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MBMG-7104/ ITHS-2202/ IMAS-3101/
IMHS-3101 @Ravindra Nath Shukla (PhD Scholar) ABV-IIITM
Learning Objectives
In this chapter, you learn:
 To construct and interpret confidence interval
estimates for the mean and the proportion
 How to determine the sample size necessary to
develop a confidence interval estimate for the mean
or proportion
 How to use confidence interval estimates in auditing
Chap 8-2
Chapter Outline
 Confidence Intervals for the Population
Mean, μ
 when Population Standard Deviation σ is Known
 when Population Standard Deviation σ is Unknown
 Confidence Intervals for the Population
Proportion, π
 Determining the Required Sample Size
Chap 8-3
Point and Interval Estimates
 A point estimate is a single number,
 a confidence interval provides additional information about the
variability of the estimate
Chap 8-4
Point Estimate
Lower
Confidence
Limit
Upper
Confidence
Limit
Width of
confidence interval
Point Estimates
Chap 8-5
We can estimate a
Population Parameter …
with a Sample
Statistic
(a Point Estimate)
Mean
Proportion p
π
X
μ
Confidence Intervals
 How much uncertainty is associated with a point estimate of a
population parameter?
 An interval estimate provides more information about a population
characteristic than does a point estimate.
 Such interval estimates are called confidence intervals.
Chap
8-6
Chap 8-6
Confidence Interval Estimate
 An interval gives a range of values:
Takes into consideration variation in sample
statistics from sample to sample
Based on observations from 1 sample
Gives information about closeness to
unknown population parameters
Stated in terms of level of confidence
e.g. 95% confident, 99% confident
Can never be 100% confident
Chap
8-7
Chap 8-7
Confidence Interval Example
Cereal fill example
 Population has µ = 368 and σ = 15.
 If you take a sample of size n = 25 you know
 368 ± 1.96 * 15 / = (362.12, 373.88) contains 95% of
the sample means
 When you don’t know µ, you use X to estimate µ
 If X = 362.3 the interval is 362.3 ± 1.96 * 15 / = (356.42, 368.18)
 Since 356.42 ≤ µ ≤ 368.18 the interval based on this sample makes a correct
statement about µ.
But what about the intervals from other possible samples
of size 25?
25
25
Chap
8-8
Chap 8-8
Confidence Interval Example
Sample # X
Lower
Limit
Upper
Limit
Contain
µ?
1 362.30 356.42 368.18 Yes
2 369.50 363.62 375.38 Yes
3 360.00 354.12 365.88 No
4 362.12 356.24 368.00 Yes
5 373.88 368.00 379.76 Yes
Chap
8-9
Chap 8-9
(continued)
Confidence Interval Example
 In practice you only take one sample of size n
 In practice you do not know µ so you do not know if the
interval actually contains µ
 However, you do know that 95% of the intervals formed
in this manner will contain µ
 Thus, based on the one sample you actually selected,
you can be 95% confident your interval will contain µ
(this is a 95% confidence interval)
Chap 8-10
(continued)
Note: 95% confidence is based on the fact that we used Z = 1.96.
Estimation Process
Chap
8-11
Chap 8-11
(mean, μ, is
unknown)
Population
Random Sample
Mean
X = 50
Sample
I am 95%
confident that
μ is between
40 & 60.
General Formula
 The general formula for all confidence
intervals is:
Chap
8-12
Chap 8-12
Point Estimate ± (Critical Value)(Standard Error)
Where:
•Point Estimate is the sample statistic estimating the population
parameter of interest
•Critical Value is a table value based on the sampling
distribution of the point estimate and the desired confidence
level
•Standard Error is the standard deviation of the point estimate
Confidence Level
 Confidence Level
Confidence the interval will
contain the unknown population
parameter
A percentage (less than 100%)
Chap
8-13
Chap 8-13
Confidence Level, (1-)
 Suppose confidence level = 95%
 Also written (1 - ) = 0.95, (so  = 0.05)
 A relative frequency interpretation:
 95% of all the confidence intervals that can be constructed will contain
the unknown true parameter
 A specific interval either will contain or will not contain the true
parameter
 No probability involved in a specific interval
Chap
8-14
Chap 8-14
(continued)
Confidence Intervals
Chap
8-15
Chap 8-15
Population
Mean
σ Unknown
Confidence
Intervals
Population
Proportion
σ Known
Confidence Interval for μ
(σ Known)
 Assumptions
 Population standard deviation σ is known
 Population is normally distributed
 If population is not normal, use large sample
 Confidence interval estimate:
where is the point estimate
Zα/2 is the normal distribution critical value for a probability of /2 in each tail
is the standard error
Chap
8-16
Chap 8-16
n
σ
/2
Z
X α

X
n
σ/
Finding the Critical Value, Zα/2
 Consider a 95% confidence interval:
Chap
8-17
Chap 8-17
Zα/2 = -1.96 Zα/2 = 1.96
0.05
so
0.95
1 

 α
α
0.025
2

α
0.025
2

α
Point Estimate
Lower
Confidence
Limit
Upper
Confidence
Limit
Z units:
X units: Point Estimate
0
1.96
/2
Z 

