1. STATISTICS ELEMENTARY MARIO F. TRIOLA
Section Measures of Variation
MARIO F. TRIOLA
EIGHTH
EDITION

2. Compute measures of variability.
Objective
Compute measures of variability.

3. Waiting Times of Bank Customers
at Different Banks
in minutes
Jefferson Valley Bank
Bank of Providence
6.5
4.2
6.6
5.4
6.7
5.8
6.8
6.2
7.1
6.7
7.3
7.7
7.4
7.7
7.7
8.5
7.7
9.3
7.7
10.0

4. Waiting Times of Bank Customers
at Different Banks
in minutes
Jefferson Valley Bank
Bank of Providence
6.5
4.2
6.6
5.4
6.7
5.8
6.8
6.2
7.1
6.7
7.3
7.7
7.4
7.7
7.7
8.5
7.7
9.3
7.7
10.0
Jefferson Valley Bank
Bank of Providence
Mean
Median
Mode
Midrange
7.15
7.20
7.7
7.10
7.15
7.20
7.7
7.10

5. Section 2.5 Measures of Variation
This last example shows that each bank has the same measures of center, but a closer look at the distribution of waiting times shows that the variability of waiting times is not the same.

6. Dotplots of Waiting Times
Even though the measures of center are all the same, it is obvious from the dotplots of each group of data that there are some differences in the ‘spread’ (or variation) of the data.
page 69 of text
Figure 2-14

7. Measures of Variation Range Standard Deviation Variance
Interquartile Range

8. Range Measures of Variation value greatest least value
The range is not a very useful measure of variation as it only uses two values of the data.
Other measures of variation (to follow) will more more useful as they are computed by using every data value.

9. Range Jefferson Valley Providence Jefferson Valley Providence 6.5 4.2
6.6
5.4
6.7
5.8
6.8
6.2
7.1
7.3
7.7
7.4
8.5
9.3
10.0
Jefferson Valley
Range = = 1.2 minutes
Providence
Range = 10.0 – 4.2 = 5.8 minutes

10. Interquartile Range IQR = Q3 – Q1
Measures of Variation
Interquartile Range
IQR = Q3 – Q1
The range is not a very useful measure of variation as it only uses two values of the data.
Other measures of variation (to follow) will more more useful as they are computed by using every data value.

11. Interquartile Range (IQR) = Q3-Q1
Jefferson Valley
Providence
6.5
4.2
6.6
5.4
6.7
5.8
6.8
6.2
7.1
7.3
7.7
7.4
8.5
9.3
10.0
Jefferson Valley
Median = 7.2
Q1 = Q3 = 7.7
IQR = = 1.0
Providence
Q1 = Q3 = 8.5
IQR = 8.5 – 5.8 = 2.7 minutes

12. Measures of Variation Standard Deviation
a measure of variation of the scores about the mean
(average deviation from the mean)
Ask students to explain what this definition indicates.

13. Sample Standard Deviation Formula

14. Sample Standard Deviation Formula
 (x - x)2
S =
n - 1
Formula 2-4
The definition indicates that one should find the average distance each score is from the mean.
The use of n-1 in the denominator is necessary because there are only n-1 independent values - that is, only n-1 values can be assigned any number before the nth value is determined.
page 70 of text

15. Computing Standard Deviation
Compute the mean x

16. Computing Standard Deviation
Compute the mean
Subtract the mean from each data value x
x-x

17. Computing Standard Deviation
Compute the mean
Subtract the mean from each data value
Square the differences x
x-x

18. Computing Standard Deviation
Compute the mean
Subtract the mean from each data value
Square the differences
Sum the squared differences x
x-x

19. Computing Standard Deviation
Compute the mean
Subtract the mean from each data value
Square the differences
Sum the squared differences
Divide the sum by (n-1) x
x-x

20. Computing Standard Deviation
Compute the mean
Subtract the mean from each data value
Square the differences
Sum the squared differences
Divide the sum by (n-1)
Take the square root of this result x
x-x

