How Do You Estimate the Number of Elements with a Specific Property in a Sample?

In summary, point estimation is a statistical method used to estimate a population parameter based on a sample of data. It differs from interval estimation in that it provides a single estimate rather than a range of values. The assumptions of point estimation include a random and representative sample, a large sample size, and a normally distributed population. The purpose of point estimation is to provide an estimate that can be used to make inferences about the entire population. However, its limitations include only providing a single estimate and assuming a representative sample, which may not always be the case.
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emob2p
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Suppose I have a total population with x/y percent having a certain property q. If I take an n-number sample of this population, what is the most likely number of elements in this sample that will have property q?

I want to say the fraction z/n will be as close to x/y as possible where z is the number of q-elements in our sample. Any thoughts on how to prove this?
 
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You want to maximize the binomial distribution.
 
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The most likely number of elements in the sample that will have property q can be estimated using the point estimation method. This method involves using a sample statistic, such as the sample proportion, to estimate the population parameter of interest. In this case, we are interested in the proportion of the population with property q.

The sample proportion, denoted by p, is defined as the number of elements in the sample with property q divided by the total sample size, n. Therefore, the most likely number of elements in the sample with property q can be estimated as p*n.

To prove that this is the most likely number, we can use the concept of maximum likelihood estimation. This approach states that the value of the parameter that maximizes the likelihood of obtaining the observed sample is the best estimate of the true population parameter.

In our case, the likelihood of obtaining a sample with z q-elements out of n total elements can be expressed as:

L(p) = (x/y)^z * (1-x/y)^(n-z)

To find the value of p that maximizes this likelihood, we can take the derivative of L(p) with respect to p and set it equal to 0:

dL(p)/dp = z*(x/y)^z * (1-x/y)^(n-z-1) - (n-z)*(x/y)^(z+1) * (1-x/y)^(n-z-1) = 0

Simplifying this equation, we get:

z*(1-p) - (n-z)*p = 0

Solving for p, we get:

p = z/n

Therefore, the value of p that maximizes the likelihood of obtaining the observed sample is z/n, which is the sample proportion. This proves that p*n is the most likely number of elements in the sample with property q, as it is the value of p that maximizes the likelihood.

In conclusion, the most likely number of elements in the sample with property q can be estimated using the sample proportion, which is calculated as the number of elements in the sample with property q divided by the total sample size. This method follows the principle of maximum likelihood estimation and can be used to provide a reliable estimate of the population parameter of interest.
 

FAQ: How Do You Estimate the Number of Elements with a Specific Property in a Sample?

What is point estimation?

Point estimation is a statistical method used to estimate a population parameter, such as the mean or proportion, based on a sample of data.

How is point estimation different from interval estimation?

Point estimation provides a single estimate of a population parameter, while interval estimation provides a range of values within which the true population parameter is likely to fall.

What are the assumptions of point estimation?

The assumptions of point estimation include having a random and representative sample, a large sample size, and a normally distributed population.

What is the purpose of point estimation?

The purpose of point estimation is to provide an estimate of a population parameter based on a sample of data. This estimate can then be used to make inferences about the entire population.

What are the limitations of point estimation?

Point estimation only provides a single estimate of a population parameter and does not take into account any variability or uncertainty in the estimate. It also assumes that the sample is representative of the entire population, which may not always be the case.

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