lesson 13: sampling distribution
DESCRIPTION
LESSON 13: SAMPLING DISTRIBUTION. Outline Central Limit Theorem Sampling Distribution of Mean. CENTRAL LIMIT THEOREM. - PowerPoint PPT PresentationTRANSCRIPT
1
Outline
• Central Limit Theorem• Sampling Distribution of Mean
LESSON 13: SAMPLING DISTRIBUTION
2
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.X
3
Sample Size and Mean
0
0.02
0.04
0.06
0.08
0.1
1 5 9 13 17 21 25 29 33 37 41 45 49
Class Number
Rel
ativ
e F
req
uen
cy
Distribution of random numbers
4
Sample Size and Mean
0
0.02
0.04
0.06
0.08
0.1
1 5 9 13 17 21 25 29 33 37 41 45 49
Class Number
Rel
ativ
e F
req
uen
cy
Distribution of means of n random numbers, n=4
5
Sample Size and Mean
0
0.02
0.04
0.06
0.08
0.1
1 5 9 13 17 21 25 29 33 37 41 45 49
Class Number
Rel
ativ
e F
req
uen
cy
Distribution of means of n random numbers, n=10
6
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
nnXX
,,2
2
n
X
X
7
• 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.
n
nσ
μ
X
deviation, standard
mean,
SAMPLING DISTRIBUTION OF THE SAMPLE MEAN
8
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
9
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.
f(x)
SAMPLING DISTRIBUTION OF THE SAMPLE MEAN
10
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.
f(x)
SAMPLING DISTRIBUTION OF THE SAMPLE MEAN
11
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.
SAMPLING DISTRIBUTION OF THE SAMPLE MEAN
12
• For a small, finite population N, the formula for the standard deviation of sampling mean is corrected as follows:
1
N
nN
nX
CORRECTION FOR SMALL SAMPLE SIZE
13
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