ABSTRACT
In this chapter, the author starts by plotting the original linear fit to the data and experiment with adding differing amounts of error to the predictors. He explores transformations and diagnostics for these data, but his focuses on just the measurement error issue. The author investigates the effect of adding measurement error to the predictor. The accuracy of the prediction depends on where the prediction is to be made. The greater the distance is from the observed data, the more unstable the prediction. This means that predictions tend to be greater extrapolations than with data that are closer to the orthogonality.
