Virgin Non-Parametric Regression vs Chad Linear Regression

The virgin non-parametric regression Requires a tonne of observations for convergence Heavily depends on hyper-parameters that must be chosen optimally Choice of hyper-parameters needs orders of magnitude more computational power Does not have a closed-form solution Terribly inefficient, high-level implementations are slower than Crysis on Pentium III Dies a horrible death with 10 regressors from the curse of dimensionality Conditions yielding normality of the estimator are hard to verify Fails miserably in regions of low density of explanatory variables Convergence so poor, has to be injected with parametric assumptions or index restrictions Even bootstrapped confidence intervals are not trusted at first glance Computationally intensive, makes 2019 computers bleed Does not have a clear notion of efficient estimation Even after estimation, partial effects require extra computation Is rarely used in applied economic analysis Impossible to find simple instructions for inference Most courses spend hours just to get to Parzen–Rosenblatt, Nadaraya–Watson, maybe LOBESS, and then stop Implementations vary widely in software, same kernels have different scaling factors Behaves like a spoiled brat at boundaries, requires correction THE CHAD LINEAR REGRESSION With 20 data points, ends up being published in top macroeconomic journals Works in one step without fine-tuning Can be computed analytically with paper and pencil Optimised to the Moon and beyond in linear algebra libraries Can handle hundreds of regressors with ease Built in every software package that can handle numbers Could not care less about error distribution, ends up being normal anyway No one bats an eye when parametric assumptions are violated No questions asked with robust standard errors Can be made asymptotically efficient just in a couple of steps Gives a nice, clean interpretation any grandma can comprehend Constant partial effect across the entire support Inference can be done with one subtraction and one division to obtain a t-statistic Lots of online tutorials about versatile extensions accessible to beginners Gives the same consistent result in R, STATA, SAS, SPSS, Excel, and calculators Can account for arbitrary non-linearity by including functions of regressors, remains agreeable The support of explanatory variables does not matter

The meme contrasts a distressed, chaotic “virgin” figure representing non-parametric regression with a confident “Chad” figure representing linear regression. The joke exaggerates the practical complexity of non-parametric methods and the simplicity, robustness, and widespread availability of linear regression. It is used as a humorous reaction when comparing an overcomplicated approach with a familiar method that seems to solve everything effortlessly.

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