chap06 normal distributions & continous
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Modul Statistik Bisnis IITRANSCRIPT
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-1
Chapter 6
The Normal Distribution and Other Continuous Distributions
Statistics for ManagersUsing Microsoft® Excel
4th Edition
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-2
Chapter Goals
After completing this chapter, you should be able to:
Describe the characteristics of the normal distribution
Translate normal distribution problems into standardized normal distribution problems
Find probabilities using a normal distribution table
Evaluate the normality assumption
Recognize when to apply the uniform and exponential distributions
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-3
Chapter Goals
After completing this chapter, you should be able to:
Define the concept of a sampling distribution
Determine the mean and standard deviation for the sampling distribution of the sample mean, X
Determine the mean and standard deviation for the sampling distribution of the sample proportion, ps
Describe the Central Limit Theorem and its importance
Apply sampling distributions for both X and ps
_
_
(continued)
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-4
Probability Distributions
Continuous Probability
Distributions
Binomial
Hypergeometric
Poisson
Probability Distributions
Discrete Probability
Distributions
Normal
Uniform
Exponential
Ch. 5 Ch. 6
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-5
Continuous Probability Distributions
A continuous random variable is a variable that can assume any value on a continuum (can assume an uncountable number of values) thickness of an item time required to complete a task temperature of a solution height, in inches
These can potentially take on any value, depending only on the ability to measure accurately.
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-6
The Normal Distribution
Probability Distributions
Normal
Uniform
Exponential
Continuous Probability
Distributions
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-7
The Normal Distribution
‘Bell Shaped’ Symmetrical Mean, Median and Mode
are Equal
Location is determined by the mean, μ
Spread is determined by the standard deviation, σ
The random variable has an infinite theoretical range: + to
Mean = Median = Mode
X
f(X)
μ
σ
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-8
By varying the parameters μ and σ, we obtain different normal distributions
Many Normal Distributions
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-9
The Normal Distribution Shape
X
f(X)
μ
σ
Changing μ shifts the distribution left or right.
Changing σ increases or decreases the spread.
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-10
The Normal Probability Density Function
The formula for the normal probability density function is
Where e = the mathematical constant approximated by 2.71828
π = the mathematical constant approximated by 3.14159
μ = the population mean
σ = the population standard deviation
X = any value of the continuous variable
2μ)/σ](1/2)[(Xe2π
1f(X)
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-11
The Standardized Normal
Any normal distribution (with any mean and standard deviation combination) can be transformed into the standardized normal distribution (Z)
Need to transform X units into Z units
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-12
Translation to the Standardized Normal Distribution
Translate from X to the standardized normal (the “Z” distribution) by subtracting the mean of X and dividing by its standard deviation:
σ
μXZ
Z always has mean = 0 and standard deviation = 1
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-13
The Standardized Normal Probability Density Function
The formula for the standardized normal probability density function is
Where e = the mathematical constant approximated by 2.71828
π = the mathematical constant approximated by 3.14159
Z = any value of the standardized normal distribution
2(1/2)Ze2π
1f(Z)
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-14
The Standardized Normal Distribution
Also known as the “Z” distribution Mean is 0 Standard Deviation is 1
Z
f(Z)
0
1
Values above the mean have positive Z-values, values below the mean have negative Z-values
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-15
Example
If X is distributed normally with mean of 100 and standard deviation of 50, the Z value for X = 200 is
This says that X = 200 is two standard deviations (2 increments of 50 units) above the mean of 100.
2.050
100200
σ
μXZ
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-16
Comparing X and Z units
Z100
2.00200 X
Note that the distribution is the same, only the scale has changed. We can express the problem in original units (X) or in standardized units (Z)
(μ = 100, σ = 50)
(μ = 0, σ = 1)
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-17
Finding Normal Probabilities
Probability is the area under thecurve!
