Hearted Youtube comments on Memeable Data (@memeabledata) channel.
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I really love your style (explanations, animations, visualization, sounds, letting viewers guess and your progress through the data/video). Beautiful 3D analysis! Please keep on like that! As addition to your analysis: It may be possible to get cheap vegan protein, but you also have to consider the sheer amount of mass you would have to eat. Nobody can eat, let's say, 5kg/day of rice/potatoes/... just to get enough proteins. Even 1 kg beans that get heavier when bioled in water. And protein shakes/powder with concentrated (vegan) proteins (with a human-friendly protein profile of mixed vegan sources) could be taken into consideration. In my own calculation, cheaper protein powder (500g with ~70% protein for 10€) is very cheap in comparison to eggs, cheese, ham and stuff.
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As a Russian, I really want to make many notes about this, but I just want to note at 2:24 that Ukrainian population in 2021 is not 43 millions. At the World Bank's page that the video refers to, there is no significant population losses for Ukraine in 2013-2014, which have to be. Only Crimea has population around 2.5 million men, and DPR and LPR also have population of <4 million. So, Ukrainian population must be significantly lower.
There may be some arguments that "it's actually Ukrainian, your referendums are fake and illegal" etc., but if these 6 million people have Russian passport, obey Russian laws, pay taxes to Russian budget, speak Russian language, if some of them are serving in Russian army, if their homes have risk of being destroyed by Ukrainian weapons, they are probably part of Russian population, not Ukrainian one. Or they need to be counted separately at least. Pretty much the same for Kherson and Zaporozhie regions btw.
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@memeabledata The probability distribution would be f(x) = e^(ax+b), or equivalently f(x) = c·e^(ax) with c = e^b. It has two degrees of freedom, a and c, so you need two constraints. The first constraint is that it integrates to 1 over the domain (the attractiveness, between 0 and 1). Since the antiderivative is c/a e^(ax), that gives 1 = c/a·(e^a - 1). The second constraint is that the expected value is a given value E, so x·f(x) integrates to E. Since the anti-derivative is c/a² · (ax-1) · e^(ax), that gives E = c/a² · ( (a-1) · e^a + 1 ).
c as function of a follows from the first constraint: c = a·(e^a - 1). Plug that into the second constraint, then a is one of the roots of the function F(x) = (e^x - 1)/x · ( (x-1) · e^x + 1) - E, such that F(a) = 0. You can find this numerically with a graphing calculator like Desmos. Now calculate c from that, and you have the model.
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