This document outlines key concepts in descriptive statistics including measures of central tendency (mean, median, mode), measures of variability (range, standard deviation, variance), and measures of shape (skewness, kurtosis). It defines these terms and concepts, provides examples of how to compute them, and explains how to interpret and compare distributions based on these measures. The learning objectives are to understand and be able to calculate various descriptive statistics and use them to analyze and describe data distributions.

1. © 2002 Thomson / South-Western Slide 3-1
Chapter 3
Descriptive
Statistics

2. © 2002 Thomson / South-Western Slide 3-2
Learning Objectives
• Distinguish between measures of
central tendency, measures of
variability, and measures of shape
• Understand the meanings of mean,
median, mode, quartile, percentile, and
range
• Compute mean, median, mode,
percentile, quartile, range, variance,
standard deviation, and mean absolute
deviation

3. © 2002 Thomson / South-Western Slide 3-3
Learning Objectives -- Continued
• Differentiate between sample and
population variance and standard
deviation
• Understand the meaning of standard
deviation as it is applied by using the
empirical rule
• Understand box and whisker plots,
skewness, and kurtosis

4. © 2002 Thomson / South-Western Slide 3-4
Measures of Central Tendency
• Measures of central tendency yield
information about “particular places or
locations in a group of numbers.”
• Common Measures of Location
–Mode
–Median
–Mean
–Percentiles
–Quartiles

5. © 2002 Thomson / South-Western Slide 3-5
Mode
• The most frequently occurring value in a
data set
• Applicable to all levels of data
measurement (nominal, ordinal, interval,
and ratio)
• Bimodal -- Data sets that have two
modes
• Multimodal -- Data sets that contain
more than two modes

6. © 2002 Thomson / South-Western Slide 3-6
• The mode is 44.
• There are more 44s
than any other value.
35
37
37
39
40
40
41
41
43
43
43
43
44
44
44
44
44
45
45
46
46
46
46
48
Mode -- Example

7. © 2002 Thomson / South-Western Slide 3-7
Median (ΔΙΑΜΕΣΟΣ)
• Middle value in an ordered array of
numbers.
• Applicable for ordinal, interval, and ratio
data
• Not applicable for nominal data
• Unaffected by extremely large and
extremely small values.

8. © 2002 Thomson / South-Western Slide 3-8
Median: Computational Procedure
• First Procedure
– Arrange observations in an ordered array.
– If number of terms is odd, the median is
the middle term of the ordered array.
– If number of terms is even, the median is
the average of the middle two terms.
• Second Procedure
– The median’s position in an ordered array
is given by (n+1)/2.

9. © 2002 Thomson / South-Western Slide 3-9
Median: Example with
an Odd Number of Terms
Ordered Array includes:
3 4 5 7 8 9 11 14 15 16 16 17 19 19 20 21 22
• There are 17 terms in the ordered array.
• Position of median = (n+1)/2 = (17+1)/2 = 9
• The median is the 9th term, 15.
• If the 22 is replaced by 100, the median
remains at 15.
• If the 3 is replaced by -103, the median
remains at 15.

10. © 2002 Thomson / South-Western Slide 3-10
Mean (ΜΕΣΟΣ)
• Is the average of a group of numbers
• Applicable for interval and ratio data,
not applicable for nominal or ordinal
data
• Affected by each value in the data set,
including extreme values
• Computed by summing all values in the
data set and dividing the sum by the
number of values in the data set

11. © 2002 Thomson / South-Western Slide 3-11
Population Mean
  
   

   


X
N N
X X X XN
1 2 3
24 13 19 26 11
5
93
5
18 6
...
.

12. © 2002 Thomson / South-Western Slide 3-12
Sample Mean
X
X
n n
X X X Xn
 
   

    


 1 2 3
57 86 42 38 90 66
6
379
6
63167
...
.

