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```Sample
Sample
Of size 2
Sample
Of size 3
1
A,B=3,1
2
A,B,C=3,1,5
3
2
A,C=3,5
4
A,B,D=3,1,6
3.33
3
A,D=3,6
4.5
A,B,E=3,1,2
2
4
A,E=3,2
2.5
A,C,D=3,5,6
4.67
5
B,C=1,5
3
A,C,E=3,5,2
3.33
6
B,D=1,6
3.5
A,D,E=3,6,2
3.67
7
B,E=1,2
1.5
B,C,D=1,5,6
4
8
C,D=5,6
5.5
B,C,E=1,5,2
2.67
9
C,E=5,2
3.5
B,D,E=1,6,2
3
10
D,E=6,2
4
C,D,E=5,6,2
4.33
Sample
Sample
Of size 2
p:
Sample
Proportion
Sample
Of size 3
p: Sample
Proportion
1
A,B=3,1
0
A,B,C=3,1,5
0
2
A,C=3,5
0
A,B,D=3,1,6
1/3
3
A,D=3,6
0.5
A,B,E=3,1,2
1/3
4
A,E=3,2
0.5
A,C,D=3,5,6
1/3
5
B,C=1,5
0
A,C,E=3,5,2
1/3
6
B,D=1,6
0.5
A,D,E=3,6,2
2/3
7
B,E=1,2
0.5
B,C,D=1,5,6
1/3
8
C,D=5,6
0.5
B,C,E=1,5,2
1/3
9
C,E=5,2
0.5
B,D,E=1,6,2
2/3
10
D,E=6,2
1
C,D,E=5,6,2
2/3
An importer of Herbs and Spices claims that average weight of packets of Saffron is 20
grams. However packets are actually filled to an average weight, μ=19.5 grams and
standard deviation, σ=1.8 gram. A random sample of 36 packets is selected, calculate
A- The probability that the average weight is 20 grams or more;
B- The two limits within which 95% of all packets weight;
C- The two limits within which 95% of all weights fall (n=36);
D- If the size of the random sample was 16 instead of 36 how would this affect the
results in (a), (b) and (c)? (State any assumptions made)
THE DISTRIBUTION OF THE SAMPLE MEAN
CENTRAL LIMIT THEOREM
A-) FROM ANY POPULATION
If x1,x2,…………,xn is a random variable of size n taken from any
distribution with mean µ and variance σ2 then, for large n, the
distribution of the sample mean x̄ is approximately normal
and x̄ ~ N(µ, σ2/n),
where x̄ = (x1+x2+…………+xn )/n
THE DISTRIBUTION OF THE SAMPLE MEAN
CENTRAL LIMIT THEOREM
B-) FROM A NORMAL POPULATION
If x1,x2,…………,xn is a random variable of size n taken from a normal
distribution with mean µ and variance σ2 such that X ~ N(µ, σ2),
then the distribution of x̄ is also normal and x̄ ~ N(µ, σ2/n),
where x̄ = (x1+x2+…………+xn )/n
The distribution of the sample mean (x̄) is known as the sampling
distribution of means and
the standard deviation of this distribution σ/ √n is known as the
standard error of the mean
Example for Correction Factor:
What is the value of the finite population correction
factor when
a-) n= 20 and N=200 ?
b-) n= 20 and N= 2000 ?
Example 2: Tuition Cost
The mean tuition cost at state universities throughout the USA is 4,260 USD per
year (2002 year figures). Use this value as the population mean and assume that
the population standard deviation is 900 USD. Suppose that a random sample of
50 state universities will be selected.
A-) Show the sampling distribution of x̄ (where x̄ is the sample mean tuition cost
for the 50 state universities)
B-) What is the probability that the random sample will provide a sample mean
within 250 USD of the population mean?
C-) What is the probability that the simple random sample will provide a sample
mean within 100 USD of the population mean?
Example 1:
A random variable of size 15 is taken from normal distribution with mean 60 and
standard deviation 4. Find the probability that the mean of the sample is less than 58.
Example 3:
If a random sample of size 30 is taken from binomial distribution with n=9 and p= 0.5
Q: Find the probability that the sample mean exceeds 5.
Example 4:
Suppose we have selected a random sample of n=36 observations from a population with
mean equal to 80 and standard deviation equal to 6.
Q: Find the probability that x̄ will be larger than 82.
Example 5: Ping-Pong Balls
The diameter of a brand of Ping-Pong balls is approximately normally distributed, with a
mean of 1.30 inches and a standard deviation of 0.04 inch. If you select a random sample
of 16 Ping-Pong balls,
A-) What is the sampling distribution of the sample mean?
B-) What is the probability that sample mean is less than 1.28 inches?
C-) What is the probability that sample mean is between 1.31 and 1.33 inches?
D-) The probability is 60% that sample mean will be between what two values,
symmetrically distributed around the population mean?
Example 6: E-Mails
Time spent using e-mail per session is normally distributed, with a mean of 8 minutes
and a standard deviation of 2 minutes. If you select a random sample of 25 sessions,
A-) What is the probability that sample mean is between 7.8 and 8.2 minutes?
B-) What is the probability that sample mean is between 7.5 and 8.0 minutes?
C-) If you select a random sample of 100 sessions, what is the probability that sample
mean is between 7.8 and 8.2 minutes?
D-) Explain the difference in the results of (A) and (C).
Example 6: ELECTION
A political pollster is conducting an analysis of sample results in order to make
predictions on election night. Assuming a two-candidate election, if a specific candidate
receives at least 55% of the vote in the sample, that candidate will be forecast as the
winner of the election. If you select a random sample of 100 voters, what is the
probability that a candidate will be forecast as the winner when
A-) the population percentage of her vote is 50.1% ?
B-) the population percentage of her vote is 60% ?
C-) the population percentage of her vote is 49% (and she will actually lose the election)?
D-) If the sample size is increased to 400, what are your answers to (A) through (C) ?
Discuss.
Types of Survey Errors
• Coverage error
Excluded from frame
• Non response error
• Sampling error
• Measurement error
nonresponses
Random differences from
sample to sample
Population
Distribution
?
??
?
Sampling
Distribution
??
? ??
?
Sample
?
?
X
Standard Normal
Distribution
Standardize
x
Z
Sampling Distribution Properties
Larger sample
size
As n increases,
σx
decreases
Smaller sample
size
μ
x
Sampling Distribution
Properties
μx  μ
(i.e.
Normal Population
Distribution
x is unbiased )
Variation:
σ
σx 
n
μ
x
μx
x
Normal Sampling
Distribution
(has the same mean)
How Large is Large Enough?
• For most distributions, n ≥ 30 will give a
sampling distribution that is nearly normal
• For fairly symmetric distributions, n ≥ 15
• For normal population distributions, the
sampling distribution of the mean is always
normally distributed
```
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