Ratio & YaNePon
So I just found this meme that says the best cereal is the one with the most emojis in its name. Want to build a spreadsheet to test that hypothesis? Let's crunch some numbers and see if itās memeātrue or just a weird glitch.
Sure, start with a list of cereal names, add a column that counts the emojis in each name, then assign a popularity metricāsales, ratings, or your own taste score. Compute a weighted score (emoji count Ć popularity weight), run a simple linear regression or correlation to see if the emoji count predicts popularity. Donāt forget to check for outliers like āCerealāwithānoāemojiā that might skew the data. If the R² is low, the meme was a glitch; if itās high, youāve got a statistical champion.
Okay, so first step: grab a list of cerealsāthink Froot Loops, Frosted Flakes, Lucky Charms, Cinnamon Toast Crunch, maybe even the new āMemeāMunchā that has a š¦ in the logo. Next, for each name, Iāll count the emojis, but Iāll also doubleācheck because Iām a bit late on the punchline; like if you see a š¦, you might think itās just a unicorn, but maybe itās a cryptic meme symbol for āgolden opportunity.ā Then Iāll pick a popularity metric; sales sounds boring, so Iāll just rate them on a scale of ātastes like a snackā to āwill haunt me at night.ā Then multiply the emoji count by the taste score to get a weighted score. After that, Iāll toss them into a linear regressionāthough Iām pretty sure the R² will be ālike 0.01ā because obviously the universe doesnāt care about unicorns. But hey, if itās high, weāve discovered the algorithm behind the meme craze. If itās low, we just prove the meme was a glitch, and I can post a new meme about the glitch. Sound good, or should I start with a snack?
Thatās a solid planājust remember to normalize the taste score first, otherwise a 10āpoint scale will dominate the emoji count. Also track the standard deviation of the emoji counts; if most cereals have zero or one, the variance will be low and R² will naturally be small. If you do find a spike for something like āMemeāMunch š¦ā, note it as an outlier and run the regression with and without it. The key metric will be the coefficient for the emoji count; if itās statistically significant (pāÆ<āÆ0.05), youāve got a memeātruth. Otherwise, go back to the drawing board or just share the meme anyway.
Got it, so youāre saying weāll do the math, but first we gotta convert the taste scale so that 1 and 10 are on the same page, because otherwise the emoji count is like a tiny emoji on a 10āpoint mountain. Then weāll look at how spread out the emojis are, maybe most cereals are just plain, no emojis, like zero or one, so the standard deviation will be like a tiny bubble, and thatāll make the R² look like a ghostly number. If āMemeāMunch š¦ā shows up, weāll flag it as the wild card, run the regression with it, then run it again without it, just to see if itās pulling the curve like a prankster. And if the coefficient for emoji count is a real number that passes the pāÆ<āÆ0.05 test, weāll claim the meme is legit; otherwise weāll just share the meme anyway because thatās what we do with all of this data. Ready to pull up the spreadsheet?
Sounds good, letās pull the data into a table: cereal name, emoji count, normalized taste score, weighted score. Then run the regression, check R², pāvalue, and flag the unicorn entry. Iāll keep the output minimal and just give you the numbers so you can decide what to do next. Ready to open the sheet.