13. © 2002 Thomson / South-Western Slide 3-13
Quartiles
Measures of central tendency that divide
a group of data into four subgroups
• Q1: 25% of the data set is below the first
quartile
• Q2: 50% of the data set is below the
second quartile
• Q3: 75% of the data set is below the third
quartile

14. © 2002 Thomson / South-Western Slide 3-14
Quartiles, continued
• Q1 is equal to the 25th percentile
• Q2 is located at 50th percentile and
equals the median
• Q3 is equal to the 75th percentile
Quartile values are not necessarily
members of the data set

15. © 2002 Thomson / South-Western Slide 3-15
Quartiles
25% 25% 25% 25%
Q3
Q2
Q1

16. © 2002 Thomson / South-Western Slide 3-16
• Ordered array: 106, 109, 114, 116, 121,
122, 125, 129
• Q1:
• Q2:
• Q3:
Quartiles: Example
i Q
  


25
100
8 2
109 114
2
1115
1
( ) .
i Q
  


50
100
8 4
116 121
2
1185
2
( ) .
i Q
  


75
100
8 6
122 125
2
1235
3
( ) .

17. © 2002 Thomson / South-Western Slide 3-17
Measures of Variability
• Measures of variability describe the spread
or the dispersion of a set of data.
• Common Measures of Variability
–Range
–Interquartile Range
–Mean Absolute Deviation
–Variance
–Standard Deviation
– Z scores
–Coefficient of Variation

18. © 2002 Thomson / South-Western Slide 3-18
Variability
Mean
Mean
Mean
No Variability in Cash Flow
Variability in Cash Flow Mean

19. © 2002 Thomson / South-Western Slide 3-19
Variability
No Variability
Variability

20. © 2002 Thomson / South-Western Slide 3-20
Range
• The difference between the largest and
the smallest values in a set of data
• Simple to compute
• Ignores all data points
except the
two extremes
• Example:
Range =
Largest - Smallest =
48 - 35 = 13
35
37
37
39
40
40
41
41
43
43
43
43
44
44
44
44
44
45
45
46
46
46
46
48

21. © 2002 Thomson / South-Western Slide 3-21
Interquartile Range
• Range of values between the first and third
quartiles
• Range of the “middle half”
• Less influenced by extremes
Interquartile Range Q Q
 
3 1

22. © 2002 Thomson / South-Western Slide 3-22
Deviation from the Mean
• Data set: 5, 9, 16, 17, 18
• Mean:
• Deviations from the mean: -8, -4, 3, 4, 5
   
X
N
65
5
13
0 5 10 15 20
-8 -4
+3 +4
+5


23. © 2002 Thomson / South-Western Slide 3-23
Mean Absolute Deviation
• Average of the absolute deviations from
the mean
5
9
16
17
18
-8
-4
+3
+4
+5
0
+8
+4
+3
+4
+5
24
X X   X  
M A D
X
N
. . .
.




 
24
5
4 8

24. © 2002 Thomson / South-Western Slide 3-24
Population Variance
• Average of the squared deviations from
the arithmetic mean
5
9
16
17
18
-8
-4
+3
+4
+5
0
64
16
9
16
25
130
X X    
2
X  
 
2
2
130
5
26 0






 X
N
.

25. © 2002 Thomson / South-Western Slide 3-25
Population Standard Deviation
• Square root of
the variance
 
2
2
2
130
5
26 0
26 0
5 1











 X
N
.
.
.
5
9
16
17
18
-8
-4
+3
+4
+5
0
64
16
9
16
25
130
X X    
2
X  

26. © 2002 Thomson / South-Western Slide 3-26
Empirical Rule
• Data are normally distributed (or
approximately normal)
 
 1
 
 2
 
 3
95
99.7
68
Distance from
the Mean
Percentage of Values
Falling Within Distance

27. © 2002 Thomson / South-Western Slide 3-27
Sample Variance
• Average of the squared deviations from
the arithmetic mean
2,398
1,844
1,539
1,311
7,092
625
71
-234
-462
0
390,625
5,041
54,756
213,444
663,866
X X X
  
2
X X

 
2
2
1
663 866
3
221 288 67
S
X X
n






,
, .

