Answer: Yes.
Step-by-step explanation:
First ratio
5 : 15
Simplify (divide both sides by 5)
1 : 3
Second Ratio
3 : 9
Simplify (divide both sides by 3)
1 : 3
Answer:
We can find the individual probabilities:
And replacing we got:
![P(X \geq 5) = 1-[0.00114+0.009282+0.0358+0.0869+0.149]= 0.7178](https://tex.z-dn.net/?f=P%28X%20%5Cgeq%205%29%20%3D%201-%5B0.00114%2B0.009282%2B0.0358%2B0.0869%2B0.149%5D%3D%200.7178)
Step-by-step explanation:
Previous concepts
The binomial distribution is a "DISCRETE probability distribution that summarizes the probability that a value will take one of two independent values under a given set of parameters. The assumptions for the binomial distribution are that there is only one outcome for each trial, each trial has the same probability of success, and each trial is mutually exclusive, or independent of each other".
Solution to the problem
Let X the random variable of interest, on this case we now that:
The probability mass function for the Binomial distribution is given as:
Where (nCx) means combinatory and it's given by this formula:
And we want to find this probability:

And we can use the complement rule:
We can find the individual probabilities:
And replacing we got:
![P(X \geq 5) = 1-[0.00114+0.009282+0.0358+0.0869+0.149]= 0.7178](https://tex.z-dn.net/?f=P%28X%20%5Cgeq%205%29%20%3D%201-%5B0.00114%2B0.009282%2B0.0358%2B0.0869%2B0.149%5D%3D%200.7178)
3(2)(-1)=
3×2×-1=
3×2=6×-1=-6
-6 is the answer
4 x 3 = 12
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Finding the sample size for estimating a population proportion.
The formula is:
n = (z/m)^2 p~(1−p~)
where:
Z is the z value of the confidence level where 95% is equal to 1.96
M is the margin of error where 0.05
And p~ is the estimated value of the proportion where it is 0.50
Solution:
n = (1.96/0.05)^2 (0.5) (1-0.5)
= 1.536.64 (0.5) (0.5)
= 768.32 (0.5)
= 384.16
This is the minimum sample size, therefore we should round it up to 385. The answer is letter c.