α
Common Levels of Confidence
 Commonly used confidence levels are 90%, 95%, and 99%
Chap
8-18
Chap 8-18
Confidence
Level
Confidence
Coefficient, Zα/2 value
1.28
1.645
1.96
2.33
2.58
3.08
3.27
0.80
0.90
0.95
0.98
0.99
0.998
0.999
80%
90%
95%
98%
99%
99.8%
99.9%


1
Intervals and Level of Confidence
Chap
8-19
Chap 8-19
μ
μx

Confidence Intervals
Intervals
extend from
to
(1-)x100%
of intervals
constructed
contain μ;
()x100% do
not.
Sampling Distribution of the Mean
n
σ
2
/
α
Z
X 
n
σ
2
/
α
Z
X 
x
x1
x2
/2
 /2



1
Example
 A sample of 11 circuits from a large normal population has a mean
resistance of 2.20 ohms. We know from past testing that the
population standard deviation is 0.35 ohms.
 Determine a 95% confidence interval for the true mean resistance
of the population.
Chap
8-20
Chap 8-20
Example
 A sample of 11 circuits from a large normal population has a mean
resistance of 2.20 ohms. We know from past testing that the
population standard deviation is 0.35 ohms.
 Solution:
Chap
8-21
Chap 8-21
2.4068
1.9932
0.2068
2.20
)
11
(0.35/
1.96
2.20
n
σ
/2
Z
X







μ
α
(continued)
Interpretation
 We are 95% confident that the true mean
resistance is between 1.9932 and 2.4068
ohms
 Although the true mean may or may not
be in this interval, 95% of intervals
formed in this manner will contain the
true mean
Chap
8-22
Chap 8-22
Confidence Intervals
Chap
8-23
Chap 8-23
Population
Mean
σ Unknown
Confidence
Intervals
Population
Proportion
σ Known
Do You Ever Truly Know σ?
 Probably not!
 In virtually all real world business situations,
σ is not known.
 If there is a situation where σ is known, then
µ is also known (since to calculate σ you need
to know µ.)
 If you truly know µ there would be no need to
gather a sample to estimate it.
Chap
8-24
Chap 8-24
Confidence Interval for μ
(σ Unknown)
 If the population standard deviation σ
is unknown, we can substitute the
sample standard deviation, S
 This introduces extra uncertainty, since
S is variable from sample to sample
 So we use the t distribution instead of
the normal distribution
Chap
8-25
Chap 8-25
Confidence Interval for μ
(σ Unknown)
 Assumptions
 Population standard deviation is unknown
 Population is normally distributed
 If population is not normal, use large sample
 Use Student’s t Distribution
 Confidence Interval Estimate:
(where tα/2 is the critical value of the t distribution with n -1
degrees of freedom and an area of α/2 in each tail)
Chap 8-26
n
S
t
X 2
/
α

(continued)
Student’s t Distribution
 The t is a family of distributions
 The tα/2 value depends on degrees of
freedom (d.f.)
 Number of observations that are free to vary after
sample mean has been calculated
d.f. = n - 1
Chap 8-27
Degrees of Freedom (df)
Idea: Number of observations that are free to vary
after sample mean has been calculated
Example: Suppose the mean of 3 numbers is 8.0
Let X1 = 7
Let X2 = 8
What is X3?
Chap 8-28
If the mean of these three
values is 8.0,
then X3 must be 9
(i.e., X3 is not free to vary)
Here, n = 3, so degrees of freedom = n – 1 = 3 – 1 = 2
(2 values can be any numbers, but the third is not free to vary
for a given mean)
Student’s t Distribution
Chap
8-29
Chap 8-29
t
0
t (df = 5)
t (df = 13)
t-distributions are bell-
shaped and symmetric, but
have ‘fatter’ tails than the
normal
Standard
Normal
(t with df = ∞)
Note: t Z as n increases
Student’s t Table
Chap
8-30
Chap 8-30
Upper Tail Area
df .10 .05 .025
1 3.078 6.314 12.706
2 1.886
3 1.638 2.353 3.182
t
0 2.920
The body of the table
contains t values, not
probabilities
Let: n = 3
df = n - 1 = 2
 = 0.10
/2 = 0.05
/2 = 0.05
4.303
2.920
Selected t distribution values
Chap 8-31
With comparison to the Z value
Confidence t t t Z
Level (10 d.f.) (20 d.f.) (30 d.f.) (∞ d.f.)
0.80 1.372 1.325 1.310 1.28
0.90 1.812 1.725 1.697 1.645
0.95 2.228 2.086 2.042 1.96
0.99 3.169 2.845 2.750 2.58
Note: t Z as n increases
Example of t distribution
confidence interval
A random sample of n = 25 has X = 50 and
S = 8. Form a 95% confidence interval for μ
 d.f. = n – 1 = 24, so
The confidence interval is
Chap
8-32
Chap 8-32
2.0639
0.025
t
/2 

