Normal approx binomial question.

In summary, the conversation discusses using the normal approximation for a binomial distribution with parameters n=40 and p=0.6 to determine the probability of a certain value being greater than or less than a given value. The calculations are shown and compared to the actual answer, with some discrepancy noted. The second problem also discusses the limitations of using a normal table for very small probabilities.
  • #1
Shawj02
20
0
Ok, the question says

Binomial with n=40. p=0.6 use normal approximation and determine..
a) value at 14.
b)value lest then 12.

So I thought I had this down and packed but the answer in the back of the book tells me otherwise. Anyways the following is my working.

Y~Nor(13.5<x<14.5)
Mean=np=24
S.D = sqrt(np(1-p)) = 3.094

larger prob area
Z=(x-mean)/S.D

=(14.5 - 24)/3.094
=-3.07
(Tables book, 3.07 = 0.9989)
So (1-0.9989)

smaller prob area
Z=(x-mean)/S.D

=(13.5 - 24)/3.094
=-3.39
(Tables book, 3.39 = 0.9997)
So (1-0.9997)

So the final answer is:
larger prob area - smaller prob area
total prob =(1-0.9989) - (1-0.9997)
=8*10^-4

But the actual answer is meant to be 0.049?

B) I do pretty much the same thing But after the whole Z=(x-mean)/S.D (x=11.5)
end up with Z=-4.034 Which isn't on the tables book. So I can't figgure out the final prob.

Thanks!
 
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  • #2
First, I get

[tex]
\sigma \approx 3.0984
[/tex]

Regardless of this, your work for the first portion looks good. My calculations (in a spreadsheet and in statistical software) show the normal approximation to equal [tex] 0.0007 [/tex], certainly not [tex] 0.049 [/tex] - are you sure the solution you are looking at is for this particular problem? I also used the binomial distribution to work out [tex] \Pr(X = 14) [/tex] for this case, and I get [tex] 0.0008 [/tex], so the approximation is reasonable.


The [tex] Z [/tex]-value you get for the second problem is correct. The fact that it is below the smallest value in the normal table means that (to the decimal accuracy of the table) the corresponding probability is zero. That matches up rather well with the number from the binomial distribution, so all looks good.

Note: the reason these answers are so small is easy to see: both the cutoffs (12, 14) are several standard deviations from the mean. The normal distribution used for approximation is symmetric (every normal distribution is), and for the given values of [tex] n [/tex] and [tex] p [/tex] the binomial distribution is also quite symmetric: there just isn't much probability that far out in the tails.
 

1. What is the normal approximation to the binomial distribution?

The normal approximation to the binomial distribution is a mathematical formula used to estimate the probability of a certain number of successes in a series of independent trials. It is based on the assumption that the distribution of the number of successes follows a bell-shaped curve, or a normal distribution.

2. When can the normal approximation be used for a binomial distribution?

The normal approximation can be used for a binomial distribution when the number of trials is large (typically, at least 30) and the probability of success is not too close to 0 or 1. In these cases, the binomial distribution can be approximated by a normal distribution, making it easier to calculate probabilities.

3. How accurate is the normal approximation to the binomial distribution?

The accuracy of the normal approximation to the binomial distribution depends on the number of trials and the probability of success. Generally, the larger the number of trials and the closer the probability of success is to 0.5, the more accurate the approximation will be. However, it is important to note that the normal approximation is just an estimate and may not be perfectly accurate.

4. Can the normal approximation be used for all types of binomial distributions?

No, the normal approximation cannot be used for all types of binomial distributions. It is only suitable for binomial distributions with a large number of trials and a probability of success that is not too close to 0 or 1. If these conditions are not met, other methods such as exact calculations or simulation may be more appropriate.

5. How is the normal approximation to the binomial distribution calculated?

The normal approximation to the binomial distribution is calculated using the mean and standard deviation of the binomial distribution. The mean is equal to the number of trials multiplied by the probability of success, and the standard deviation is equal to the square root of (number of trials * probability of success * (1 - probability of success)). These values are then used to calculate probabilities using the normal distribution formula.

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