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LESSON 13: SAMPLING DISTRIBUTION. Outline Central Limit Theorem Sampling Distribution of Mean. CENTRAL LIMIT THEOREM.

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Lesson 13 sampling distribution
LESSON 13: SAMPLING DISTRIBUTION

Outline

  • Central Limit Theorem

  • Sampling Distribution of Mean


CENTRAL LIMIT THEOREM

Central Limit Theorem: If a random sample is drawn from any population, the sampling distribution of the sample mean is approximately normal for a sufficiently large sample size. The larger the sample size, the more closely the sampling distribution of will resemble a normal distribution.



Distribution of means of n random numbers, n=4


Distribution of means of n random numbers, n=10


SAMPLING DISTRIBUTION OF THE SAMPLE MEAN

  • If the sample size increases, the variation of the sample mean decreases.

  • Where,

    = Population mean

    = Population standard deviation

    = Sample size

    = Mean of the sample means

    = Standard deviation of the sample means


SAMPLING DISTRIBUTION OF THE SAMPLE MEAN

  • Summary: For any general distribution with mean and standard deviation

    • The distribution of mean of a sample of size can be approximated by a normal distribution with

  • Exercise: Generate 1000 random numbers uniformly distributed between 0 and 1. Consider 200 samples of size 5 each. Compute the sample means. Check if the histogram of sample means is normally distributed and mean and standard deviation follow the above rules.


SAMPLING DISTRIBUTION OF THE SAMPLE MEAN

Example 1: An automatic machine in a manufacturing process requires an important sub-component. The lengths of the sub-component are normally distributed with a mean, =120 cm and standard deviation, =5 cm. What does the central limit theorem say about the sampling distribution of the mean if samples of size 4 are drawn from this population?


SAMPLING DISTRIBUTION OF THE SAMPLE MEAN

Example 2: An automatic machine in a manufacturing process requires an important sub-component. The lengths of the sub-component are normally distributed with a mean, =120 cm and standard deviation, =5 cm. Find the probability that one randomly selected unit has a length greater than 123 cm.


SAMPLING DISTRIBUTION OF THE SAMPLE MEAN

Example 3: An automatic machine in a manufacturing process requires an important sub-component. The lengths of the sub-component are normally distributed with a mean, =120 cm and standard deviation, =5 cm. Find the probability that, if four units are randomly selected, their mean length exceeds 123 cm.


SAMPLING DISTRIBUTION OF THE SAMPLE MEAN

Example 4: An automatic machine in a manufacturing process requires an important sub-component. The lengths of the sub-component are normally distributed with a mean, =120 cm and standard deviation, =5 cm. Find the probability that, if four units are randomly selected, all four have lengths that exceed 123 cm.


CORRECTION FOR SMALL SAMPLE SIZE

  • For a small, finite population N, the formula for the standard deviation of sampling mean is corrected as follows:


READING AND EXERCISES

Lesson 13

Reading:

Sections 8-1, 8-2, 8-3, pp. 260-276

Exercises:

9-3,9-4, 9-8, 9-17, 9-19


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