α
t
25
8
(2.0639)
50
n
S
/2 

 α
t
X
46.698 ≤ μ ≤ 53.302
Example of t distribution
confidence interval
 Interpreting this interval requires the assumption that
the population you are sampling from is approximately a
normal distribution (especially since n is only 25).
 This condition can be checked by creating a:
 Normal probability plot or
 Boxplot
Chap
8-33
Chap 8-33
(continued)
Example 2
 An online grocery store is interested in estimating
the basket size (number of items ordered by the
customer) of its customer order so that it can
optimize its size of crates used for delivering the
grocery items. From a sample of 81 customers, the
average basket size was estimated as 24 and the
standard deviation estimated from the sample was
3.8. Calculate the 95% confidence interval for the
basket size of the customer order.
Chap
8-34
Confidence Intervals
Chap 8-35
Population
Mean
σ Unknown
Confidence
Intervals
Population
Proportion
σ Known
Confidence Intervals for the
Population Proportion, π
 An interval estimate for the population
proportion ( π ) can be calculated by
adding an allowance for uncertainty to
the sample proportion ( p )
Chap
8-36
Chap 8-36
Confidence Intervals for the
Population Proportion, π
 Recall that the distribution of the sample
proportion is approximately normal if the
sample size is large, with standard deviation
 We will estimate this with sample data
Chap
8-37
Chap 8-37
(continued)
n
p)
p(1
n
)
(1
σp

 

Confidence Interval Endpoints
 Upper and lower confidence limits for the population proportion are
calculated with the formula
 where
 Zα/2 is the standard normal value for the level of confidence
desired
 p is the sample proportion
 n is the sample size
 Note: must have X > 5 and n – X > 5
Chap
8-38
Chap 8-38
n
p)
p(1
/2
Z
p

 α
Example
 A random sample of 100 people
shows that 25 are left-handed.
 Form a 95% confidence interval for
the true proportion of left-handers
Chap
8-39
Chap 8-39
Example
 A random sample of 100 people shows that 25 are left-handed.
Form a 95% confidence interval for the true proportion of left-
handers.
Chap 8-40
/100
0.25(0.75)
1.96
25/100
p)/n
p(1
Z
p /2



 
0.3349
0.1651
(0.0433)
1.96
0.25





(continued)
np = 100 * 0.25 = 25 > 5 & n(1-p) = 100 * 0.75 = 75 > 5
Make sure
the sample
is big enough
Interpretation
 We are 95% confident that the true percentage of left-handers in
the population is between
16.51% and 33.49%.
 Although the interval from 0.1651 to 0.3349 may or may not
contain the true proportion, 95% of intervals formed from samples
of size 100 in this manner will contain the true proportion.
Chap
8-41
Chap 8-41
Example 2
 A retail store was interested in finding the proportion of
customers who pay through cash (as against credit or
debit card) for the merchandize they buy at the store.
From a sample of 100 customers, it was found that 70
customers paid by cash. Calculate the 95% confidence
interval for proportion of customers who pay by cash.
Chap
8-42
Determining Sample Size
Chap
8-43
Chap 8-43
For the
Mean
Determining
Sample Size
For the
Proportion
Sampling Error
 The required sample size can be found to obtain a desired margin of
error (e) with a specified level of confidence (1 - )
 The margin of error is also called sampling error
 the amount of imprecision in the estimate of the population parameter
 the amount added and subtracted to the point estimate to form the
confidence interval
Chap
8-44
Chap 8-44
Determining Sample Size
Chap
8-45
Chap 8-45
For the
Mean
Determining
Sample Size
n
σ
2
/
α
Z
X 
n
σ
2
/
α
Z
e 
Sampling error
(margin of error)
Determining Sample Size
Chap
8-46
Chap 8-46
For the
Mean
Determining
Sample Size
n
σ
2
/

Z
e 
(continued)
2
2
2
2
/ σ
e
Z
n 

Now solve
for n to get
Determining Sample Size
 To determine the required sample size for the mean, you must know:
 The desired level of confidence (1 - ), which determines the critical
value, Zα/2
 The acceptable sampling error, e
 The standard deviation, σ
Chap
8-47
Chap 8-47
(continued)
Required Sample Size Example
If  = 45, what sample size is needed to estimate the mean within
± 5 with 90% confidence?
Chap
8-48
Chap 8-48
(Always round up)
219.19
5
(45)
(1.645)
e
σ
Z
n 2
2
2
2
2
2



So the required sample size is n = 220
Determining Sample Size
Chap 8-49
Determining
Sample Size
For the
Proportion
2
2
e
)
(1
Z
n
π
π 

Now solve
for n to get
n
)
(1
Z
e
π
π 

(continued)
Determining Sample Size
 To determine the required sample size for the
proportion, you must know:
 The desired level of confidence (1 - ), which determines
the critical value, Zα/2
 The acceptable sampling error, e
 The true proportion of events of interest, π
 π can be estimated with a pilot sample if necessary (or conservatively use 0.5 as
an estimate of π)
Chap
8-50
Chap 8-50
(continued)
Required Sample Size Example
How large a sample would be necessary
to estimate the true proportion defective
in a large population within ±3%, with
95% confidence?
(Assume a pilot sample yields p = 0.12)
Chap
8-51
Chap 8-51
Required Sample Size Example
Chap
8-52
Chap 8-52
Solution:
For 95% confidence, use Zα/2 = 1.96
e = 0.03
p = 0.12, so use this to estimate π
So use n = 451
450.74
(0.03)
0.12)
(0.12)(1
(1.96)
e
)
(1
Z
n 2
2
2
2
/2





