Linear Models

  • Find the best linear fit for a given set of data points and residuals (or let this app show you how it is done).

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  • Adjust regression parameters to bend and shift a two-dimensional polynomial surface.

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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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  • The goal of this text is to provide a broad set of topics and methods that will give students a solid foundation in understanding how to make decisions with data. This text presents workbook-style, project-based material that emphasizes real world applications and conceptual understanding. Each chapter contains:

    • An introductory case study focusing on a particular statistical method in order to encourage students to experience data analysis as it is actually practiced.
    • A guided research project that walks students through the entire process of data analysis, reinforcing statistical thinking and conceptual understanding.
    • Optional extended activities that provide more in-depth coverage in diverse contexts and theoretical backgrounds. These sections are particularly useful for more advanced courses that discuss the material in more detail. Some Advanced Lab sections that require a stronger background in mathematics are clearly marked throughout the text.
    • Data sets from multiple disciplines and software instructions for Minitab and R.

    The text is highly adaptable in that the various chapters/parts can be taken out of order or even skipped to customize the course to your audience. Depending on the level of in-class active learning, group work, and discussion that you prefer in your course, some of this work might occur during class time and some outside of class. 

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  • A song for use in helping students to recognize, in context, the idea of ANOVA as comparing variance between groups to variance within groups.  Music & Lyrics © 2016 by Monty Harper.  This song is part of an NSF-funded library of interactive songs that involved students creating responses to prompts that are then included in the lyrics (see www.causeweb.org/smiles for the interactive version of the song, a short reading covering the topic, and an assessment item).

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  • The Journal of Statistics Education provides a collection of Java applets and excel spreadsheets (and the articles associated with them) from as early as 1998 on this webpage.

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  • This collection of data can be used for many useful statistical analyses. Data and description are in a separate file and useful for SAS data analysis too. Data are categorized by analysis type, hence easy to pic relevant data sets accordingly. The data can be used for many analysis such as, Categorical data analysis, Polynomial Linear, Nonlinear, Logistic, Poisson, Negative Binomial Regression analysis, Response Surface Regression, Binary Response Regression, Time Series Data,1-Way ANOVA/ Independent Samples t-test, Multi-Factor ANOVA, and many other data analysis.
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  • Many data sets useful for modeling bivariate relationships. The data sets are formatted for use in Fathom, but text versions are also available.
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  • A joke that might be used in a discussion of the problem of using a simple linear regression to extrapolate beyond the range of the data (where it is unlikely that the linear relationship would continue to hold). The joke was written by Dennis Pearl from Penn State University.
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  • A video for use in teaching about the dangers of extrapolating well beyond the range of the data in linear regression. The lyrics and Powerpoint components of the video were written by Michael Posner while the vocals were done by Reena Freedman of Villanova University and won first place in the video category of the 2017 A-mu-sing contest. The lyrics parody the song "How Far I'll Go" from the Disney animated feature film Moana (sung by Alessia Cara for the movie soundtrack).
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