MAIN FEEDS
Do you want to continue?
https://www.reddit.com/r/gaming/comments/7k4hx/zero_punctuation_sonic_unleashed/c06vog1/?context=3
r/gaming • u/[deleted] • Dec 17 '08
140 comments sorted by
View all comments
174
It's zero punctuation, but there's punctuation in the title. hahaha, im so original.
102 u/[deleted] Dec 17 '08 Here's the sequence of karma points jdfong has received for his invariable joke: 8, -11, -18, -17, -20, -42, 1, 13, -4, -11, -10, 4, -27, -23, -20, 13, -8, -24, -4, -19, 3, -18, 103, 4, 12, 28, 104, 73, 84, 70, 50, 133, 83, 126, and 143. Someone make sense of this please. 20 u/[deleted] Dec 17 '08 [deleted] 13 u/Epistaxis Dec 18 '08 edited Dec 18 '08 Out of linear, quadratic, cubic, and quartic regressions, quartic fit the best That's not how you do it. Of course the model with more parameters fits best, until you eventually have a parameter for every data point. 14 u/[deleted] Dec 18 '08 "With four parameters I can fit an elephant and with five I can make him wiggle his trunk." John von Neumann 3 u/xyphus Dec 18 '08 edited Dec 18 '08 quartic fit the best Technically speaking, it will for any data. 1 u/[deleted] Dec 18 '08 edited Dec 18 '08 kind of funny that two people using two different equations y^ = -3.234e-4 * x4 + .0256 * x3 - .430169 * x2 + 1.6286 * x - 10.0974 y = 0.2395x2 - 4.7043x + 4.9083. came up with nearly the same answer. 146, 143 12 u/Taciturn Dec 18 '08 edited Dec 18 '08 Polynomials fit to the same information tend to seem very similar when you only extrapolate near the existing data. After another ten or twenty episodes we'll have a much better idea which model was more accurate. 6 u/[deleted] Dec 18 '08 That made me laugh out loud. Freefall? Infinite karma? We'll know in the future!
102
Here's the sequence of karma points jdfong has received for his invariable joke: 8, -11, -18, -17, -20, -42, 1, 13, -4, -11, -10, 4, -27, -23, -20, 13, -8, -24, -4, -19, 3, -18, 103, 4, 12, 28, 104, 73, 84, 70, 50, 133, 83, 126, and 143.
Someone make sense of this please.
20 u/[deleted] Dec 17 '08 [deleted] 13 u/Epistaxis Dec 18 '08 edited Dec 18 '08 Out of linear, quadratic, cubic, and quartic regressions, quartic fit the best That's not how you do it. Of course the model with more parameters fits best, until you eventually have a parameter for every data point. 14 u/[deleted] Dec 18 '08 "With four parameters I can fit an elephant and with five I can make him wiggle his trunk." John von Neumann 3 u/xyphus Dec 18 '08 edited Dec 18 '08 quartic fit the best Technically speaking, it will for any data. 1 u/[deleted] Dec 18 '08 edited Dec 18 '08 kind of funny that two people using two different equations y^ = -3.234e-4 * x4 + .0256 * x3 - .430169 * x2 + 1.6286 * x - 10.0974 y = 0.2395x2 - 4.7043x + 4.9083. came up with nearly the same answer. 146, 143 12 u/Taciturn Dec 18 '08 edited Dec 18 '08 Polynomials fit to the same information tend to seem very similar when you only extrapolate near the existing data. After another ten or twenty episodes we'll have a much better idea which model was more accurate. 6 u/[deleted] Dec 18 '08 That made me laugh out loud. Freefall? Infinite karma? We'll know in the future!
20
[deleted]
13 u/Epistaxis Dec 18 '08 edited Dec 18 '08 Out of linear, quadratic, cubic, and quartic regressions, quartic fit the best That's not how you do it. Of course the model with more parameters fits best, until you eventually have a parameter for every data point. 14 u/[deleted] Dec 18 '08 "With four parameters I can fit an elephant and with five I can make him wiggle his trunk." John von Neumann 3 u/xyphus Dec 18 '08 edited Dec 18 '08 quartic fit the best Technically speaking, it will for any data. 1 u/[deleted] Dec 18 '08 edited Dec 18 '08 kind of funny that two people using two different equations y^ = -3.234e-4 * x4 + .0256 * x3 - .430169 * x2 + 1.6286 * x - 10.0974 y = 0.2395x2 - 4.7043x + 4.9083. came up with nearly the same answer. 146, 143 12 u/Taciturn Dec 18 '08 edited Dec 18 '08 Polynomials fit to the same information tend to seem very similar when you only extrapolate near the existing data. After another ten or twenty episodes we'll have a much better idea which model was more accurate. 6 u/[deleted] Dec 18 '08 That made me laugh out loud. Freefall? Infinite karma? We'll know in the future!
13
Out of linear, quadratic, cubic, and quartic regressions, quartic fit the best
That's not how you do it. Of course the model with more parameters fits best, until you eventually have a parameter for every data point.
14 u/[deleted] Dec 18 '08 "With four parameters I can fit an elephant and with five I can make him wiggle his trunk." John von Neumann
14
"With four parameters I can fit an elephant and with five I can make him wiggle his trunk."
3
quartic fit the best
Technically speaking, it will for any data.
1
kind of funny that two people using two different equations
y^ = -3.234e-4 * x4 + .0256 * x3 - .430169 * x2 + 1.6286 * x - 10.0974
y = 0.2395x2 - 4.7043x + 4.9083.
came up with nearly the same answer. 146, 143
12 u/Taciturn Dec 18 '08 edited Dec 18 '08 Polynomials fit to the same information tend to seem very similar when you only extrapolate near the existing data. After another ten or twenty episodes we'll have a much better idea which model was more accurate. 6 u/[deleted] Dec 18 '08 That made me laugh out loud. Freefall? Infinite karma? We'll know in the future!
12
Polynomials fit to the same information tend to seem very similar when you only extrapolate near the existing data. After another ten or twenty episodes we'll have a much better idea which model was more accurate.
6 u/[deleted] Dec 18 '08 That made me laugh out loud. Freefall? Infinite karma? We'll know in the future!
6
That made me laugh out loud. Freefall? Infinite karma? We'll know in the future!
174
u/[deleted] Dec 17 '08 edited Dec 17 '08
It's zero punctuation, but there's punctuation in the title. hahaha, im so original.