π
π

(continued)
Applications in Auditing
 Six advantages of statistical sampling in auditing
 Sampling is less time consuming and less costly
 Sampling provides an objective way to calculate the sample size in advance
 Sampling provides results that are objective and defensible
 Because the sample size is based on demonstrable statistical principles, the audit is
defensible before one’s superiors and in a court of law
Chap 8-53
Applications in Auditing
 Sampling provides an estimate of the sampling error
 Allows auditors to generalize their findings to the population with a known sampling
error
 Can provide more accurate conclusions about the population
 Sampling is often more accurate for drawing conclusions about large
populations
 Examining every item in a large population is subject to significant non-sampling
error
 Sampling allows auditors to combine, and then evaluate collectively, samples
collected by different individuals
Chap
8-54
Chap 8-54
(continued)
Confidence Interval for Population
Total Amount
 Point estimate for a population of size N:
 Confidence interval estimate:
Chap 8-55
X
N
total
Population 
1
n
S
)
2
/
(



N
n
N
t
N
X
N α
(This is sampling without replacement, so use the finite population
correction factor in the confidence interval formula)
Confidence Interval for Population
Total: Example
Chap
8-56
Chap 8-56
A firm has a population of 1,000 accounts and wishes
to estimate the total population value.
A sample of 80 accounts is selected with average
balance of $87.6 and standard deviation of $22.3.
Construct the 95% confidence interval estimate of the
total balance.
Example Solution
Chap
8-57
Chap 8-57
The 95% confidence interval for the population total
balance is $82,837.52 to $92,362.48
/2
S
( )
1
n
22.3 1,000 80
(1,000)(87.6) (1,000)(1.9905)
1,000 1
80
87,600 4,762.48
N n
N X N t
N





 

 
22.3
S
,
6
.
87
X
80,
n
,
1000
N 



Confidence Interval for
Total Difference
 Point estimate for a population of size N:
 Where the average difference, D, is:
Chap
8-58
Chap 8-58
D
N
Difference
Total 
value
original
-
value
audited
D
where
n
D
D
i
n
1
i
i




Confidence Interval for
Total Difference
 Confidence interval estimate:
where
Chap
8-59
Chap 8-59
1
n
S
)
( D
2
/



N
n
N
t
N
D
N 
(continued)
1
)
D
(
1
2





n
D
S
n
i
i
D
One-Sided Confidence Intervals
 Application: find the upper bound for the proportion of items that do not
conform with internal controls
 where
 Zα is the standard normal value for the level of confidence desired
 p is the sample proportion of items that do not conform
 n is the sample size
 N is the population size
Chap
8-60
Chap 8-60
1
N
n
N
n
p)
p(1
Z
p
bound
Upper




 α
Ethical Issues
 A confidence interval estimate (reflecting sampling
error) should always be included when reporting a point
estimate
 The level of confidence should always be reported
 The sample size should be reported
 An interpretation of the confidence interval estimate
should also be provided
Chap
8-61
Chap 8-61
Chapter Summary
 Introduced the concept of confidence intervals
 Discussed point estimates
 Developed confidence interval estimates
 Created confidence interval estimates for the mean (σ
known)
 Determined confidence interval estimates for the mean
(σ unknown)
 Created confidence interval estimates for the
proportion
 Determined required sample size for mean and
proportion confidence interval estimates with a desired
margin of error
Chap
8-62
Chap 8-62
Chapter Summary
 Developed applications of confidence interval
estimation in auditing
 Confidence interval estimation for population total
 Confidence interval estimation for total difference in the population
 One-sided confidence intervals for the proportion nonconforming
 Addressed confidence interval estimation and
ethical issues
Chap
8-63
Chap 8-63
(continued)
Estimation & Sample Size
Determination For Finite
Populations
Chap
8-64
Basic Business Statistics
Topic Learning Objectives
In this topic, you learn:
 When to use a finite population correction factor in
calculating a confidence interval for either µ or π
 How to use a finite population correction factor in
calculating a confidence interval for either µ or π
 How to use a finite population correction factor in
calculating a sample size for a confidence interval for
either µ or π
Chap
8-65
Use A fpc When Sampling More Than 5% Of
The Population (n/N > 0.05)
Chap
8-66
Confidence Interval For µ with a fpc
/ 2
1
S N n
X t
N
n




Confidence Interval For π with a fpc
1
)
1
(
2
/




N
n
N
n
p
p
z
p 
A fpc simply
reduces the
standard error
of either the
sample mean or
the sample proportion
Confidence Interval for µ with a fpc
Chap
8-67
10
s
50,
X
100,
n
1000,
N
Suppose 



)
88
.
51
,
12
.
48
(
88
.
1
50
1
1000
100
1000
100
10
984
.
1
50
1
:
for
CI
95% 2
/










N
n
N
n
s
t
X 

Determining Sample Size with a fpc
 Calculate the sample size (n0) without a fpc
 For µ:
 For π:
 Apply the fpc utilizing the following formula to arrive at
the final sample size (n).
 n = n0N / (n0 + (N-1))
Chap
8-68
2 2
/2
0 2
Z
n
e