a b X
f(X) P a X b( )≤
Probability is measured by the area under the curve
≤
P a X b( )<<=(Note that the probability of any individual value is zero)
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-18
f(X)
Xμ
Probability as Area Under the Curve
0.50.5
The total area under the curve is 1.0, and the curve is symmetric, so half is above the mean, half is below
1.0)XP(
0.5)XP(μ 0.5μ)XP(
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-19
Empirical Rules
μ ± 1σ encloses about 68% of X’s
f(X)
Xμ μ+1σμ-1σ
What can we say about the distribution of values around the mean? There are some general rules:
σσ
68.26%
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-20
The Empirical Rule
μ ± 2σ covers about 95% of X’s
μ ± 3σ covers about 99.7% of X’s
xμ
2σ 2σ
xμ
3σ 3σ
95.44% 99.72%
(continued)
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-21
The Standardized Normal Table
The Standardized Normal table in the textbook (Appendix table E.2) gives the probability less than a desired value for Z (i.e., from negative infinity to Z)
Z0 2.00
.9772Example:
P(Z < 2.00) = .9772
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-22
The Standardized Normal Table
The value within the table gives the probability from Z = up to the desired Z value
.9772
2.0P(Z < 2.00) = .9772
The row shows the value of Z to the first decimal point
The column gives the value of Z to the second decimal point
2.0
.
.
.
(continued)
Z 0.00 0.01 0.02 …
0.0
0.1
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-23
General Procedure for Finding Probabilities
Draw the normal curve for the problem in terms of X
Translate X-values to Z-values
Use the Standardized Normal Table
To find P(a < X < b) when X is distributed normally:
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-24
Finding Normal Probabilities
Suppose X is normal with mean 8.0 and standard deviation 5.0
Find P(X < 8.6)
X
8.6
8.0
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-25
Suppose X is normal with mean 8.0 and standard deviation 5.0. Find P(X < 8.6)
Z0.12 0X8.6 8
μ = 8 σ = 10
μ = 0σ = 1
(continued)
Finding Normal Probabilities
0.125.0
8.08.6
σ
μXZ
P(X < 8.6) P(Z < 0.12)
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-26
Z
0.12
Z .00 .01
0.0 .5000 .5040 .5080
.5398 .5438
0.2 .5793 .5832 .5871
0.3 .6179 .6217 .6255
Solution: Finding P(Z < 0.12)
.5478.02
0.1 .5478
Standardized Normal Probability Table (Portion)
0.00
= P(Z < 0.12)P(X < 8.6)
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-27
Upper Tail Probabilities
Suppose X is normal with mean 8.0 and standard deviation 5.0.
Now Find P(X > 8.6)
X
8.6
8.0
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-28
Now Find P(X > 8.6)…(continued)
Z
0.12
0Z
0.12
.5478
0
1.000 1.0 - .5478 = .4522
P(X > 8.6) = P(Z > 0.12) = 1.0 - P(Z ≤ 0.12)
= 1.0 - .5478 = .4522
Upper Tail Probabilities
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-29
Probability Between Two Values
Suppose X is normal with mean 8.0 and standard deviation 5.0. Find P(8 < X < 8.6)
P(8 < X < 8.6)
= P(0 < Z < 0.12)
Z0.12 0
X8.6 8
05
88
σ
μXZ
0.125
88.6
σ
μXZ
Calculate Z-values:
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-30
Z
0.12
Solution: Finding P(0 < Z < 0.12)
.0478
0.00
= P(0 < Z < 0.12)P(8 < X < 8.6)
= P(Z < 0.12) – P(Z ≤ 0)= .5478 - .5000 = .0478
.5000
Z .00 .01
0.0 .5000 .5040 .5080
.5398 .5438
0.2 .5793 .5832 .5871
0.3 .6179 .6217 .6255
.02
0.1 .5478
Standardized Normal Probability Table (Portion)
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-31
Suppose X is normal with mean 8.0 and standard deviation 5.0.
