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Data Collection

  • The goal of WISE is to provide students and teachers of statistics easy access to a wide range of resources that are freely available on the internet. We invite you to explore our website and enjoy many wonderful statistical materials from around the world.

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  • This text was written for an introductory class in Statistics suitable for students in Business, Communications, Economics, Psychology, Social Science, or liberal arts; that is, this is the first and last class in Statistics for most students who take it. It also covers logic and reasoning at a level suitable for a general education course.  SticiGui provides text, interactive tools, lecture videos, sample exam reviews, and more for a course in basic statistical concepts.  

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  • A joke to be used in discussing the role and tools of epidemiology in studying infectious diseases.  The joke was written in 2018 by Larry Lesser from The University of Texas at El Paso.

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  • These cheat sheets make it easy to learn about and use some of the favorite packages of RStudio. 

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  • RStudio is a set of integrated tools designed to help you be more productive with R. It includes a console, syntax-highlighting editor that supports direct code execution, and a variety of robust tools for plotting, viewing history, debugging and managing your workspace. 

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  • The Cornell Statistical Consulting Unit distributes a periodic electronic newsletter, StatNews. This newsletter discusses applications of statistics that are commonly encountered in research and teaching.

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  • Which is more robust against outliers: mean or median?  This app demonstrates the (in)stability of these descriptive statistics as the value of an outlier and the number of data points change.

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  • The app allows you to see the trade-offs on various types of outlier/anomaly detection algorithms. Outliers are marked with a star and cluster centers with an X.

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  • Use presets or change parameter values manually to explore the cost-effectiveness of different research approaches to unearth true scientific discoveries. For detailed explanation and conceptual background, see LeBel, Campbell, & Loving (in press, JPSP), Table 3. This app is an extension of Zehetleitner and Felix Schönbrodt's (2016) positive predictive value app

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  • This app allows you to derive an approximation to the difference in Bayesian information criterion and to the probability of the null and the alternative hypothesis from the sum of squares obtained in an ANOVA analysis.

    Required input

    • Number of participants
    • Df ... degrees of freedom of the effect of interest
    • Whether the effect is between or within participants
    • SSEffect ... sum of squares of the effect of interest
    • SSError ... sum of squares of the error, for within-factors the by-subject error, associated with this effect
    • SSTotal ... total sum of squares, only required for within-participant designs when using effective sample size (strongly recommended, Nathoo & Masson, 2007)
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