Sacramento Kings today......

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KVB

KVB

Joined
Apr 11, 2023
Messages
8,712
Don't mush the Kings, Illini.

Don't mush the Kings.

We now about you a Landers, the ulitmate musher.

So I say again, don't mush the Kings.

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Jrgum3

Jrgum3

Joined
Oct 18, 2021
Messages
3,536
Everyone on them
I'm not on them. I flip flopped on this game and if the Kings get me again it wouldn't be the first time this year and nor will it be the last time. I can't get them right because they're inconsistent but I'm on the Pelicans here because I think they're deeper than the Kings and will find a way to get it done against a Kings team that is largely inconsistent.
 
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flyingillini

flyingillini

Joined
Jul 25, 2022
Messages
10,623
I took a little Kings +9.5 (-105) LIVE so I don't think we're done here.

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You learned this in the 10th grade KVbster,
The variance (σ2), is defined as the sum of the squared distances of each term in the distribution from the mean (μ), divided by the number of terms in the distribution (N). You take the sum of the squares of the terms in the distribution, and divide by the number of terms in the distribution (N).
 

KVB

KVB

Joined
Apr 11, 2023
Messages
8,712
You learned this in the 10th grade KVbster,
The variance (σ2), is defined as the sum of the squared distances of each term in the distribution from the mean (μ), divided by the number of terms in the distribution (N). You take the sum of the squares of the terms in the distribution, and divide by the number of terms in the distribution (N).

binomial_distribution_formula.png
 

flyingillini

flyingillini

Joined
Jul 25, 2022
Messages
10,623
Variance is the average squared deviations from the mean, while standard deviation is the square root of this number. Both measures reflect variability in a distribution, but their units differ: Standard deviation is expressed in the same units as the original values (e.g., minutes or meters).
 

flyingillini

flyingillini

Joined
Jul 25, 2022
Messages
10,623
In statistics, variance measures variability from the average or mean. It is calculated by taking the differences between each number in the data set and the mean, then squaring the differences to make them positive, and finally dividing the sum of the squares by the number of values in the data set.
 
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