cartoonist Jorge Cham (1976- ): see www.phdcomics.com/comics/archive.php?comicid=1078. Free for use in classrooms and course websites with acknowledgement (i.e. "Piled Higher and Deeper" by Jorge Cham, www.phdcomics.com)
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  • A cartoon to be used in discussing the least squares property of the regression line and the lexical ambiguity in the use of the word regression. The cartoon is #1921 in the web comic Piled Higher and Deeper by Panamanian cartoonist Jorge Cham (1976- ): see www.phdcomics.com/comics/archive.php?comicid=1921. Free for use in classrooms and course websites with acknowledgement (i.e. "Piled Higher and Deeper" by Jorge Cham, www.phdcomics.com)
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  • A cartoon to be used in discussing the interpretation of a regression equation (for example interpreting the intercept when it is well beyond the range of the data). The cartoon is #1823 in the web comic Piled Higher and Deeper by Panamanian cartoonist Jorge Cham (1976- ): see www.phdcomics.com/comics/archive.php?comicid=1823. Free for use in classrooms and course websites with acknowledgement (i.e. "Piled Higher and Deeper" by Jorge Cham, www.phdcomics.com)
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  • A cartoon to be used in discussing summary statistics (comparing means ± error bars). One aspect of part of the graph for discussion shows an error bar going below zero for a variable that should be positive. The cartoon is #1793 in the web comic Piled Higher and Deeper by Panamanian cartoonist Jorge Cham (1976- ): see www.phdcomics.com/comics/archive.php?comicid=1793. Free for use in classrooms and course websites with acknowledgement (i.e. "Piled Higher and Deeper" by Jorge Cham, www.phdcomics.com)
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  • A cartoon to be used in discussing summary statistics that juxtaposes various interesting statistics. The cartoon is #1743 in the web comic Piled Higher and Deeper by Panamanian cartoonist Jorge Cham (1976- ): see www.phdcomics.com/comics/archive.php?comicid=1743. Free for use in classrooms and course websites with acknowledgement (i.e. "Piled Higher and Deeper" by Jorge Cham, www.phdcomics.com)
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  • A cartoon suitable for use in discussing situations where the explanatory variable has essentially no predictive power (whether the variables have a statistically significant relationship or not). The cartoon is number 1725 from the webcomic series at xkcd.com created by Randall Munroe. Free to use in the classroom and on course web sites under a creative commons attribution-non-commercial 2.5 license.
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  • A quote to initiate a discussion of the fact that correlation does not imply a causal relationship (especially spurious correlations that happen by coincidence). The quote is by American novelist and poet Siri Hustvedt (1955 - ) from her 2011 novel The Summer Without Men.
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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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  • 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 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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