Statistical Model Validation Techniques in R
Instruction: Explain how to validate statistical models in R, focusing on techniques for assessing model fit and predictive accuracy.
Context: This question assesses the candidate's understanding of model validation techniques and their ability to apply these methods in R.
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Separate model assumptions, in-sample fit and predictive generalization. Residual plots can reveal nonlinearity, changing variance and influential observations; they do not establish performance on unseen data. A normality test is not a universal validation gate, particularly for non-Gaussian models.
set.seed(17) d <- data.frame(x = seq(-2, 2, length.out = 80)) d$y <- 1 + d$x + .3 * d$x^2 + rnorm(80, sd = .4) train_id <- sample(seq_len(nrow(d)), 60) train <- d[train_id, ]; test...