We will discover that everything we expect to happen, does happen. When variables
are rescaled, the coefficients, standard errors, confidence intervals, t statistics, and F
statistics change in ways that preserve all measured effects and testing outcomes. While
this is no great surprise—in fact, we would be very worried if it were not the case—it
is useful to see what occurs explicitly. Often, data scaling is used for cosmetic purposes,
such as to reduce the number of zeros after a decimal point in an estimated coefficient.
By judiciously choosing units of measurement, we can improve the appearance of an
estimated equation while changing nothing that is essential