Regression

  • A collection of Java applets and simulations covering a range of topics (descriptive statistics, confidence intervals, regression, effect size, ANOVA, etc.).

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  • This site offers separate webpages about statistical topics relevant to those studying psychology such as research design, representing data with graphs, hypothesis testing, and many more elementary statistics concepts.  Homework problems are provided for each section.

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  • 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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  • 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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  • 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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  • 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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  • This simulation illustrates least squares regression and how the least squares solution minimizes the sum of the squared residuals. The applet demonstrates, in a visual manner, various concepts related to least squares regression. These include residuals, sum of squares, the mean line, how the line of best fit is determined, and how the line of least squares solution minimizes the sum of the squared residuals.

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  • A joke to use when teaching about choices of binary response data models like the Logistic or Probit models by University of Texas at El Paso professor of Mathematical Sciences, Lawrence Mark Lesser (1964-).

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  • ... if the difference isn't different enough to make a difference, what's the difference? is a quote by American agricultural statistician Victor Chew (1923 - ). The quote is found in his 1980 paper "Testing differences among means: correct interpretation and some alternatives" ("HortScience" pages 467-470). The quote can be used in discussions of practical significance versus statistical significance.
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