2
/ 2
0 2
(1 )
Z
n
e

  

Topic Summary
 Described when to use a finite population correction
in calculating a confidence interval for either µ or π
 Examined the formulas for calculating a confidence
interval for either µ or π utilizing a finite population
correction
 Examined the formulas for calculating a sample size
in a confidence interval for either µ or π

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Business Analytics _ Confidence Interval

  • 1. MBMG-7104/ ITHS-2202/ IMAS-3101/ IMHS-3101 @Ravindra Nath Shukla (PhD Scholar) ABV-IIITM
  • 2. Learning Objectives In this chapter, you learn:  To construct and interpret confidence interval estimates for the mean and the proportion  How to determine the sample size necessary to develop a confidence interval estimate for the mean or proportion  How to use confidence interval estimates in auditing Chap 8-2
  • 3. Chapter Outline  Confidence Intervals for the Population Mean, μ  when Population Standard Deviation σ is Known  when Population Standard Deviation σ is Unknown  Confidence Intervals for the Population Proportion, π  Determining the Required Sample Size Chap 8-3
  • 4. Point and Interval Estimates  A point estimate is a single number,  a confidence interval provides additional information about the variability of the estimate Chap 8-4 Point Estimate Lower Confidence Limit Upper Confidence Limit Width of confidence interval
  • 5. Point Estimates Chap 8-5 We can estimate a Population Parameter … with a Sample Statistic (a Point Estimate) Mean Proportion p π X μ
  • 6. Confidence Intervals  How much uncertainty is associated with a point estimate of a population parameter?  An interval estimate provides more information about a population characteristic than does a point estimate.  Such interval estimates are called confidence intervals. Chap 8-6 Chap 8-6
  • 7. Confidence Interval Estimate  An interval gives a range of values: Takes into consideration variation in sample statistics from sample to sample Based on observations from 1 sample Gives information about closeness to unknown population parameters Stated in terms of level of confidence e.g. 95% confident, 99% confident Can never be 100% confident Chap 8-7 Chap 8-7
  • 8. Confidence Interval Example Cereal fill example  Population has µ = 368 and σ = 15.  If you take a sample of size n = 25 you know  368 ± 1.96 * 15 / = (362.12, 373.88) contains 95% of the sample means  When you don’t know µ, you use X to estimate µ  If X = 362.3 the interval is 362.3 ± 1.96 * 15 / = (356.42, 368.18)  Since 356.42 ≤ µ ≤ 368.18 the interval based on this sample makes a correct statement about µ. But what about the intervals from other possible samples of size 25? 25 25 Chap 8-8 Chap 8-8
  • 9. Confidence Interval Example Sample # X Lower Limit Upper Limit Contain µ? 1 362.30 356.42 368.18 Yes 2 369.50 363.62 375.38 Yes 3 360.00 354.12 365.88 No 4 362.12 356.24 368.00 Yes 5 373.88 368.00 379.76 Yes Chap 8-9 Chap 8-9 (continued)
  • 10. Confidence Interval Example  In practice you only take one sample of size n  In practice you do not know µ so you do not know if the interval actually contains µ  However, you do know that 95% of the intervals formed in this manner will contain µ  Thus, based on the one sample you actually selected, you can be 95% confident your interval will contain µ (this is a 95% confidence interval) Chap 8-10 (continued) Note: 95% confidence is based on the fact that we used Z = 1.96.
  • 11. Estimation Process Chap 8-11 Chap 8-11 (mean, μ, is unknown) Population Random Sample Mean X = 50 Sample I am 95% confident that μ is between 40 & 60.
  • 12. General Formula  The general formula for all confidence intervals is: Chap 8-12 Chap 8-12 Point Estimate ± (Critical Value)(Standard Error) Where: •Point Estimate is the sample statistic estimating the population parameter of interest •Critical Value is a table value based on the sampling distribution of the point estimate and the desired confidence level •Standard Error is the standard deviation of the point estimate
  • 13. Confidence Level  Confidence Level Confidence the interval will contain the unknown population parameter A percentage (less than 100%) Chap 8-13 Chap 8-13
  • 14. Confidence Level, (1-)  Suppose confidence level = 95%  Also written (1 - ) = 0.95, (so  = 0.05)  A relative frequency interpretation:  95% of all the confidence intervals that can be constructed will contain the unknown true parameter  A specific interval either will contain or will not contain the true parameter  No probability involved in a specific interval Chap 8-14 Chap 8-14 (continued)
  • 15. Confidence Intervals Chap 8-15 Chap 8-15 Population Mean σ Unknown Confidence Intervals Population Proportion σ Known
  • 16. Confidence Interval for μ (σ Known)  Assumptions  Population standard deviation σ is known  Population is normally distributed  If population is not normal, use large sample  Confidence interval estimate: where is the point estimate Zα/2 is the normal distribution critical value for a probability of /2 in each tail is the standard error Chap 8-16 Chap 8-16 n σ /2 Z X α  X n σ/
