Normal Approximation to Binomial Distribution

In summary, the Normal Approximation to Binomial Distribution is a statistical method used to approximate a binomial distribution with a normal distribution. It is typically used when the sample size is large and the probability of success is not too close to 0 or 1. The accuracy of the approximation depends on the sample size and probability of success, and it is not an exact match. The main difference between the two distributions is the shape of the curve and the assumptions made. To determine if the Normal Approximation is appropriate for your data, you can calculate the mean and standard deviation and visually compare the two curves.
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needhelp83
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On a production line, only 45% of items produced meet quality standards. A random sample of 500 items will be taken. Using the normal approximation to the binomial distribution, approximate the probability that less than half of the sampled items meet quality standards.

500*.5 = 250

P(Y<250) = P(X<249.5) where Bin (500, .45) and N(225, 123.75)
E(Y)=225 and Var(Y)=500*(.45)*(.55)=123.75

[tex]P( \frac{\overline{X}-225}{\sqrt{123.75}}<\frac{249.5-225}{\sqrt{123.75}})=P(Z<2.20)=\Phi(2.20)=0.9861[/tex]

Wanted to make sure this is done correctly. Thanks
 
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Looks good!
 

Related to Normal Approximation to Binomial Distribution

What is the Normal Approximation to Binomial Distribution?

The Normal Approximation to Binomial Distribution is a statistical method used to approximate a binomial distribution with a normal distribution. This is done by finding the mean and standard deviation of the binomial distribution and using those values to create a normal distribution curve that is a close approximation of the binomial distribution.

When is the Normal Approximation to Binomial Distribution used?

The Normal Approximation to Binomial Distribution is typically used when the sample size is large (at least 30) and when the probability of success is not too close to 0 or 1. It is also used when the binomial distribution is difficult to calculate or graph, making it easier to use the normal distribution as an approximation.

How accurate is the Normal Approximation to Binomial Distribution?

The accuracy of the Normal Approximation to Binomial Distribution depends on the sample size and the probability of success. Generally, the larger the sample size 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 not an exact match and may have some error.

What is the difference between the Normal Approximation to Binomial Distribution and the Binomial Distribution?

The main difference between the Normal Approximation to Binomial Distribution and the Binomial Distribution is the shape of the distribution curve. The Binomial Distribution is a discrete probability distribution, meaning it is made up of discrete values, while the Normal Approximation is a continuous probability distribution. Additionally, the Normal Approximation assumes a symmetrical distribution, while the Binomial Distribution does not have this assumption.

How can I determine if the Normal Approximation to Binomial Distribution is appropriate for my data?

To determine if the Normal Approximation to Binomial Distribution is appropriate for your data, you can calculate the mean and standard deviation of your binomial distribution. If the sample size is large enough (at least 30) and the probability of success is not too close to 0 or 1, then the Normal Approximation is likely a good fit. It is also recommended to visually compare the two distribution curves to see how closely they match.

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