Now Find P(7.4 < X < 8)
X
7.48.0
Probabilities in the Lower Tail
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-32
Probabilities in the Lower Tail
Now Find P(7.4 < X < 8)…
X7.4 8.0
P(7.4 < X < 8)
= P(-0.12 < Z < 0)
= P(Z < 0) – P(Z ≤ -0.12)
= .5000 - .4522 = .0478
(continued)
.0478
.4522
Z-0.12 0
The Normal distribution is symmetric, so this probability is the same as P(0 < Z < 0.12)
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-33
Steps to find the X value for a known probability:1. Find the Z value for the known probability
2. Convert to X units using the formula:
Finding the X value for a Known Probability
ZσμX
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-34
Finding the X value for a Known Probability
Example: Suppose X is normal with mean 8.0 and
standard deviation 5.0. Now find the X value so that only 20% of all
values are below this X
X? 8.0
.2000
Z? 0
(continued)
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-35
Find the Z value for 20% in the Lower Tail
20% area in the lower tail is consistent with a Z value of -0.84Z .03
-0.9 .1762 .1736
.2033
-0.7 .2327 .2296
.04
-0.8 .2005
Standardized Normal Probability Table (Portion)
.05
.1711
.1977
.2266
…
…
…
…X? 8.0
.2000
Z-0.84 0
1. Find the Z value for the known probability
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-36
2. Convert to X units using the formula:
Finding the X value
80.3
0.5)84.0(0.8
ZσμX
So 20% of the values from a distribution with mean 8.0 and standard deviation 5.0 are less than 3.80
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-37
Assessing Normality
Not all continuous random variables are normally distributed
It is important to evaluate how well the data set is approximated by a normal distribution
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-38
Assessing Normality
Construct charts or graphs For small- or moderate-sized data sets, do stem-and-
leaf display and box-and-whisker plot look symmetric?
For large data sets, does the histogram or polygon appear bell-shaped?
Compute descriptive summary measures Do the mean, median and mode have similar values? Is the interquartile range approximately 1.33 σ? Is the range approximately 6 σ?
(continued)
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-39
Assessing Normality
Observe the distribution of the data set Do approximately 2/3 of the observations lie within
mean 1 standard deviation? Do approximately 80% of the observations lie within
mean 1.28 standard deviations? Do approximately 95% of the observations lie within
mean 2 standard deviations? Evaluate normal probability plot
Is the normal probability plot approximately linear with positive slope?
(continued)
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-40
The Normal Probability Plot
Normal probability plot Arrange data into ordered array
Find corresponding standardized normal quantile
values
Plot the pairs of points with observed data values on
the vertical axis and the standardized normal quantile
values on the horizontal axis
Evaluate the plot for evidence of linearity
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-41
A normal probability plot for data from a normal distribution will be
approximately linear:
30
60
90
-2 -1 0 1 2 Z
X
The Normal Probability Plot(continued)
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-42
Normal Probability Plot
Left-Skewed Right-Skewed
Rectangular
30
60
90
-2 -1 0 1 2 Z
X
(continued)
30
60
90
-2 -1 0 1 2 Z
X
30
60
90
-2 -1 0 1 2 Z
X Nonlinear plots indicate a deviation from normality
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-43
The Uniform Distribution
Continuous Probability
Distributions
Probability Distributions
Normal
Uniform
Exponential
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-44
The Uniform Distribution
The uniform distribution is a probability distribution that has equal probabilities for all possible outcomes of the random variable
Also called a rectangular distribution
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-45
The Continuous Uniform Distribution:
otherwise 0
bXaifab
1
where
f(X) = value of the density function at any X value
a = minimum value of X
b = maximum value of X
The Uniform Distribution(continued)
f(X) =
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-46
Properties of the Uniform Distribution
The mean of a uniform distribution is
The standard deviation is
2
baμ
12
a)-(bσ
2
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-47
Uniform Distribution Example
Example: Uniform probability distribution over the range 2 ≤ X ≤ 6:
2 6
.25
f(X) = = .25 for 2 ≤ X ≤ 66 - 21
X
f(X)
42
62
2
baμ
1547.112
2)-(6
12
a)-(bσ
22
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-48
The Exponential Distribution
Continuous Probability
Distributions
Probability Distributions
Normal
Uniform
Exponential
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-49
The Exponential Distribution
Used to model the length of time between two occurrences of an event (the time between arrivals)
Examples: Time between trucks arriving at an unloading dock Time between transactions at an ATM Machine Time between phone calls to the main operator
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-50
The Exponential Distribution
Xλe1X)time P(arrival
Defined by a single parameter, its mean λ (lambda) The probability that an arrival time is less than
some specified time X is
where e = mathematical constant approximated by 2.71828
λ = the population mean number of arrivals per unit
X = any value of the continuous variable where 0 < X <
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-51
Exponential Distribution Example
Example: Customers arrive at the service counter at the rate of 15 per hour. What is the probability that the arrival time between consecutive customers is less than three minutes?
