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 ( pixi )
where pi is probability of xi and Σ pi = 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(x4) - M4(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 )