  • 17. Finding the Critical Value, Zα/2  Consider a 95% confidence interval: Chap 8-17 Chap 8-17 Zα/2 = -1.96 Zα/2 = 1.96 0.05 so 0.95 1    α α 0.025 2  α 0.025 2  α Point Estimate Lower Confidence Limit Upper Confidence Limit Z units: X units: Point Estimate 0 1.96 /2 Z   α
  • 18. Common Levels of Confidence  Commonly used confidence levels are 90%, 95%, and 99% Chap 8-18 Chap 8-18 Confidence Level Confidence Coefficient, Zα/2 value 1.28 1.645 1.96 2.33 2.58 3.08 3.27 0.80 0.90 0.95 0.98 0.99 0.998 0.999 80% 90% 95% 98% 99% 99.8% 99.9%   1
  • 19. Intervals and Level of Confidence Chap 8-19 Chap 8-19 μ μx  Confidence Intervals Intervals extend from to (1-)x100% of intervals constructed contain μ; ()x100% do not. Sampling Distribution of the Mean n σ 2 / α Z X  n σ 2 / α Z X  x x1 x2 /2  /2    1
  • 20. Example  A sample of 11 circuits from a large normal population has a mean resistance of 2.20 ohms. We know from past testing that the population standard deviation is 0.35 ohms.  Determine a 95% confidence interval for the true mean resistance of the population. Chap 8-20 Chap 8-20
  • 21. Example  A sample of 11 circuits from a large normal population has a mean resistance of 2.20 ohms. We know from past testing that the population standard deviation is 0.35 ohms.  Solution: Chap 8-21 Chap 8-21 2.4068 1.9932 0.2068 2.20 ) 11 (0.35/ 1.96 2.20 n σ /2 Z X        μ α (continued)
  • 22. Interpretation  We are 95% confident that the true mean resistance is between 1.9932 and 2.4068 ohms  Although the true mean may or may not be in this interval, 95% of intervals formed in this manner will contain the true mean Chap 8-22 Chap 8-22
  • 23. Confidence Intervals Chap 8-23 Chap 8-23 Population Mean σ Unknown Confidence Intervals Population Proportion σ Known
  • 24. Do You Ever Truly Know σ?  Probably not!  In virtually all real world business situations, σ is not known.  If there is a situation where σ is known, then µ is also known (since to calculate σ you need to know µ.)  If you truly know µ there would be no need to gather a sample to estimate it. Chap 8-24 Chap 8-24
  • 25. Confidence Interval for μ (σ Unknown)  If the population standard deviation σ is unknown, we can substitute the sample standard deviation, S  This introduces extra uncertainty, since S is variable from sample to sample  So we use the t distribution instead of the normal distribution Chap 8-25 Chap 8-25
  • 26. Confidence Interval for μ (σ Unknown)  Assumptions  Population standard deviation is unknown  Population is normally distributed  If population is not normal, use large sample  Use Student’s t Distribution  Confidence Interval Estimate: (where tα/2 is the critical value of the t distribution with n -1 degrees of freedom and an area of α/2 in each tail) Chap 8-26 n S t X 2 / α  (continued)
  • 27. Student’s t Distribution  The t is a family of distributions  The tα/2 value depends on degrees of freedom (d.f.)  Number of observations that are free to vary after sample mean has been calculated d.f. = n - 1 Chap 8-27
  • 28. Degrees of Freedom (df) Idea: Number of observations that are free to vary after sample mean has been calculated Example: Suppose the mean of 3 numbers is 8.0 Let X1 = 7 Let X2 = 8 What is X3? Chap 8-28 If the mean of these three values is 8.0, then X3 must be 9 (i.e., X3 is not free to vary) Here, n = 3, so degrees of freedom = n – 1 = 3 – 1 = 2 (2 values can be any numbers, but the third is not free to vary for a given mean)
  • 29. Student’s t Distribution Chap 8-29 Chap 8-29 t 0 t (df = 5) t (df = 13) t-distributions are bell- shaped and symmetric, but have ‘fatter’ tails than the normal Standard Normal (t with df = ∞) Note: t Z as n increases
  • 30. Student’s t Table Chap 8-30 Chap 8-30 Upper Tail Area df .10 .05 .025 1 3.078 6.314 12.706 2 1.886 3 1.638 2.353 3.182 t 0 2.920 The body of the table contains t values, not probabilities Let: n = 3 df = n - 1 = 2  = 0.10 /2 = 0.05 /2 = 0.05 4.303 2.920
  • 31. Selected t distribution values Chap 8-31 With comparison to the Z value Confidence t t t Z Level (10 d.f.) (20 d.f.) (30 d.f.) (∞ d.f.) 0.80 1.372 1.325 1.310 1.28 0.90 1.812 1.725 1.697 1.645 0.95 2.228 2.086 2.042 1.96 0.99 3.169 2.845 2.750 2.58 Note: t Z as n increases
  • 32. Example of t distribution confidence interval A random sample of n = 25 has X = 50 and S = 8. Form a 95% confidence interval for μ  d.f. = n – 1 = 24, so The confidence interval is Chap 8-32 Chap 8-32 2.0639 0.025 t /2   α t 25 8 (2.0639) 50 n S /2    α t X 46.698 ≤ μ ≤ 53.302
  • 33. Example of t distribution confidence interval  Interpreting this interval requires the assumption that the population you are sampling from is approximately a normal distribution (especially since n is only 25).  This condition can be checked by creating a:  Normal probability plot or  Boxplot Chap 8-33 Chap 8-33 (continued)
  • 34. Example 2  An online grocery store is interested in estimating the basket size (number of items ordered by the customer) of its customer order so that it can optimize its size of crates used for delivering the grocery items. From a sample of 81 customers, the average basket size was estimated as 24 and the standard deviation estimated from the sample was 3.8. Calculate the 95% confidence interval for the basket size of the customer order. Chap 8-34