The mean number of arrivals per hour is 15, so λ = 15
Three minutes is .05 hours
P(arrival time < .05) = 1 – e-λX = 1 – e-(15)(.05) = .5276
So there is a 52.76% probability that the arrival time between successive customers is less than three minutes
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-52
Sampling Distributions
Sampling Distributions
Sampling Distributions
of the Mean
Sampling Distributions
of the Proportion
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-53
Sampling Distributions
A sampling distribution is a distribution of all of the possible values of a statistic for a given size sample selected from a population
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-54
Developing a Sampling Distribution
Assume there is a population …
Population size N=4
Random variable, X,
is age of individuals
Values of X: 18, 20,
22, 24 (years)
A B C D
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-55
.3
.2
.1
0 18 20 22 24
A B C D
Uniform Distribution
P(x)
x
(continued)
Summary Measures for the Population Distribution:
Developing a Sampling Distribution
214
24222018
N
Xμ i
2.236N
μ)(Xσ
2i
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-56
1st 2nd Observation Obs 18 20 22 24
18 18,18 18,20 18,22 18,24
20 20,18 20,20 20,22 20,24
22 22,18 22,20 22,22 22,24
24 24,18 24,20 24,22 24,24
16 possible samples (sampling with replacement)
Now consider all possible samples of size n=2
1st 2nd Observation Obs 18 20 22 24
18 18 19 20 21
20 19 20 21 22
22 20 21 22 23
24 21 22 23 24
(continued)
Developing a Sampling Distribution
16 Sample Means
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-57
1st 2nd Observation Obs 18 20 22 24
18 18 19 20 21
20 19 20 21 22
22 20 21 22 23
24 21 22 23 24
Sampling Distribution of All Sample Means
18 19 20 21 22 23 240
.1
.2
.3 P(X)
X
Sample Means Distribution
16 Sample Means
_
Developing a Sampling Distribution
(continued)
(no longer uniform)
_
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-58
Summary Measures of this Sampling Distribution:
Developing aSampling Distribution
(continued)
2116
24211918
N
Xμ i
X
1.5816
21)-(2421)-(1921)-(18
N
)μX(σ
222
2Xi
X
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-59
Comparing the Population with its Sampling Distribution
18 19 20 21 22 23 240
.1
.2
.3 P(X)
X 18 20 22 24
A B C D
0
.1
.2
.3
PopulationN = 4
P(X)
X _
1.58σ 21μXX
2.236σ 21μ
Sample Means Distributionn = 2
_
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-60
Sampling Distributions of the Mean
Sampling Distributions
Sampling Distributions
of the Mean
Sampling Distributions
of the Proportion
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-61
Standard Error of the Mean
Different samples of the same size from the same population will yield different sample means
A measure of the variability in the mean from sample to sample is given by the Standard Error of the Mean:
Note that the standard error of the mean decreases as the sample size increases
n
σσ
X
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-62
If the Population is Normal
If a population is normal with mean μ and
standard deviation σ, the sampling distribution
of is also normally distributed with
and
(This assumes that sampling is with replacement or sampling is without replacement from an infinite population)
X
μμX
n
σσ
X
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-63
Z-value for Sampling Distributionof the Mean
Z-value for the sampling distribution of :
where: = sample mean
= population mean
= population standard deviation
n = sample size
Xμσ
n
σμ)X(
σ
)μX(Z
X
X
X
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-64
Finite Population Correction
Apply the Finite Population Correction if: the sample is large relative to the population
(n is greater than 5% of N)
and… Sampling is without replacement
Then
1NnN
n
σ
μ)X(Z
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-65
Normal Population Distribution
Normal Sampling Distribution (has the same mean)
Sampling Distribution Properties
(i.e. is unbiased )xx
x
μμx
μ
xμ
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-66
Sampling Distribution Properties
For sampling with replacement:
As n increases,
decreasesLarger sample size
Smaller sample size
x
(continued)
xσ
μ
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-67
If the Population is not Normal
We can apply the Central Limit Theorem:
Even if the population is not normal, …sample means from the population will be
approximately normal as long as the sample size is large enough.
