Bbs11 ppt ch03

Basic Business Statistics, 11e © 2009 Prentice-Hall, Inc. Chap 3-1
Chapter 3
Numerical Descriptive Measures
Basic Business Statistics
11th Edition

Basic Business Statistics, 11e © 2009 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 descriptive summary measures for a
frequency distribution
 To construct and interpret a boxplot
 To calculate the covariance and the coefficient of
correlation
Learning Objectives

Summary Definitions
 The central tendency is the extent to which all the
data values group around a typical or central value.
 The variation is the amount of dispersion, or
scattering, of values
 The shape is the pattern of the distribution of values
from the lowest value to the highest value.

Measures of Central Tendency:
The Mean
 The arithmetic mean (often just called “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
The ith value
Pronounced x-bar

The 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



The 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

Locating the Median
 The location of the median when the values are in numerical order
(smallest to largest):
 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 

The Mode
 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

Review Example
House Prices:
$2,000,000
$500,000
$300,000
$100,000
$100,000
Sum $3,000,000
 Mean: ($3,000,000/5)
= $600,000
 Median: middle value of ranked
data
= $300,000
 Mode: most frequent value
= $100,000

Which Measure to Choose?
 The mean is generally used, unless extreme values
(outliers) exist.
 The median is often used, since the median is not
sensitive to extreme values. For example, median
home prices may be reported for a region; it is less
sensitive to outliers.
 In some situations it makes sense to report both the
mean and the median.

Measure of Central Tendency For The Rate Of Change
Of A Variable Over Time:
The Geometric Mean & The Geometric Rate of Return
 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  

The Geometric Mean Rate of
Return: Example
An investment of $100,000 declined to $50,000 at the end of
year one and rebounded to $100,000 at end of year two:
The overall two-year return is zero, since it started and ended
at the same level.
000,100$X000,50$X000,100$X 321 
50% decrease 100% increase

The Geometric Mean Rate of
Return: Example
Use the 1-year returns to compute the arithmetic mean
and the geometric mean:
%2525.
2
)1()5.(


X
Arithmetic
mean rate
of return:
Geometric
mean rate of
return:
%012/1112/1)]2()50[(.
12/1))]1(1())5.(1[(
1/1)]1()21()11[(


 n
nRRRGR 
Misleading result
More
representative
result
(continued)

Summary
Central Tendency
Arithmetic
Mean
Median Mode Geometric Mean
n
X
X
n
i
i
 1
n/1
n21G )XXX(X  
Middle value
in the ordered
array
Most
frequently
observed
value
Rate of
change of
a variable
over time

Same center,
different variation
Measures of Variation
 Measures of variation give
information on the spread
or variability or
dispersion of the data
values.
Variation
Standard
Deviation
Coefficient
of Variation
Range Variance

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

Why The Range Can Be Misleading
 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
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

 Average (approximately) of squared deviations
of values from the mean
 Sample variance:
The 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

The 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



The Standard Deviation
Steps for Computing Standard Deviation
1. Compute the difference between each value and the
mean.
2. Square each difference.
3. Add the squared differences.
4. Divide this total by n-1 to get the sample variance.
5. Take the square root of the sample variance to get
the sample standard deviation.

Sample Standard Deviation:
Calculation Example
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

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

Comparing Standard Deviations
Smaller standard deviation
Larger standard deviation

Summary Characteristics
 The more the data are spread out, the greater the
range, variance, and standard deviation.
 The more the data are concentrated, the smaller the
range, variance, and standard deviation.
 If the values are all the same (no variation), all these
measures will be zero.
 None of these measures are ever negative.

The Coefficient of Variation
 Measures relative variation
 Always in percentage (%)
 Shows variation relative to mean
 Can be used to compare the variability of two or
more sets of data measured in different units
100%
X
S
CV 









Comparing Coefficients 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 









Locating Extreme Outliers:
Z-Score
 To compute the Z-score of a data value, subtract the
mean and divide by the standard deviation.
 The Z-score is the number of standard deviations a
data value is from the mean.
 A data value is considered an extreme outlier if its Z-
score is less than -3.0 or greater than +3.0.
 The larger the absolute value of the Z-score, the
farther the data value is from the mean.