  • 35. Confidence Intervals Chap 8-35 Population Mean σ Unknown Confidence Intervals Population Proportion σ Known
  • 36. Confidence Intervals for the Population Proportion, π  An interval estimate for the population proportion ( π ) can be calculated by adding an allowance for uncertainty to the sample proportion ( p ) Chap 8-36 Chap 8-36
  • 37. Confidence Intervals for the Population Proportion, π  Recall that the distribution of the sample proportion is approximately normal if the sample size is large, with standard deviation  We will estimate this with sample data Chap 8-37 Chap 8-37 (continued) n p) p(1 n ) (1 σp    
  • 38. Confidence Interval Endpoints  Upper and lower confidence limits for the population proportion are calculated with the formula  where  Zα/2 is the standard normal value for the level of confidence desired  p is the sample proportion  n is the sample size  Note: must have X > 5 and n – X > 5 Chap 8-38 Chap 8-38 n p) p(1 /2 Z p   α
  • 39. Example  A random sample of 100 people shows that 25 are left-handed.  Form a 95% confidence interval for the true proportion of left-handers Chap 8-39 Chap 8-39
  • 40. Example  A random sample of 100 people shows that 25 are left-handed. Form a 95% confidence interval for the true proportion of left- handers. Chap 8-40 /100 0.25(0.75) 1.96 25/100 p)/n p(1 Z p /2      0.3349 0.1651 (0.0433) 1.96 0.25      (continued) np = 100 * 0.25 = 25 > 5 & n(1-p) = 100 * 0.75 = 75 > 5 Make sure the sample is big enough
  • 41. Interpretation  We are 95% confident that the true percentage of left-handers in the population is between 16.51% and 33.49%.  Although the interval from 0.1651 to 0.3349 may or may not contain the true proportion, 95% of intervals formed from samples of size 100 in this manner will contain the true proportion. Chap 8-41 Chap 8-41
  • 42. Example 2  A retail store was interested in finding the proportion of customers who pay through cash (as against credit or debit card) for the merchandize they buy at the store. From a sample of 100 customers, it was found that 70 customers paid by cash. Calculate the 95% confidence interval for proportion of customers who pay by cash. Chap 8-42
  • 43. Determining Sample Size Chap 8-43 Chap 8-43 For the Mean Determining Sample Size For the Proportion
  • 44. Sampling Error  The required sample size can be found to obtain a desired margin of error (e) with a specified level of confidence (1 - )  The margin of error is also called sampling error  the amount of imprecision in the estimate of the population parameter  the amount added and subtracted to the point estimate to form the confidence interval Chap 8-44 Chap 8-44
  • 45. Determining Sample Size Chap 8-45 Chap 8-45 For the Mean Determining Sample Size n σ 2 / α Z X  n σ 2 / α Z e  Sampling error (margin of error)
  • 46. Determining Sample Size Chap 8-46 Chap 8-46 For the Mean Determining Sample Size n σ 2 /  Z e  (continued) 2 2 2 2 / σ e Z n   Now solve for n to get
  • 47. Determining Sample Size  To determine the required sample size for the mean, you must know:  The desired level of confidence (1 - ), which determines the critical value, Zα/2  The acceptable sampling error, e  The standard deviation, σ Chap 8-47 Chap 8-47 (continued)
  • 48. Required Sample Size Example If  = 45, what sample size is needed to estimate the mean within ± 5 with 90% confidence? Chap 8-48 Chap 8-48 (Always round up) 219.19 5 (45) (1.645) e σ Z n 2 2 2 2 2 2    So the required sample size is n = 220
  • 49. Determining Sample Size Chap 8-49 Determining Sample Size For the Proportion 2 2 e ) (1 Z n π π   Now solve for n to get n ) (1 Z e π π   (continued)
  • 50. Determining Sample Size  To determine the required sample size for the proportion, you must know:  The desired level of confidence (1 - ), which determines the critical value, Zα/2  The acceptable sampling error, e  The true proportion of events of interest, π  π can be estimated with a pilot sample if necessary (or conservatively use 0.5 as an estimate of π) Chap 8-50 Chap 8-50 (continued)
  • 51. Required Sample Size Example How large a sample would be necessary to estimate the true proportion defective in a large population within ±3%, with 95% confidence? (Assume a pilot sample yields p = 0.12) Chap 8-51 Chap 8-51
  • 52. Required Sample Size Example Chap 8-52 Chap 8-52 Solution: For 95% confidence, use Zα/2 = 1.96 e = 0.03 p = 0.12, so use this to estimate π So use n = 451 450.74 (0.03) 0.12) (0.12)(1 (1.96) e ) (1 Z n 2 2 2 2 /2      π π  (continued)
  • 53. Applications in Auditing  Six advantages of statistical sampling in auditing  Sampling is less time consuming and less costly  Sampling provides an objective way to calculate the sample size in advance  Sampling provides results that are objective and defensible  Because the sample size is based on demonstrable statistical principles, the audit is defensible before one’s superiors and in a court of law Chap 8-53
