When you have to repeatedly take the same test, with constant probability of succeeding/failing, you have to use Bernoulli's distribution. It states that, if you take 
tests with "succeeding" probability 
, and you want to "succeed" k of those n times, the probability is
In your case, you have n=18 (the number of tests), and p=0.3 (the probability of succeeding). We want to succeed between 8 and 12 times, which means choosing k=8,9,10,11, or 12. For example, the probability of succeeding 8 times is
you can plug the different values of k to get the probabilities of succeeding 9, 10, 11 and 12 times, and your final answer will be
Answer:
18.
Step-by-step explanation:
Central Limit Theorem
The Central Limit Theorem establishes that, for a normally distributed random variable X, with mean 
and standard deviation 
, the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean and standard deviation 
.
For a skewed variable, the Central Limit Theorem can also be applied, as long as n is at least 30.
For a proportion p in a sample of size n(n being at least 30), the sampling distribution of the sample proportion will be approximately normal with mean 
and standard deviation 
In this question:
n has to be at least 30. So the choice that answer this question, a size of n too small to use a normal curve to approximate the sampling distribution, is 18.
On your graph for the bottom numbers write 1,2,3,4,5 and on the left side write 4,8,12
The lines whose equations are given intersect at. (4, 0)
The points (2, 1), (3, 3), (4, 5), and (5, 6) are collinear.
a. True
