Answer:
(1) A Normal approximation to binomial can be applied for population 1, if n = 100.
(2) A Normal approximation to binomial can be applied for population 2, if n = 100, 50 and 40.
(3) A Normal approximation to binomial can be applied for population 2, if n = 100, 50, 40 and 20.
Explanation:
Consider a random variable X following a Binomial distribution with parameters n and p.
If the sample selected is too large and the probability of success is close to 0.50 a Normal approximation to binomial can be applied to approximate the distribution of X if the following conditions are satisfied:
The three populations has the following proportions:
p₁ = 0.10
p₂ = 0.30
p₃ = 0.50
(1)
Check the Normal approximation conditions for population 1, for all the provided n as follows:
![n_(a)p_(1)=10* 0.10=1<10\\\\n_(b)p_(1)=100* 0.10=10=10\\\\n_(c)p_(1)=50* 0.10=5<10\\\\n_(d)p_(1)=40* 0.10=4<10\\\\n_(e)p_(1)=20* 0.10=2<10](https://img.qammunity.org/2021/formulas/mathematics/college/i0d23nxaxjcgl0lb79xt60546s8s1i9fqr.png)
Thus, a Normal approximation to binomial can be applied for population 1, if n = 100.
(2)
Check the Normal approximation conditions for population 2, for all the provided n as follows:
![n_(a)p_(1)=10* 0.30=3<10\\\\n_(b)p_(1)=100* 0.30=30>10\\\\n_(c)p_(1)=50* 0.30=15>10\\\\n_(d)p_(1)=40* 0.10=12>10\\\\n_(e)p_(1)=20* 0.10=6<10](https://img.qammunity.org/2021/formulas/mathematics/college/1a0niz3ucg6ftira0ntpf3ank7peeqdqsp.png)
Thus, a Normal approximation to binomial can be applied for population 2, if n = 100, 50 and 40.
(3)
Check the Normal approximation conditions for population 3, for all the provided n as follows:
![n_(a)p_(1)=10* 0.50=5<10\\\\n_(b)p_(1)=100* 0.50=50>10\\\\n_(c)p_(1)=50* 0.50=25>10\\\\n_(d)p_(1)=40* 0.50=20>10\\\\n_(e)p_(1)=20* 0.10=10=10](https://img.qammunity.org/2021/formulas/mathematics/college/eyzcf2yuc71440p3m5hqwe9utm5rtvic2v.png)
Thus, a Normal approximation to binomial can be applied for population 2, if n = 100, 50, 40 and 20.