Calculate Expected Value , Variance Standard Deviation(S.D)
Feed Numbers(N1)
FeedProbabilities
Total
Mean ( M1 )
Variance(D1)
S.D(sd1)
Feed Numbers
(N2)
FeedProbabilities
Total
Mean ( M2 )
Variance(D2)
S.D(sd2)
Feed Numbers
(N3)
FeedProbabilities
Total
Mean ( M3 )
Variance(D3)
S.D(sd3)
Use the formulae- variance = Mean ( x²) - (Mean x )² standard deviation- sd =square root of variance Mean = Σ ( p
i
x
i
) where p
i
is probability of x
i
and Σ p
i
= 1 If c is a constant , then following formula hold good and can be tested above- M ( x + c ) = M ( x ) + c M ( c * x ) = c*M( x ) D ( x + c ) = D ( x ) D ( c * x ) = c²D ( x ) If x and y are 2 independent random variables , then following formulae hold good: When y = cx where c is a constant D ( x + y ) = D ( x ) + D ( y ) + 2cD ( x ) When y = x + c where c is a constant D ( x + y ) = 2[ D ( x ) + D ( y ) ] = 4D (x) = 4D(y) as D(x) =D(y) Where x and y are completely random with respect to number of entries and corelation D ( x + y ) = D ( x ) + D ( y ) If D (x+y) != D(x) + D(y).. difference + or - gives indication of randomness D(x²) =[ M(x
4
) - M
4
(x) ] - D²(x) - 2M²(x)*D(x) D ( x * y ) = ? If y = x + c where c is a constant, D(x*y) = D( x + c / 2 )² If y =c*x then D(x*y) = c²D(x²) M ( x + y ) = M ( x ) + M ( y ) M ( x * y ) = M ( x ) * M ( y )