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Numerical Descriptive Measures for Basic Business Statistics Data
- 1. Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc.. Chap 3-1
Chapter 3
Numerical Descriptive Measures
Basic Business Statistics
10th
Edition
- 2. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-2
In this chapter, you learn:
To describe the properties of central tendency,
variation, and shape in numerical data
To calculate descriptive summary measures for a
population
To calculate the coefficient of variation and Z-
scores
To construct and interpret a box-and-whisker plot
To calculate the covariance and the coefficient of
correlation
Learning Objectives
- 3. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-3
Chapter Topics
Measures of central tendency, variation, and
shape
Mean, median, mode, geometric mean
Quartiles
Range, interquartile range, variance and standard
deviation, coefficient of variation, Z-scores
Symmetric and skewed distributions
Population summary measures
Mean, variance, and standard deviation
The empirical rule and Chebyshev rule
- 4. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-4
Chapter Topics
Five number summary and box-and-whisker
plot
Covariance and coefficient of correlation
Pitfalls in numerical descriptive measures and
ethical issues
(continued)
- 5. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-5
Summary Measures
Arithmetic Mean
Median
Mode
Describing Data Numerically
Variance
Standard Deviation
Coefficient of Variation
Range
Interquartile Range
Geometric Mean
Skewness
Central Tendency Variation ShapeQuartiles
- 6. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-6
Measures of Central Tendency
Central Tendency
Arithmetic Mean Median Mode Geometric Mean
n
X
X
n
i
i∑=
= 1
n/1
n21G )XXX(X ×××=
Overview
Midpoint of
ranked
values
Most
frequently
observed
value
- 7. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-7
Arithmetic Mean
The arithmetic mean (mean) is the most
common measure of central tendency
For a sample of size n:
Sample size
n
XXX
n
X
X n21
n
1i
i
+++
==
∑=
Observed values
- 8. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-8
Arithmetic Mean
The most common measure of central tendency
Mean = sum of values divided by the number of values
Affected by extreme values (outliers)
(continued)
0 1 2 3 4 5 6 7 8 9 10
Mean = 3
0 1 2 3 4 5 6 7 8 9 10
Mean = 4
3
5
15
5
54321
==
++++
4
5
20
5
104321
==
++++
- 9. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-9
Median
In an ordered array, the median is the “middle”
number (50% above, 50% below)
Not affected by extreme values
0 1 2 3 4 5 6 7 8 9 10
Median = 3
0 1 2 3 4 5 6 7 8 9 10
Median = 3
- 10. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-10
Finding the Median
The location of the median:
If the number of values is odd, the median is the middle number
If the number of values is even, the median is the average of
the two middle numbers
Note that is not the value of the median, only the
position of the median in the ranked data
dataorderedtheinposition
2
1n
positionMedian
+
=
2
1n +
- 11. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-11
Mode
A measure of central tendency
Value that occurs most often
Not affected by extreme values
Used for either numerical or categorical
(nominal) data
There may may be no mode
There may be several modes
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Mode = 9
0 1 2 3 4 5 6
No Mode
- 12. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-12
Five houses on a hill by the beach
Review Example
$2,000 K
$500 K
$300 K
$100 K
$100 K
House Prices:
$2,000,000
500,000
300,000
100,000
100,000
- 13. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-13
Review Example:
Summary Statistics
Mean: ($3,000,000/5)
= $600,000
Median: middle value of ranked data
= $300,000
Mode: most frequent value
= $100,000
House Prices:
$2,000,000
500,000
300,000
100,000
100,000
Sum $3,000,000
- 14. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-14
Mean is generally used, unless
extreme values (outliers) exist
Then median is often used, since
the median is not sensitive to
extreme values.
Example: Median home prices may be
reported for a region – less sensitive to
outliers
Which measure of location
is the “best”?
