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Published byHoratio Hardy Modified over 6 years ago

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**Sample Distribution Models for Means and Proportions**

Central limit Theorem Sample Distribution Models for Means and Proportions

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**Distribution of sample proportions**

We’re interested in finding a certain proportion from a population. We would need to take a sample from the population. From this we’ll find the sample proportion, If we did this repeatedly keeping the same sample size and looked at the distribution of sample proportions: From the CLT, the distribution would be approximately Normal

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**The mean of the sample distribution would be the population proportion,**

The standard deviation is There are two assumptions that must be met for this to be true The sampled values must be independent The sample size, n, must be large enough

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**Conditions to check for the assumptions**

Success/Failure: The expected number of successes and failure is both greater than 10 10% Condition: Each sample is less than 10% of the population Randomization: The sample was obtained through random sample techniques or we can at least assume that the sample is representative.

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Example: Skittles According to the manufacturer of the candy Skittles, 20% of the candy produced is the color red. What is the probability that given a large bag of skittles with 58 candies that we get at least 17 red?

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Conditions: 10% condition: 58 skittles is less than 10% of all skittles produced. Success/Failure: Randomization: Though not from a random sample we can assume the bag is representative of the population. We can use the Normal model for the distribution of sample proportions.

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Mean: Standard Deviation: So the model for becomes N(0.20,0.0525) Sample proportion:

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**Then to find the probability that we get a sample proportion of 0**

Then to find the probability that we get a sample proportion of or higher:

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Central Limit Theorem The mean of a random sample has a sampling distribution whose shape can be approximated by a Normal model. The larger the sample, the better the approximation will be. This is regardless of the shape of the distribution of the population being sampled from or the shape of the distribution of the sample.

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**Distribution of Sample Means**

A sample of size n is taken from a population with mean, µ, and standard deviation, σ. From the CLT the distribution of the sample means, , will be approximately Normal. The mean is the mean of the population: The standard deviation is the population standard deviation divided by the square root of sample size, n.

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Assumptions There are two assumptions that must be met for this to be true The sampled values must be independent The sample size, n, must be large enough

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Conditions: Randomization: The sample was obtained through random sample techniques or we can at least assume that the sample is representative. Independence(10% Condition) Large enough sample: No set rule here. If the population distribution is thought to be unimodal and symmetric a small sample size will do. If it is more skewed, larger samples are needed. n > 30

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Example: SAT Scores Suppose that male seniors have a mean score of with a standard deviation of 130. If we take a random sample of 100 male seniors what would be the distribution of sample means look like? What would be the probability that we would get an average of less than 1150?

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Conditions Randomization: Stated as a random sample Independent: 100 male seniors is less than 10% of all male seniors A sample of 100 would be a large enough sample A Normal model would be appropriate for the distribution of sample means.

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Parameters: So we have a Normal model N(1200,13).

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