Numerical Descriptive Measures

Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 3-1
Chapter 3
Numerical Descriptive Measures
Statistics for Managers
Using Microsoft®
Excel
4th
Edition

Statistics for Managers Using
Microsoft Excel, 4e © 2004
Prentice-Hall, Inc. Chap 3-2
After completing this chapter, you should be able to:
 Compute and interpret the mean, median, and mode for a
set of data
 Find the range, variance, standard deviation, and
coefficient of variation and know what these values mean
 Apply the empirical rule and the Bienaymé - Chebshev rule
to describe the variation of population values around the
mean
 Construct and interpret a box-and-whiskers plot
 Compute and explain the correlation coefficient
 Use numerical measures along with graphs, charts, and
tables to describe data
Chapter Goals

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
 Symmetric and skewed distributions
 Population summary measures
 Mean, variance, and standard deviation
 The empirical rule and Bienaymé - Chebyshev rule

Chapter Topics
 Five number summary and box-and-whisker
plots
 Covariance and coefficient of correlation
 Pitfalls in numerical descriptive measures and
ethical considerations
(continued)

Summary Measures
Arithmetic Mean
Median
Mode
Describing Data Numerically
Variance
Standard Deviation
Coefficient of Variation
Range
Inter - quartile Range
Geometric Mean
Skewness
Central Tendency Variation ShapeQuartiles

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

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

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
==
++++

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

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 +

Mode
 A measure of central tendency
 Value that occurs most often
 Not affected by extreme values
 Used for either numerical or categorical 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

 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

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

 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”?

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 −+××+×+= 

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.

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)

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

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

(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

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.

Range
 Simplest measure of variation
 Difference between the largest and the smallest
observations:
Range = Xlargest – Xsmallest
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Range = 14 - 1 = 13
Example:

 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

Inter - quartile Range
 Can eliminate some outlier problems by using
the inter - quartile range
 Eliminate some high- and low-valued
observations and calculate the range from the
remaining values
 Interquartile range = 3rd
quartile – 1st
quartile
= Q3 – Q1

Interquartile Range
Median
(Q2)
X
maximumX
minimum Q1 Q3
Example:
25% 25% 25% 25%
12 30 45 57 70
Interquartile range
= 57 – 30 = 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 = arithmetic mean
n = sample size
Xi = ith
value of the variable X
X

Standard Deviation
 Most commonly used measure of variation
 Shows variation about the mean
 Has the same units as the original data
 Sample standard deviation:
1-n
)X(X
S
n
1i
2
i∑=
−
=

Calculation Example:
Sample Standard Deviation
Sample
Data (Xi) : 10 12 14 15 17 18 18 24
n = 8 Mean = X = 16
4.2426
7
126
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

Measuring variation
Small standard deviation
Large standard deviation

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 = 4.570
Data C

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)

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 ⋅







=

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 =⋅=⋅







=

Shape of a Distribution
 Describes how data is distributed
 Measures of shape
 Symmetric or skewed
Mean = MedianMean < Median Median < Mean
Right-SkewedLeft-Skewed Symmetric

Using Microsoft Excel
 Descriptive Statistics can be obtained
from Microsoft®
Excel
 Use menu choice:
tools / data analysis / descriptive statistics
 Enter details in dialog box

Using Excel
Use menu choice:
tools / data analysis /
descriptive statistics

 Enter dialog box
details
 Check box for
summary statistics
 Click OK
Using Excel
(continued)

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

Population Summary Measures
 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
Where

 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

Population Standard Deviation
 Most commonly used measure of variation
 Shows variation about the mean
 Has the same units as the original data
 Population standard deviation:
N
μ)(X
σ
N
1i
2
i∑=
−
=

 If the data distribution is bell-shaped, then
the interval:
 contains about 68% of the values in
the population or the sample
The Empirical Rule
1σμ ±
μ
68%
1σμ ±

 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 ±

 Regardless of how the data are distributed,
at least (1 - 1/k2
) of the values will fall within
k standard deviations of the mean (for k > 1)
 Examples:
(1 - 1/12
) = 0% ……..... k=1 (μ ± 1σ)
(1 - 1/22
) = 75% …........ k=2 (μ ± 2σ)
(1 - 1/32
) = 89% ………. k=3 (μ ± 3σ)
Bienaymé - Chebyshev Rule
withinAt least

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%

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

Distribution Shape and
Box-and-Whisker Plot
Right-SkewedLeft-Skewed Symmetric
Q1 Q2 Q3 Q1 Q2 Q3 Q1 Q2 Q3

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
 This data is right skewed, as the plot depicts
0 2 3 5 270 2 3 5 27
Min Q1 Q2 Q3 Max

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
−
−−
=
∑=

 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

Coefficient of Correlation
 Measures the relative strength of the linear
relationship between two variables
 Sample coefficient of correlation:
YX
n
1i
2
i
n
1i
2
i
n
1i
ii
SS
)Y,X(cov
)YY()XX(
)YY)(XX(
r =
−−
−−
=
∑∑
∑
==
=

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 any positive linear
relationship

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

Using Excel to Find
the Correlation Coefficient
 Select
Tools/Data Analysis
 Choose Correlation from
the selection menu
 Click OK . . .

Using Excel to Find
the Correlation Coefficient
 Input data range and select
appropriate options
 Click OK to get output
(continued)

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
Scatter Plot of Test Scores
70
75
80
85
90
95
100
70 75 80 85 90 95 100
Test #1 Score
Test#2Score

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

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

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
 Illustrated shape of distribution
 Symmetric, skewed, box-and-whisker plots

Chapter Summary
 Discussed covariance and correlation
coefficient
 Addressed pitfalls in numerical descriptive
measures and ethical considerations
(continued)

Numerical Descriptive Measures

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