Answer:
A.- Normal
B.- Centered at the true proportion (see imagen)
C.- SE(p)=0.125
D.- There’s no way the histogram could ever look like a Normal model with only two possible values for the variable
Explanation:
A.- Normal
Based on the Central Limit Theorem (CLT) that states:
"The mean of a random sample has a sampling distribution whose shape can be approximated by a Normal model". The larger the sample, the better the approximation will be.
B.- See attached graph
C.- SE(p)=
![√(pq/n)=\sqrt[n]{(0.5)(0.5)/16}=0.125](https://img.qammunity.org/2020/formulas/mathematics/high-school/f3cuxphy6uzcu5xb4bas682ik5g72n3vfc.png)
D.- Success/Failure Condition:
The Success/Failure condition says that the sample size must be big enough so that both the number of “successes,” np and the number of “failures,” nq, are expected to be at least 10.
np=(16)(0.125)=2 not enough