- 15. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-15
Quartiles
Quartiles split the ranked data into 4 segments with
an equal number of values per segment
25% 25% 25% 25%
The first quartile, Q1, is the value for which 25% of the
observations are smaller and 75% are larger
Q2 is the same as the median (50% are smaller, 50% are
larger)
Only 25% of the observations are greater than the third
quartile
Q1 Q2 Q3
- 16. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-16
Quartile Formulas
Find a quartile by determining the value in the
appropriate position in the ranked data, where
First quartile position: Q1 = (n+1)/4
Second quartile position: Q2 = (n+1)/2 (the median position)
Third quartile position: Q3 = 3(n+1)/4
where n is the number of observed values
- 17. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-17
(n = 9)
Q1 is in the (9+1)/4 = 2.5 position of the ranked data
so use the value half way between the 2nd
and 3rd
values,
so Q1 = 12.5
Quartiles
Sample Data in Ordered Array: 11 12 13 16 16 17 18 21 22
Example: Find the first quartile
Q1 and Q3 are measures of noncentral location
Q2 = median, a measure of central tendency
- 18. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-18
(n = 9)
Q1 is in the (9+1)/4 = 2.5 position of the ranked data,
so Q1 = 12.5
Q2 is in the (9+1)/2 = 5th
position of the ranked data,
so Q2 = median = 16
Q3 is in the 3(9+1)/4 = 7.5 position of the ranked data,
so Q3 = 19.5
Quartiles
Sample Data in Ordered Array: 11 12 13 16 16 17 18 21 22
Example:
(continued)
- 19. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-19
Geometric Mean
Geometric mean
Used to measure the rate of change of a variable
over time
Geometric mean rate of return
Measures the status of an investment over time
Where Ri is the rate of return in time period i
n/1
n21G )XXX(X ×××=
1)]R1()R1()R1[(R n/1
n21G −+××+×+=
- 20. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-20
Example
An investment of $100,000 declined to $50,000 at the
end of year one and rebounded to $100,000 at end
of year two:
000,100$X000,50$X000,100$X 321 ===
50% decrease 100% increase
The overall two-year return is zero, since it started and
ended at the same level.
- 21. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-21
Example
Use the 1-year returns to compute the arithmetic
mean and the geometric mean:
%0111)]2()50[(.
1%))]100(1(%))50(1[(
1)]R1()R1()R1[(R
2/12/1
2/1
n/1
n21G
=−=−×=
−+×−+=
−+××+×+=
%25
2
%)100(%)50(
X =
+−
=
Arithmetic
mean rate
of return:
Geometric
mean rate
of return:
Misleading result
More
accurate
result
(continued)
- 22. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-22
Same center,
different variation
Measures of Variation
Variation
Variance Standard
Deviation
Coefficient
of Variation
Range Interquartile
Range
Measures of variation give
information on the spread or
variability of the data
values.
- 23. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-23
Range
Simplest measure of variation
Difference between the largest and the smallest
values in a set of data:
Range = Xlargest – Xsmallest
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Range = 14 - 1 = 13
Example:
- 24. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-24
Ignores the way in which data are distributed
Sensitive to outliers
7 8 9 10 11 12
Range = 12 - 7 = 5
7 8 9 10 11 12
Range = 12 - 7 = 5
Disadvantages of the Range
1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,3,3,3,3,4,5
1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,3,3,3,3,4,120
Range = 5 - 1 = 4
Range = 120 - 1 = 119
- 25. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-25
Interquartile Range
Can eliminate some outlier problems by using
the interquartile range
Eliminate some high- and low-valued
observations and calculate the range from the
remaining values
Interquartile range = 3rd
quartile – 1st
quartile
= Q3 – Q1
- 26. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-26
Interquartile Range
Median
(Q2)
X
maximumX
minimum Q1 Q3
Example:
25% 25% 25% 25%
12 30 45 57 70
Interquartile range
= 57 – 30 = 27
- 27. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-27
Average (approximately) of squared deviations
of values from the mean
Sample variance:
Variance
1-n
)X(X
S
n
1i
2
i
2
∑=
−
=
Where = mean
n = sample size
Xi = ith
value of the variable X
X
- 28. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-28
Standard Deviation
Most commonly used measure of variation
Shows variation about the mean
Is the square root of the variance
Has the same units as the original data
Sample standard deviation:
1-n
)X(X
S
n
1i
2
i∑=
−
=
- 29. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-29
Calculation Example:
Sample Standard Deviation
Sample
Data (Xi) : 10 12 14 15 17 18 18 24
n = 8 Mean = X = 16
4.3095
7
130
18
16)(2416)(1416)(1216)(10
1n
)X(24)X(14)X(12)X(10
S
2222
2222
==
−
−++−+−+−
=
−
−++−+−+−
=
A measure of the “average”
scatter around the mean
- 30. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-30
Measuring variation
Small standard deviation
Large standard deviation
- 31. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-31
Comparing Standard Deviations
Mean = 15.5
S = 3.33811 12 13 14 15 16 17 18 19 20 21
11 12 13 14 15 16 17 18 19 20 21
Data B
Data A
Mean = 15.5
S = 0.926
11 12 13 14 15 16 17 18 19 20 21
Mean = 15.5
S = 4.567
Data C
- 32. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-32
Advantages of Variance and
Standard Deviation
Each value in the data set is used in the
calculation
Values far from the mean are given extra
weight
(because deviations from the mean are squared)
- 33. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-33
Coefficient of Variation
Measures relative variation
Always in percentage (%)
Shows variation relative to mean
Can be used to compare two or more sets of
data measured in different units
100%
X
S
CV ⋅
=
- 34. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-34
Comparing Coefficient
of Variation
Stock A:
Average price last year = $50
Standard deviation = $5
Stock B:
Average price last year = $100
Standard deviation = $5
Both stocks
have the same
standard
deviation, but
stock B is less
variable relative
to its price
10%100%
$50
$5
100%
X
S
CVA =⋅=⋅
=
5%100%
$100
$5
100%
X
S
CVB =⋅=⋅
=
- 35. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-35
Z Scores
A measure of distance from the mean (for example, a
Z-score of 2.0 means that a value is 2.0 standard
deviations from the mean)
The difference between a value and the mean, divided
by the standard deviation
A Z score above 3.0 or below -3.0 is considered an
outlier
S
XX
Z
−
=
- 36. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-36
Z Scores
Example:
If the mean is 14.0 and the standard deviation is 3.0,
what is the Z score for the value 18.5?