Z-Score
where X represents the data value
X is the sample mean
S is the sample standard deviation
S
XX
Z



Z-Score
 Suppose the mean math SAT score is 490, with a
standard deviation of 100.
 Compute the Z-score for a test score of 620.
3.1
100
130
100
490620





S
XX
Z
A score of 620 is 1.3 standard deviations above the
mean and would not be considered an outlier.

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

General Descriptive Stats Using
Microsoft Excel
1. Select Tools.
2. Select Data Analysis.
3. Select Descriptive
Statistics and click OK.

General Descriptive Stats Using
Microsoft Excel
4. Enter the cell
range.
5. Check the
Summary
Statistics box.
6. Click OK

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

Minitab Output
Descriptive Statistics: House Price
Total
Variable Count Mean SE Mean StDev Variance Sum Minimum
House Price 5 600000 357771 800000 6.40000E+11 3000000 100000
N for
Variable Median Maximum Range Mode Skewness Kurtosis
House Price 300000 2000000 1900000 100000 2.01 4.13

Numerical Descriptive
Measures for a Population
 Descriptive statistics discussed previously described a
sample, not the population.
 Summary measures describing a population, called
parameters, are denoted with Greek letters.
 Important population parameters are the population mean,
variance, and standard deviation.

for a Population: The mean µ
 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 = population mean
N = population size
Where

 Average of squared deviations of values from
the mean
 Population variance:
For A Population: The Variance σ2
N
μ)(X
σ
N
1i
2
i
2



Where μ = population mean
N = population size

Numerical Descriptive Measures For A
Population: The Standard Deviation σ
 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



Sample statistics versus
population parameters
Measure Population
Parameter
Sample
Statistic
Mean
Variance
Standard
Deviation
X
2
S
S

2



 The empirical rule approximates the variation of
data in a bell-shaped distribution
 Approximately 68% of the data in a bell shaped
distribution is within 1 standard deviation of the
mean or
The Empirical Rule
1σμ 
μ
68%
1σμ

 Approximately 95% of the data in a bell-shaped
distribution lies within two standard deviations of the
mean, or µ ± 2σ
 Approximately 99.7% of the data in a bell-shaped
distribution lies within three standard deviations of the
mean, or µ ± 3σ
The Empirical Rule
3σμ
99.7%95%
2σμ

Using the Empirical Rule
 Suppose that the variable Math SAT scores is bell-
shaped with a mean of 500 and a standard deviation
of 90. Then,
 68% of all test takers scored between 410 and 590
(500 ± 90).
 95% of all test takers scored between 320 and 680
(500 ± 180).
 99.7% of all test takers scored between 230 and 770
(500 ± 270).

 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 - 1/22) x 100% = 75% …........ k=2 (μ ± 2σ)
(1 - 1/32) x 100% = 89% ………. k=3 (μ ± 3σ)
Chebyshev Rule
withinAt least

Computing Numerical Descriptive
Measures From A Frequency Distribution
 Sometimes you have only a frequency
distribution, not the raw data.
 In this situation you can compute
approximations to the mean and the standard
deviation of the data

Approximating the Mean from a
Frequency Distribution
 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


Approximating the Standard Deviation
from a Frequency Distribution
 Assume that all values within each class interval are
located at the midpoint of the 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
1-n
f)X(m
S
c
1j
j
2
j



Quartile Measures
 Quartiles split the ranked data into 4 segments with
an equal number of values per segment
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% of the observations
are smaller and 50% are larger)
 Only 25% of the observations are greater than the third
quartile
Q1 Q2 Q3
25% 25% 25%

Quartile Measures:
Locating Quartiles
Find a quartile by determining the value in the
appropriate position in the ranked data, where
First quartile position: Q1 = (n+1)/4 ranked value
Second quartile position: Q2 = (n+1)/2 ranked value
Third quartile position: Q3 = 3(n+1)/4 ranked value
where n is the number of observed values

Quartile Measures:
Calculation Rules
 When calculating the ranked position use the
following rules
 If the result is a whole number then it is the ranked
position to use
 If the result is a fractional half (e.g. 2.5, 7.5, 8.5, etc.)
then average the two corresponding data values.
 If the result is not a whole number or a fractional half
then round the result to the nearest integer to find the
ranked position.