28. © 2002 Thomson / South-Western Slide 3-28
Sample Standard Deviation
• Square root of the
sample variance  
2
2
2
1
663 866
3
221 288 67
221 288 67
470 41
S
X X
S
n
S









,
, .
, .
.
2,398
1,844
1,539
1,311
7,092
625
71
-234
-462
0
390,625
5,041
54,756
213,444
663,866
X X X
  
2
X X


29. © 2002 Thomson / South-Western Slide 3-29
Coefficient of Variation
• Ratio of the standard deviation to the
mean, expressed as a percentage
• Measurement of relative dispersion
 
C V
. .


100

30. © 2002 Thomson / South-Western Slide 3-30
Coefficient of Variation
 
 
1
29
4 6
100
4 6
29
100
1586
1
1
1
1









.
.
.
. .
CV  
 
2
84
10
100
10
84
100
1190
2
2
2
2









CV
. .
.

31. © 2002 Thomson / South-Western Slide 3-31
Measures of Shape
• Skewness
– Absence of symmetry
– Extreme values in one side of a
distribution
• Kurtosis
– Peakedness of a distribution
• Box and Whisker Plots
– Graphic display of a distribution
– Reveals skewness

32. © 2002 Thomson / South-Western Slide 3-32
Skewness
Negatively
Skewed
Positively
Skewed
Symmetric
(Not Skewed)

33. © 2002 Thomson / South-Western Slide 3-33
Skewness
Negatively
Skewed
Mode
Median
Mean
Symmetric
(Not Skewed)
Mean
Median
Mode
Positively
Skewed
Mode
Median
Mean

34. © 2002 Thomson / South-Western Slide 3-34
Coefficient of Skewness
• Summary measure for skewness
• If S < 0, the distribution is negatively
skewed (skewed to the left).
• If S = 0, the distribution is symmetric (not
skewed).
• If S > 0, the distribution is positively
skewed (skewed to the right).
 
S
Md


3 


35. © 2002 Thomson / South-Western Slide 3-35
Coefficient of Skewness
 
 
1
1
1
1
1 1
1
23
26
12 3
3
3 23 26
12 3
073











 
M
S
M
d
d
.
.
.
 
 
2
2
2
2
2 2
2
26
26
12 3
3
3 26 26
12 3
0












M
S
M
d
d
.
.
 
 
3
3
3
3
3 3
3
29
26
12 3
3
3 29 26
12 3
073











 
M
S
M
d
d
.
.
.

36. © 2002 Thomson / South-Western Slide 3-36
Kurtosis
• Peakedness of a distribution
– Leptokurtic: high and thin
– Mesokurtic: normal in shape
– Platykurtic: flat and spread out
Leptokurtic
Mesokurtic
Platykurtic

37. © 2002 Thomson / South-Western Slide 3-37
Box and Whisker Plot
• Five specific values are used:
–Median, Q2
–First quartile, Q1
–Third quartile, Q3
–Minimum value in the data set
–Maximum value in the data set

38. © 2002 Thomson / South-Western Slide 3-38
Box and Whisker Plot, continued
• Inner Fences
– IQR = Q3 - Q1
– Lower inner fence = Q1 - 1.5 IQR
– Upper inner fence = Q3 + 1.5 IQR
• Outer Fences
– Lower outer fence = Q1 - 3.0 IQR
– Upper outer fence = Q3 + 3.0 IQR

39. © 2002 Thomson / South-Western Slide 3-39
Box and Whisker Plot
Q1 Q3
Q2
Minimum Maximum

40. © 2002 Thomson / South-Western Slide 3-40
Skewness: Box and Whisker Plots,
and Coefficient of Skewness
Negatively
Skewed
Positively
Skewed
Symmetric
(Not Skewed)
S < 0 S = 0 S > 0