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

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THE VIRGIN ORGANIZED SHELF EDUCATION EDUCATION SUFRIEL JUSTICE STREAMING ON THE AUTOOT RAKIM Sullabus DIN BLACK STAR JAZZ AND JUSTICE HORNE ALL INCOMPUTE HARNEY & HOTH BLACK SIDE RIVER NES THE SOUT THE CHAD OMINOUS STACKS MARTIN

The virgin new monster school Definitely ran By greedy youtubers Crewmate There is ostor anons us The chad old monster school Definitely ran by, youtubers you don't care about money No cringe Mass cringe OUCH! Good thumbnail S----- thumbnails Uses viral s--- Doesn't use Doesn't fell like anything viral a school Felt like a school Unlikeable characters Likeable characters made with mematic

Virgin Mars Colonisation -Thinks mars will someday turn out as friendly as Earth from future """terraforming projects"""""" Lives for like 1 year on mars before dying of bone cancer Thin, weak atmosphere -Temperature comparable to a cold day in wisconsin -Will literally never happen. -Elon Musk prob fooled you into thinking this is possible for attention -Barely restraining himself from depression caused suicide Chad Venus suicide mission -Accepts venus' eternal literal manifastation of hell. Can literally swim in the 90 bar air, how cool is that -Survives for like 9 minutes max before his space suit melts and space helmet cracks opencracks -Temperature comparable to sticking your head in a pizza oven. Huge rush of adrelinine and testosterone Can easily be done, only ethics are stopping NASA

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Virgin vs. Chad - The Virgin Non-Parametric Regression vs. The Chad Linear Regression

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

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