The value 18.5 is 1.5 standard deviations above the
mean
(A negative Z-score would mean that a value is less
than the mean)
1.5
3.0
14.018.5
S
XX
Z =
−
=
−
=
(continued)
- 37. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-37
Shape of a Distribution
Describes how data are distributed
Measures of shape
Symmetric or skewed
Mean = MedianMean < Median Median < Mean
Right-SkewedLeft-Skewed Symmetric
- 38. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-38
Using Microsoft Excel
Descriptive Statistics can be obtained
from Microsoft®
Excel
Use menu choice:
tools / data analysis / descriptive statistics
Enter details in dialog box
- 39. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-39
Using Excel
Use menu choice:
tools / data analysis /
descriptive statistics
- 40. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-40
Enter dialog box
details
Check box for
summary statistics
Click OK
Using Excel
(continued)
- 41. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-41
Excel output
Microsoft Excel
descriptive statistics output,
using the house price data:
House Prices:
$2,000,000
500,000
300,000
100,000
100,000
- 42. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-42
Numerical Measures
for a Population
Population summary measures are called parameters
The population mean is the sum of the values in the
population divided by the population size, N
N
XXX
N
X
N21
N
1i
i
+++
==µ
∑=
μ = population mean
N = population size
Xi = ith
value of the variable X
Where
- 43. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-43
Average of squared deviations of values from
the mean
Population variance:
Population Variance
N
μ)(X
σ
N
1i
2
i
2
∑=
−
=
Where μ = population mean
N = population size
Xi = ith
value of the variable X
- 44. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-44
Population Standard Deviation
Most commonly used measure of variation
Shows variation about the mean
Is the square root of the population variance
Has the same units as the original data
Population standard deviation:
N
μ)(X
σ
N
1i
2
i∑=
−
=
- 45. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-45
If the data distribution is approximately
bell-shaped, then the interval:
contains about 68% of the values in
the population or the sample
The Empirical Rule
1σμ ±
μ
68%
1σμ ±
- 46. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-46
contains about 95% of the values in
the population or the sample
contains about 99.7% of the values
in the population or the sample
The Empirical Rule
2σμ ±
3σμ ±
3σμ ±
99.7%95%
2σμ ±
- 47. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-47
Regardless of how the data are distributed,
at least (1 - 1/k2
) x 100% of the values will
fall within k standard deviations of the mean
(for k > 1)
Examples:
(1 - 1/12
) x 100% = 0% ……..... k=1 (μ ± 1σ)
(1 - 1/22
) x 100% = 75% …........ k=2 (μ ± 2σ)
(1 - 1/32
) x 100% = 89% ………. k=3 (μ ± 3σ)
Chebyshev Rule
withinAt least
- 48. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-48
Approximating the Mean from a
Frequency Distribution
Sometimes only a frequency distribution is available, not
the raw data
Use the midpoint of a class interval to approximate the
values in that class
Where n = number of values or sample size
c = number of classes in the frequency distribution
mj = midpoint of the jth
class
fj = number of values in the jth
class
n
fm
X
c
1j
jj∑=
=
- 49. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-49
Approximating the Standard Deviation
from a Frequency Distribution
Assume that all values within each class interval
are located at the midpoint of the class
Approximation for the standard deviation from a
frequency distribution:
1-n
f)X(m
S
c
1j
j
2
j∑=
−
=
- 50. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-50
Exploratory Data Analysis
Box-and-Whisker Plot: A Graphical display of
data using 5-number summary:
Minimum -- Q1 -- Median -- Q3 -- Maximum
Example:
Minimum 1st Median 3rd Maximum
Quartile Quartile
Minimum 1st Median 3rd Maximum
Quartile Quartile
25% 25% 25% 25%
- 51. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-51
Shape of Box-and-Whisker Plots