  • 54. Applications in Auditing  Sampling provides an estimate of the sampling error  Allows auditors to generalize their findings to the population with a known sampling error  Can provide more accurate conclusions about the population  Sampling is often more accurate for drawing conclusions about large populations  Examining every item in a large population is subject to significant non-sampling error  Sampling allows auditors to combine, and then evaluate collectively, samples collected by different individuals Chap 8-54 Chap 8-54 (continued)
  • 55. Confidence Interval for Population Total Amount  Point estimate for a population of size N:  Confidence interval estimate: Chap 8-55 X N total Population  1 n S ) 2 / (    N n N t N X N α (This is sampling without replacement, so use the finite population correction factor in the confidence interval formula)
  • 56. Confidence Interval for Population Total: Example Chap 8-56 Chap 8-56 A firm has a population of 1,000 accounts and wishes to estimate the total population value. A sample of 80 accounts is selected with average balance of $87.6 and standard deviation of $22.3. Construct the 95% confidence interval estimate of the total balance.
  • 57. Example Solution Chap 8-57 Chap 8-57 The 95% confidence interval for the population total balance is $82,837.52 to $92,362.48 /2 S ( ) 1 n 22.3 1,000 80 (1,000)(87.6) (1,000)(1.9905) 1,000 1 80 87,600 4,762.48 N n N X N t N           22.3 S , 6 . 87 X 80, n , 1000 N    
  • 58. Confidence Interval for Total Difference  Point estimate for a population of size N:  Where the average difference, D, is: Chap 8-58 Chap 8-58 D N Difference Total  value original - value audited D where n D D i n 1 i i    
  • 59. Confidence Interval for Total Difference  Confidence interval estimate: where Chap 8-59 Chap 8-59 1 n S ) ( D 2 /    N n N t N D N  (continued) 1 ) D ( 1 2      n D S n i i D
  • 60. One-Sided Confidence Intervals  Application: find the upper bound for the proportion of items that do not conform with internal controls  where  Zα is the standard normal value for the level of confidence desired  p is the sample proportion of items that do not conform  n is the sample size  N is the population size Chap 8-60 Chap 8-60 1 N n N n p) p(1 Z p bound Upper      α
  • 61. Ethical Issues  A confidence interval estimate (reflecting sampling error) should always be included when reporting a point estimate  The level of confidence should always be reported  The sample size should be reported  An interpretation of the confidence interval estimate should also be provided Chap 8-61 Chap 8-61
  • 62. Chapter Summary  Introduced the concept of confidence intervals  Discussed point estimates  Developed confidence interval estimates  Created confidence interval estimates for the mean (σ known)  Determined confidence interval estimates for the mean (σ unknown)  Created confidence interval estimates for the proportion  Determined required sample size for mean and proportion confidence interval estimates with a desired margin of error Chap 8-62 Chap 8-62
  • 63. Chapter Summary  Developed applications of confidence interval estimation in auditing  Confidence interval estimation for population total  Confidence interval estimation for total difference in the population  One-sided confidence intervals for the proportion nonconforming  Addressed confidence interval estimation and ethical issues Chap 8-63 Chap 8-63 (continued)
  • 64. Estimation & Sample Size Determination For Finite Populations Chap 8-64 Basic Business Statistics
  • 65. Topic Learning Objectives In this topic, you learn:  When to use a finite population correction factor in calculating a confidence interval for either µ or π  How to use a finite population correction factor in calculating a confidence interval for either µ or π  How to use a finite population correction factor in calculating a sample size for a confidence interval for either µ or π Chap 8-65
  • 66. Use A fpc When Sampling More Than 5% Of The Population (n/N > 0.05) Chap 8-66 Confidence Interval For µ with a fpc / 2 1 S N n X t N n     Confidence Interval For π with a fpc 1 ) 1 ( 2 /     N n N n p p z p  A fpc simply reduces the standard error of either the sample mean or the sample proportion
  • 67. Confidence Interval for µ with a fpc Chap 8-67 10 s 50, X 100, n 1000, N Suppose     ) 88 . 51 , 12 . 48 ( 88 . 1 50 1 1000 100 1000 100 10 984 . 1 50 1 : for CI 95% 2 /           N n N n s t X  
  • 68. Determining Sample Size with a fpc  Calculate the sample size (n0) without a fpc  For µ:  For π:  Apply the fpc utilizing the following formula to arrive at the final sample size (n).  n = n0N / (n0 + (N-1)) Chap 8-68 2 2 /2 0 2 Z n e    2 / 2 0 2 (1 ) Z n e     
  • 69. Topic Summary  Described when to use a finite population correction in calculating a confidence interval for either µ or π  Examined the formulas for calculating a confidence interval for either µ or π utilizing a finite population correction  Examined the formulas for calculating a sample size in a confidence interval for either µ or π