R Is Not So Hard! A Tutorial, Part 4 (repost)

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The following is not a Stats Make Me Cry original, but rather something I came across and found very useful. The article demonstrates how to examine non-linear effects (e.g. quadratic effects) using a regression model in R. If you are interested in the topic, please read the preview and follow the link that follows to the original site.

Part 3 we used the lm() command to perform least squares regressions. In Part 4 we will look at more advanced aspects of regression models and see what R has to offer. One way of checking for non-linearity in your data is to fit a polynomial model and check whether the polynomial model fits the data better than a linear model. Or you may wish to fit a quadratic or higher model because you have reason to believe that the relationship between the variables is inherently polynomial in nature...

Read the rest of R Is Not So Hard! A Tutorial, Part 4 here…

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Jeremy J. Taylor

Stats Make Me Cry is owned and operated by Jeremy J. Taylor, Ph.D. Jeremy completed his doctoral training in Clinical Psychology at DePaul University and completed his pre-doctoral internship at the Kennedy Krieger Institute, Johns Hopkins School of Medicine. He is currently a Senior Research Associate at the Collaborative for Academic, Social, and Emotional Learning. Although Jeremy's background is in Psychology, he consulted on dissertations for more than 100 students, from 13 countries, and from a variety of disciplines.