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R: Data Analysis and Visualization by Ágnes Vidovics-Dancs, Kata Váradi, Tamás Vadász, Ágnes Tuza, Balázs Árpád Szucs, Julia Molnár, Péter Medvegyev, Balázs Márkus, István Margitai, Péter Juhász, Dániel Havran, Gergely Gabler, Barbara Dömötör, Gergely Daróczi, Ádám Banai, Milán Badics, Ferenc Illés, Edina Berlinger, Bater Makhabel, Hrishi V. Mittal, Jaynal Abedin, Brett Lantz, Tony Fischetti

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Linear regression diagnostics

I would be negligent if I failed to mention the boring but very critical topic of the assumptions of linear models, and how to detect violations of those assumptions. Just like the assumptions of the hypothesis tests in Chapter 6, Testing Hypotheses linear regression has its own set of assumptions, the violation of which jeopardize the accuracy of our model—and any inferences derived from it—to varying degrees. The checks and tests that ensure these assumptions are met are called diagnostics.

There are five major assumptions of linear regression:

  • That the errors (residuals) are normally distributed with a mean of 0
  • That the error terms are uncorrelated
  • That the errors have a constant variance
  • That the effect of the independent ...

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