Answer:
<A≅<Z
Step-by-step explanation:
Hope This Helps!
Answer: continuous random variable.
Step-by-step explanation:
A discrete random variable is defined as a random variable which consists of countable number. Examples include numbers of shoes, number of sales etc.
A continuous random variable is a random variable whereby the data can take several values. It is a random variable that takes time into consideration.
Therefore, the amount of time six randomly selected volleyball players play during a game will be a continuous random variable since time so involved.
Answer:
1,200 total on truck
Step-by-step explanation:
48=4% loaded on a truck and they have 96% left to load
So set up a proportion and it should look like this
48 over x = 4 over 100
48/x=4/100
Do cross multiplication 100×48= 4,800÷ 4 = 1,200 total on truck
=1,200 total on truck
Hope this helps, have a nice day! :D
Hi there what you need is lagrange multipliers for constrained minimisation. It works like this,
V(X)=α2σ2X¯1+β2\sigma2X¯2
Now we want to minimise this subject to α+β=1 or α−β−1=0.
We proceed by writing a function of alpha and beta (the paramters you want to change to minimse the variance of X, but we also introduce another parameter that multiplies the sum to zero constraint. Thus we want to minimise
f(α,β,λ)=α2σ2X¯1+β2σ2X¯2+λ(\alpha−β−1).
We partially differentiate this function w.r.t each parameter and set each partial derivative equal to zero. This gives;
∂f∂α=2ασ2X¯1+λ=0
∂f∂β=2βσ2X¯2+λ=0
∂f∂λ=α+β−1=0
Setting the first two partial derivatives equal we get
α=βσ2X¯2σ2X¯1
Substituting 1−α into this expression for beta and re-arranging for alpha gives the result for alpha. Repeating the same steps but isolating beta gives the beta result.
Lagrange multipliers and constrained minimisation crop up often in stats problems. I hope this helps!And gosh that was a lot to type!xd
31 and 37 are the only prime numbers between 30 and 40.