(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
Quartile Measures:
Locating Quartiles
Sample Data in Ordered Array: 11 12 13 16 16 17 18 21 22
Q1 and Q3 are measures of non-central location
Q2 = median, is a measure of central tendency

(n = 9)
Q1 is in the (9+1)/4 = 2.5 position of the ranked data,
so Q1 = (12+13)/2 = 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 = (18+21)/2 = 19.5
Quartile Measures
Calculating The Quartiles: Example
Sample Data in Ordered Array: 11 12 13 16 16 17 18 21 22
Q1 and Q3 are measures of non-central location
Q2 = median, is a measure of central tendency

Quartile Measures:
The Interquartile Range (IQR)
 The IQR is Q3 – Q1 and measures the spread in the
middle 50% of the data
 The IQR is also called the midspread because it covers
the middle 50% of the data
 The IQR is a measure of variability that is not
influenced by outliers or extreme values
 Measures like Q1, Q3, and IQR that are not influenced
by outliers are called resistant measures

Calculating The Interquartile
Range
Median
(Q2)
X
maximumX
minimum Q1 Q3
Example:
25% 25% 25% 25%
12 30 45 57 70
Interquartile range
= 57 – 30 = 27

The Five Number Summary
The five numbers that help describe the center, spread
and shape of data are:
 Xsmallest
 First Quartile (Q1)
 Median (Q2)
 Third Quartile (Q3)
 Xlargest

Relationships among the five-number
summary and distribution shape
Left-Skewed Symmetric Right-Skewed
Median – Xsmallest
>
Xlargest – Median
≈
Xlargest – Median
<
Xlargest – Median
Q1 – Xsmallest
>
Xlargest – Q3
Q1 – Xsmallest
≈
Xlargest – Q3
Q1 – Xsmallest
<
Xlargest – Q3
Median – Q1
>
Q3 – Median
Median – Q1
≈
Q3 – Median
Median – Q1
<
Q3 – Median

Five Number Summary and
The Boxplot
 The Boxplot: A Graphical display of the data
based on the five-number summary:
Example:
Xsmallest -- Q1 -- Median -- Q3 -- Xlargest
25% of data 25% 25% 25% of data
of data of data
Xsmallest Q1 Median Q3 Xlargest

Five Number Summary:
Shape of Boxplots
 If data are symmetric around the median then the box
and central line are centered between the endpoints
 A Boxplot can be shown in either a vertical or horizontal
orientation
Xsmallest Q1 Median Q3 Xlargest

Distribution Shape and
The Boxplot
Right-SkewedLeft-Skewed Symmetric
Q1 Q2 Q3 Q1 Q2 Q3
Q1 Q2 Q3

Boxplot Example
 Below is a Boxplot for the following data:
0 2 2 2 3 3 4 5 5 9 27
 The data are right skewed, as the plot depicts
0 2 3 5 270 2 3 5 27
Xsmallest Q1 Q2 Q3 Xlargest

Boxplot example showing an outlier
Example Boxplot Showing An Outlier
0 5 10 15 20 25 30
Sample Data
•The boxplot below of the same data shows the outlier
value of 27 plotted separately
•A value is considered an outlier if it is more than 1.5
times the interquartile range below Q1 or above Q3

The Covariance
 The covariance measures the strength of the linear
relationship between two numerical variables (X & Y)
 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 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
 The covariance has a major flaw:
 It is not possible to determine the relative strength of the
relationship from the size of the covariance
Interpreting Covariance

Coefficient of Correlation
 Measures the relative strength of the linear
relationship between two numerical 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





Features of the
Coefficient of Correlation
 The population coefficient of correlation is referred as ρ.
 The sample coefficient of correlation is referred to as r.
 Either ρ or r have the following features:
 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

Scatter Plots of Sample Data with
Various Coefficients of Correlation
Y
X
Y
X
Y
X
Y
X
r = -1 r = -.6
r = +.3r = +1
Y
X
r = 0

The Coefficient of Correlation
Using Microsoft Excel
1. Select Tools/Data
Analysis
2. Choose Correlation from
the selection menu
3. Click OK . . .

The Coefficient of Correlation
4. Input data range and select
appropriate options
5. Click OK to get output

Interpreting the Coefficient of Correlation
 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
describe and communicate the important aspects of
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
 Described measures of variation
 Range, interquartile range, variance and standard
deviation, coefficient of variation, Z-scores
 Illustrated shape of distribution
 Symmetric, skewed
 Described data using the 5-number summary
 Boxplots

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

Bbs11 ppt ch03

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