A Modern Approach to Regression with R

By R. Gould. 2010

681 citations recorded by the source corpus.

I have taught an undergraduate applied regression course for several years, and found few textbooks that are appropriate for modern students. To reveal my prejudices up front, what I want in an applied regression text are data, graphics, and computation. Data, because an applied regression course should give students an approach for understanding the world as it is revealed through data; the regression model is an end to a means but should not be treated as the end itself. Graphics, because not only are they important to modern regression, but they can help students develop a conceptual understanding of regression models. Computation, because data analysis via graphical and numerical regression is not possible without statistical software. The choice of software not only determines the sort of diagnostics and visualizations available to one’s students, but also influences how students understand regression itself.

Subfields: Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/7f1b0412092943b17c7d78233d27ca631c0ca2d5

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In Control and Systems Engineering

Citations in the corpus

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Fitting Models to Data: Residual Analysis, a Primer (2017, background): “The standardized residuals are normally distributed with a mean value of zero and (approximately) unity variance [10, 19]”