It is important to check both conditions
np ≥ 5
nq = n(1 - p) ≥ 5
when you try to approximate a binomial distribution with a normal distribution.
If these two conditions are verified, it means that the sample is "big enough" to use the approximation, otherwise, the normal distribution does not fit well enough the binomial distribution and the values of mean, etc, could be different.
Therefore if those conditions are not verified, the normal distribution is not suitable to represent the binomial distribution.
Answer:
1200
Step-by-step explanation:
<h2><u><em>
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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
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Different values of b.
<h3>Answer: There is no logarithmic function whose graph goes through given points.</h3><h3 />
Maybe the second point is ![\left(\dfrac{1}{16},\ 2\right)](https://tex.z-dn.net/?f=%5Cleft%28%5Cdfrac%7B1%7D%7B16%7D%2C%5C%202%5Cright%29)
Substitute:
![\log_b\dfrac{1}{16}=2\to b^2=\dfrac{1}{16}\to b=\sqrt{\dfrac{1}{16}}\to b=\dfrac{1}{4}](https://tex.z-dn.net/?f=%5Clog_b%5Cdfrac%7B1%7D%7B16%7D%3D2%5Cto%20b%5E2%3D%5Cdfrac%7B1%7D%7B16%7D%5Cto%20b%3D%5Csqrt%7B%5Cdfrac%7B1%7D%7B16%7D%7D%5Cto%20b%3D%5Cdfrac%7B1%7D%7B4%7D)
<h3>Then we have the answer:</h3>
![\boxed{y=\log_{\frac{1}{4}}x}](https://tex.z-dn.net/?f=%5Cboxed%7By%3D%5Clog_%7B%5Cfrac%7B1%7D%7B4%7D%7Dx%7D)