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Transcript
```Confidence Interval for a Mean
Confidence
Intervals
_
_
X %C.I .  ( x E    x E )
Sample Size
 30
 30
Sigma
 known
  
x  z 

2
n


  
x  z 

2
n


 s 

n


 s 
x  z 

2 n 
 unknown x  t 2,df 
Confidence Interval for a Proportion


X %C.I .  ( p  E  p p  E )


p  z 
2



 
pq 

n 

Inferential Statistics:

INFERENTIAL STATISTICS: Uses sample data to
make estimates, decisions, predictions, or other


The aim of inferential statistics is to make an inference
about a population, based on a sample (as opposed to a
census), AND to provide a measure of precision for the
method used to make the inference.
An inferential statement uses data from a sample and
applies it to a population.
Some Terminology


Estimation – is the process of estimating
the value of a parameter from information
obtained from a sample.
Estimators – sample measures (statistics)
that are used to estimate population
measures (parameters).
Terminology (cont’d.)


Point Estimate – is a specific
numerical value estimate of a
parameter.
Interval Estimate – of a parameter is
an interval or range of values used to
estimate the parameter. It may or may
not contain the actual value of the
parameter being estimated.
Terminology (cont’d.)


Confidence Level – of an interval
estimate of a parameter is the probability
that the interval will contain the
parameter.
Confidence Interval – is a specific
interval estimate of a parameter
determined by using data obtained from a
sample and by using a specific confidence
level.
Margin of Error, E

The term
  
z 

2 n 
is called the maximum error
of estimate or margin of error. It is the
maximum likely difference between the point
estimate of a parameter and the actual value of
the parameter. It is represented by a capital E;
  
E  z 

2 n 
Z/2 : Areas in the Tails
Obtaining :Convert
the Confidence Level
to a decimal, e.g. 95%
C.L. = .95. Then:
1    .95
  .05

  .025
2
2

95%
.025
.025

  
z 

2 n 
-z (here -1.96)
  
z 

2 n 
z (here 1.96)
2
Situation #1: Large Samples or
Normally Distributed Small Samples




A population mean is unknown to us, and we wish to
estimate it.
Sample size is > 30, and the population standard
deviation is known or unknown.
OR sample size is < 30, the population standard
deviation is known, and the population is normally
distributed.
The sample is a simple random sample.
Confidence Interval for
(Situation #1)



1 
  
x  z 

2 n 
 s 
x  z 

2 n 
Consider

The mean paid attendance for a sample of
30 Major League All Star games was
\$46,970.87, with a standard deviation of
\$14,358.21. Find a 95% confidence
interval for the mean paid attendance at all
Major League All Star games.
95% Confidence Interval for the
Mean Paid Attendance at the Major
League All Star Games
 \$14,358.21 
\$46,970.87  1.96

30


 \$46,970.87  \$5,138.02
(\$41,832.85    \$52,108.89)
Minimum Sample Size Needed

For an interval estimate of the population mean
is given by
2
 z   
n 2 
 E 



Where E is the maximum error of estimate (margin
of error)
Situation #2: Small Samples




A population mean is unknown to us, and we wish to
estimate it.
Sample size is < 30, and the population standard
deviation is unknown.
The variable is normally or approximately normally
distributed.
The sample is a simple random sample.
Student t Distribution







Is bell-shaped.
The mean, median, and mode are equal to 0 and
are located at the center of the distribution.
Curve never touches the x-axis.
Variance is greater than 1.
As sample size increases, the t distribution
approaches the standard normal distribution.
Has n-1 degrees of freedom.
Student t Distributions for
n = 3 and n = 12
Student t
Standard
normal
distribution
distribution
with n = 12
Student t
distribution
with n = 3
0
Confidence Interval for
(Situation #2)

A 1   confidence interval for
given by
 s 
x  t  ,n1 

2
 n


is
Consider

The mean salary of a sample of n=12
commercial airline pilots is \$97,334,
with a standard deviation of \$17,747.
Find a 90% confidence interval for the
mean salary of all commercial airline
pilots.
90% Confidence Interval for the
Mean Salary of Commercial Airline Pilots
 \$17,747 
\$97,334  1.796

12 

 \$97,334  \$9,201.12
(\$88,132.88    \$106,535.12)
t or z????
Is

Known?
yes
Use z-values no matter what
the sample size is.*
no
Is n greater than
or equal to 30?
yes
Use z-values and s in place
of  in the formula.
no
Use t-values and
s in the formula.**
*Variable must be normally distributed when n<30.
**Variable must be approximately normally distributed.
Situation #3: Confidence
Interval for a Proportion

A confidence interval for a population proportion p, is
given by
pˆ  z 

2
pˆ qˆ
n
Where p̂ is the sample proportion .
qˆ  1  pˆ
n = sample size
np and nq must both be greater than
or equal to 5.
Consider


In a recent survey of 150 households, 54 had
central air conditioning. Find the 90% confidence
interval for the true proportion of households that
have central air conditioning.
Here
pˆ  54
 .36
150
qˆ  1  pˆ  1  .36  .64
n  150
(.36)(. 64)
.36  1.645
150
 .36  .0645
(.296  p  .425)
We can be 90% confident that the true proportion, p, of
all homes having central air conditioning is between 29.6%
and 42.5%
Minimum Sample Size Needed

For an interval estimate of a population proportion
is given by
2
 z 
n  pˆ qˆ  2 
 E 


Where E is the maximum error of estimate (margin
of error)
End of slides
```
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