Var[a(X^2)+b] = E[ ( a(X^2) + b - (aE[X^2] + b ) )^2 ]
= E[ ( a(X^2) + b -aE[X^2] - b )^2 ]
= E[ ( a(X^2) - aE[X^2] )^2]
= E[ (a^2) ( (X^2) -E[X^2] )^2 ]
= E[ ( (X^2) - E[X^2] ) ( (X^2) - E[X^2] ) ]
= (a^2) E[ (X^4) -2(X^2)E[X^2] + ( E[X^2] )^2]
= (a^2) { E[X^4] -2E[X^2]E[X^2] + ( E[X^2] )^2 }
= (a^2) { E[X^4] -2( E[X^2] )^2 + ( E[X^2] )^2 }
= (a^2) { E[X^4] - ( E[X^2] )^2 }
= (a^2) Var[X^2]
*however, we still do not know what Var[X^2] is....
Kenetic energy equals one half mass times velocity squared.
Equal Variance
9m² or 9 square meters or 9 meters squared
price and quantity variance
The formula for this goes like this: radius of the pipe squared (32) x pi (3.1416) x length of pipe (12) = volume (amount of water). So 32 x 3.1416 x 12 = 339.2928 or about 339.3 cubic inches.
Variance is the squared deviation from the mean. (X bar - X data)^2
The standard deviation is defined as the square root of the variance, so the variance is the same as the squared standard deviation.
The variance.
Since Variance is the average of the squared distanced from the mean, Variance must be a non negative number.
Variance is std dev squared. Therefore, if std dev = 12.4, variance = 12.4^2 = 153.76.
The derivative of the moment generating function is the expectation. The variance is the second derivative of the moment generation, E(x^2), minus the expectation squared, (E(x))^2. ie var(x)=E(x^2)-(E(x))^2 :)
Given a set of n scores, the variance is sum of the squared deviation divided by n or n-1. We divide by n for the population and n-1 for the sample.
Variance
the small greek letter sigma squared.
13.1 squared = 3.62
Variance
No. Cos squared x is not the same as cos x squared. Cos squared x means cos (x) times cos (x) Cos x squared means cos (x squared)