Properties of the sampling distribution:
andμμx n
σσx
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-68
n↑
Central Limit Theorem
As the sample size gets large enough…
the sampling distribution becomes almost normal regardless of shape of population
x
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-69
Population Distribution
Sampling Distribution (becomes normal as n increases)
Central Tendency
Variation
(Sampling with replacement)
x
x
Larger sample size
Smaller sample size
If the Population is not Normal(continued)
Sampling distribution properties:
μμx
n
σσx
xμ
μ
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-70
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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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-71
Example
Suppose a population has mean μ = 8 and standard deviation σ = 3. Suppose a random sample of size n = 36 is selected.
What is the probability that the sample mean is between 7.8 and 8.2?
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-72
Example
Solution:
Even if the population is not normally distributed, the central limit theorem can be used (n > 30)
… so the sampling distribution of is approximately normal
… with mean = 8
…and standard deviation
(continued)
x
xμ
0.536
3
n
σσx
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-73
Example
Solution (continued):(continued)
0.38300.5)ZP(-0.5
363
8-8.2
nσ
μ- μ
363
8-7.8P 8.2) μ P(7.8 X
X
Z7.8 8.2 -0.5 0.5
Sampling Distribution
Standard Normal Distribution .1915
+.1915
Population Distribution
??
??
?????
??? Sample Standardize
8μ 8μX
0μz xX
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-74
Sampling Distributions of the Proportion
Sampling Distributions
Sampling Distributions
of the Mean
Sampling Distributions
of the Proportion
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-75
Population Proportions, p
p = the proportion of the population having some characteristic
Sample proportion ( ps ) provides an estimate of p:
0 ≤ ps ≤ 1
ps has a binomial distribution
(assuming sampling with replacement from a finite population or without replacement from an infinite population)
size sample
interest ofstic characteri the having sample the in itemsofnumber
n
Xps
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-76
Sampling Distribution of p
Approximated by a
normal distribution if:
where
and
(where p = population proportion)
Sampling DistributionP( ps)
.3
.2
.1 0
0 . 2 .4 .6 8 1 ps
pμsp
n
p)p(1σ
sp
5p)n(1
5np
and
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-77
Z-Value for Proportions
If sampling is without replacement
and n is greater than 5% of the
population size, then must use
the finite population correction
factor:
1N
nN
n
p)p(1σ
sp
np)p(1
pp
σ
ppZ s
p
s
s
Standardize ps to a Z value with the formula:
pσ
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-78
Example
If the true proportion of voters who support
Proposition A is p = .4, what is the probability
that a sample of size 200 yields a sample
proportion between .40 and .45?
i.e.: if p = .4 and n = 200, what is
P(.40 ≤ ps ≤ .45) ?
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-79
Example
if p = .4 and n = 200, what is
P(.40 ≤ ps ≤ .45) ?
(continued)
.03464200
.4).4(1
n
p)p(1σ
sp
1.44)ZP(0
.03464
.40.45Z
.03464
.40.40P.45)pP(.40 s
Find :
Convert to standard normal:
spσ
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-80
Example
Z.45 1.44
.4251
Standardize
Sampling DistributionStandardized
Normal Distribution
if p = .4 and n = 200, what is
P(.40 ≤ ps ≤ .45) ?
(continued)
Use standard normal table: P(0 ≤ Z ≤ 1.44) = .4251
.40 0ps
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-81
Chapter Summary
Presented key continuous distributions normal, uniform, exponential
Found probabilities using formulas and tables
Recognized when to apply different distributions
Applied distributions to decision problems
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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-82
Chapter Summary
Introduced sampling distributions Described the sampling distribution of the mean
For normal populations Using the Central Limit Theorem
Described the sampling distribution of a proportion
Calculated probabilities using sampling distributions
(continued)