The Box and central line are centered between the
endpoints if data are symmetric around the median
A Box-and-Whisker plot can be shown in either vertical
or horizontal format
Min Q1 Median Q3 Max
- 52. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-52
Distribution Shape and
Box-and-Whisker Plot
Right-SkewedLeft-Skewed Symmetric
Q1 Q2 Q3 Q1 Q2 Q3 Q1 Q2 Q3
- 53. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-53
Box-and-Whisker Plot Example
Below is a Box-and-Whisker plot for the following
data:
0 2 2 2 3 3 4 5 5 10 27
The data are right skewed, as the plot depicts
0 2 3 5 270 2 3 5 27
Min Q1 Q2 Q3 Max
- 54. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-54
The Sample Covariance
The sample covariance measures the strength of the
linear relationship between two variables (called
bivariate data)
The sample covariance:
Only concerned with the strength of the relationship
No causal effect is implied
1n
)YY)(XX(
)Y,X(cov
n
1i
ii
−
−−
=
∑=
- 55. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-55
Covariance between two random variables:
cov(X,Y) > 0 X and Y tend to move in the same direction
cov(X,Y) < 0 X and Y tend to move in opposite directions
cov(X,Y) = 0 X and Y are independent
Interpreting Covariance
- 56. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-56
Coefficient of Correlation
Measures the relative strength of the linear
relationship between two variables
Sample coefficient of correlation:
where
YX SS
Y),(Xcov
r =
1n
)X(X
S
n
1i
2
i
X
−
−
=
∑=
1n
)Y)(YX(X
Y),(Xcov
n
1i
ii
−
−−
=
∑=
1n
)Y(Y
S
n
1i
2
i
Y
−
−
=
∑=
- 57. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-57
Features of
Correlation Coefficient, r
Unit free
Ranges between –1 and 1
The closer to –1, the stronger the negative linear
relationship
The closer to 1, the stronger the positive linear
relationship
The closer to 0, the weaker the linear relationship
- 58. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-58
Scatter Plots of Data with Various
Correlation Coefficients
Y
X
Y
X
Y
X
Y
X
Y
X
r = -1 r = -.6 r = 0
r = +.3r = +1
Y
X
r = 0
- 59. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-59
Using Excel to Find
the Correlation Coefficient
Select
Tools/Data Analysis
Choose Correlation from
the selection menu
Click OK . . .
- 60. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-60
Using Excel to Find
the Correlation Coefficient
Input data range and select
appropriate options
Click OK to get output
(continued)
- 61. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-61
Interpreting the Result
r = .733
There is a relatively
strong positive linear
relationship between
test score #1
and test score #2
Students who scored high on the first test tended
to score high on second test, and students who
scored low on the first test tended to score low on
the second test
Scatter Plot of Test Scores
70
75
80
85
90
95
100
70 75 80 85 90 95 100
Test #1 Score
Test#2Score
- 62. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-62
Pitfalls in Numerical
Descriptive Measures
Data analysis is objective
Should report the summary measures that best meet
the assumptions about the data set
Data interpretation is subjective
Should be done in fair, neutral and clear manner
- 63. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-63
Ethical Considerations
Numerical descriptive measures:
Should document both good and bad results
Should be presented in a fair, objective and
neutral manner
Should not use inappropriate summary
measures to distort facts
- 64. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-64
Chapter Summary
Described measures of central tendency
Mean, median, mode, geometric mean
Discussed quartiles
Described measures of variation
Range, interquartile range, variance and standard
deviation, coefficient of variation, Z-scores
Illustrated shape of distribution
Symmetric, skewed, box-and-whisker plots
- 65. Basic Business Statistics, 10e ©
2006 Prentice-Hall, Inc. Chap 3-65
Chapter Summary
Discussed covariance and correlation
coefficient
Addressed pitfalls in numerical descriptive
measures and ethical considerations
(continued)