Better Regressions Every Time

What happens if the explanatory and response variables are sorted independently before regression? Suppose we have data set (Xᵢ, Yᵢ) with n points. We want to perform a linear regression, but first we sort the Xᵢ values and the Yᵢ values independently of each other, forming data set (Xᵢ, Yⱼ). Is there any meaningful interpretation of the regression on the new data set? Does this have a name? I imagine this is a silly question so I apologize, I'm not formally trained in statistics. In my mind this completely destroys our data and the regression is meaningless. But my manager says he gets "better regressions most of the time" when he does this (here "better" means more predictive). I have a feeling he is deceiving himself. EDIT: Thank you for all of your nice and patient examples. I showed him the examples by @RUser4512 and @gung and he remains staunch. He's becoming irritated and I'm becoming exhausted. I feel crestfallen. I will probably begin looking for other jobs soon. But my manager says he gets "better regressions most of the time" when he does this

A screenshot of a statistics question is paired with Pepe looking defeated and a row of increasingly distressed crying nerd-like figures. The joke is that the manager insists on sorting the explanatory and response variables independently, creating meaningless regressions while proudly calling them “better”; the image is used as a reaction to bad analysis, self-deception, or an incompetent boss. EN: A screenshot of a statistics question is paired with Pepe looking defeated and a row of increasingly distressed crying nerd-like figures. The joke is that the manager insists on sorting the explanatory and response variables independently, creating meaningless regressions while proudly calling them “better”; the image is used as a reaction to bad analysis, self-deception, or an incompetent boss.

• @profunctor_io / 4001 2020-02-11