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Chap 3-1
EF 507
QUANTITATIVE METHODS FOR ECONOMICS AND FINANCE
FALL 2008
Chapter 3
Describing Data: Numerical
Chap 3-2
Describing Data Numerically
Arithmetic Mean
Median
Mode
Describing Data Numerically
Variance
Standard Deviation
Coefficient of Variation
Range
Central Tendency Variation
Chap 3-3
Measures of Central Tendency
Central Tendency
Mean Median Mode
n
xx
n
1ii
Overview
Midpoint of ranked values
Most frequently observed value
Arithmetic average
Chap 3-4
Arithmetic Mean
The arithmetic mean (mean) is the most common measure of central tendency
For a population of N values:
For a sample of size n:
Sample sizen
xxx
n
xx n21
n
1ii
Observed
values
N
xxx
N
xμ N21
N
1ii
Population size
Population values
Chap 3-5
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
35
15
5
54321
4
5
20
5
104321
Chap 3-6
Median
In an ordered list, 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
Chap 3-7
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
dataorderedtheinposition2
1npositionMedian
2
1n
Chap 3-8
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 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
Chap 3-9
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
Chap 3-10
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
Chap 3-11
Mean is generally used, unless extreme values (outliers) exist
If 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”?
Chap 3-12
Shape of a Distribution
Describes how data are distributed Measures of shape
Symmetric or skewed
Mean = Median Mean < Median Median < Mean
Right-SkewedLeft-Skewed Symmetric
Chap 3-13
Same center, different variation
Measures of Variability
Variation
VarianceStandard Deviation
Coefficient of Variation
Range
Measures of variation give information on the spread or variability of the data values.
Chap 3-14
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:
Chap 3-15
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
Chap 3-16
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
Chap 3-17
Average of squared deviations of values from the mean (Why “N-1”?)
Population variance:
Population Variance
1-N
μ)(xσ
N
1i
2i
2
Where = population mean
N = population size
xi = ith value of the variable x
μ
Chap 3-18
Average (approximately) of squared deviations of values from the mean
Sample variance:
Sample Variance
1-n
)x(xs
n
1i
2i
2
Where = arithmetic mean
n = sample size
Xi = ith value of the variable X
X
Chap 3-19
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:
1-N
μ)(xσ
N
1i
2i
Chap 3-20
Sample 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(xS
n
1i
2i
Chap 3-21
Calculation Example:Sample Standard Deviation
Sample Data (xi) : 10 12 14 15 17 18 18 24
n = 8 Mean = x = 16
4.24267
126
18
16)(2416)(1416)(1216)(10
1n
)x(24)x(14)x(12)X(10s
2222
2222
A measure of the “average” scatter around the mean
Chap 3-22
Measuring variation
Small standard deviation
Large standard deviation
Chap 3-23
Comparing Standard Deviations
Mean = 15.5 s = 3.338 11 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
Chap 3-24
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)
Chap 3-25
For any population with mean μ and standard deviation σ , and k > 1 , the percentage of observations that fall within the interval
[μ + kσ] Is at least
Chebyshev’s Theorem
)]%(1/k100[1 2
Chap 3-26
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)
Chebyshev’s Theorem
withinAt least
(continued)
Chap 3-27
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
Chap 3-28
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
Chap 3-29
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
sCV
Chap 3-30
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
$5100%
x
sCVA
5%100%$100
$5100%
x
sCVB
Chap 3-31
Using Microsoft Excel
Descriptive Statistics can be obtained from Microsoft® Excel
Use menu choice:
tools / data analysis / descriptive statistics
Enter details in dialog box
Chap 3-32
Using Excel
Use menu choice:
tools / data analysis /
descriptive statistics
Chap 3-33
Enter dialog box details
Check box for summary statistics
Click OK
Using Excel(continued)
Chap 3-34
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
Chap 3-35
Weighted Mean
The weighted mean of a set of data is
Where wi is the weight of the ith observation
Use when data is already grouped into n classes, with wi values in the ith class
i
nn2211
n
1iii
w
xwxwxw
w
xwx
Chap 3-36
The Sample Covariance The covariance measures the strength of the linear relationship
between two variables
The population covariance:
The sample covariance:
Only concerned with the strength of the relationship No causal effect is implied
N
))(y(xy),(xCov
N
1iyixi
xy
1n
)y)(yx(xsy),(xCov
n
1iii
xy
Chap 3-37
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
Interpreting Covariance
Chap 3-38
Coefficient of Correlation
Measures the relative strength of the linear relationship between two variables
Population correlation coefficient (rho):
Sample correlation coefficient (r):
YX ss
y),(xCovr
YXσσ
y),(xCovρ
Chap 3-39
Features of Correlation Coefficient, r
Unit free (difference with covariance)
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
Chap 3-40
Scatter Plots of Data with Various Correlation Coefficients
Y
X
Y
X
Y
X
Y
X
Y
X
r = -1 r = -0.6 r = 0
r = +0.3r = +1
Y
Xr = 0
Chap 3-41
Using Excel to Find the Correlation Coefficient
Select Tools/Data Analysis
Choose Correlation from the selection menu
Click OK . . .
Chap 3-42
Using Excel to Find the Correlation Coefficient
Input data range and select appropriate options
Click OK to get output
(continued)
Chap 3-43
Interpreting the Result
r = 0.733
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 ScoreT
est
#2 S
core
Chap 3-44
Obtaining Linear Relationships
An equation can be fit to show the best linear relationship between two variables:
Y = β0 + β1X
Where Y is the dependent variable and X is the independent variable (Ch 12)
