Jackknife and linear regression in Excel: implementation and comparison

Even though standard regression seems to be performing much better, predictions for individual salary - regression versus Jackknife - are not far off, as illustrated in the top figure. Both for regression and Jackknife, only 8 different estimated values are generated, since we have just 8 codes. Note that if we boost correlations to the point that Correl(Python, R) 1, then the linear regression model will crash, while the Jackknife will perform nicely. Rudimentary, approximate methods such as Jackknife regression (not to be confused with Efron's bootstrap) are just nearly as good as so-called exact models such as traditional regression, for predictive modeling. The reason is because data is anything but exact, and statistical models are approximate representations of the reality: all models are wrong, some are not as wrong as others. Approximate solutions provide substantial advantages: easy to code (even in SQL) and understand, robust, and easy to interpret. In short, they are a good choice for inclusion in black-box, automated data science.