21. Computing Standard Deviation Providence Bank #5 pg81
Compute the mean

22. Computing Standard Deviation Providence Bank #5 pg81
Compute the mean
Subtract the mean from each data value

23. Computing Standard Deviation Providence Bank #5 pg81
Compute the mean
Subtract the mean from each data value
Square the differences

24. Computing Standard Deviation Providence Bank #5 pg81
Compute the mean
Subtract the mean from each data value
Square the differences
Sum the squared differences

25. Computing Standard Deviation Providence Bank #5 pg81
Compute the mean
Subtract the mean from each data value
Square the differences
Sum the squared differences
Divide the sum by (n-1)

26. Computing Standard Deviation Providence Bank #5 pg81
Compute the mean
Subtract the mean from each data value
Square the differences
Sum the squared differences
Divide the sum by (n-1)
Take the square root of this result

27. Population Standard Deviation
 (x - µ) 2
 = N
 is the lowercase Greek ‘sigma’.
Note the division by N(population size), rather than n-1
Most data is a sample, rather than a population; therefore, this formula is not use very often.

28. for Standard Deviation
Symbols
for Standard Deviation
Sample
Population s Sx
xn-1
Textbook 
x
xn
Book
Some graphics
calculators
Some graphics
calculators
Some
non-graphics
calculators
Some
non-graphics
calculators
Articles in professional journals and reports often use SD for standard deviation and VAR for variance.

29. Measures of Variation
Variance

30. standard deviation squared
Measures of Variation
Variance
standard deviation squared
page 74 of text

31. standard deviation squared
Measures of Variation
Variance
standard deviation squared s  } 2
use square key
on calculator
After computing the standard deviation, square the resulting number using a calculator.
Notation 2

32. Variance  (x - x )2  (x - µ)2 n - 1  2 = N s2 = Sample Variance
The formulas for variance are the same as for standard deviation without the square root - remind student, squaring a square root will result in the radicand.
 (x - µ)2 N
 2 =
Population
Variance

33. Round-off Rule for measures of variation
Carry one more decimal place than is present in the original set of values.
Round only the final answer, never in the middle of a calculation.
Same rounding rule as for measures of center.
page 75 of text

34. HOMEWORK
Page , 5

35. Standard Deviation from a Frequency Table

36. Standard Deviation from a Frequency Table
Rating
Frequency
0-2 20
3-5 14
6-8 15
9-11 2
12-14 1
midpoints 1 4 7 10 13 20 56
105 13

37. Rating
Frequency
0-2 20
3-5 14
6-8 15
9-11 2
12-14 1
midpoints 1 4 7 10 13

38. Standard Deviation from a Frequency Table
Rating
Frequency
0-2 20
3-5 14
6-8 15
9-11 2
12-14 1
midpoints 1 4 7 10 13

39. Standard Deviation from a Frequency Table
Rating
Frequency
0-2 20
3-5 14
6-8 15
9-11 2
12-14 1
midpoints 1 4 7 10 13

40. Standard Deviation from a Frequency Table

41. HOMEWORK
Page , 11, 29

42. Objective: Understanding Standard Deviation
Apply the Empirical Rule
Apply Chebyshev’s Rule
Apply Range Rule of Thumb
Identify Unusual Values

43. Estimation of Standard Deviation
Range Rule of Thumb
x s
x - 2s x
(maximum usual value)
(minimum
usual value)
Range  4s or
Reminder: range is the highest score minus the lowest score

44. Estimation of Standard Deviation
Range Rule of Thumb
x s
x - 2s x
(maximum usual value)
(minimum
usual value)
Range  4s or
Reminder: range is the highest score minus the lowest score
Range 4
s 

45. Estimation of Standard Deviation highest value - lowest value
Range Rule of Thumb
x s
x - 2s x
(maximum usual value)
(minimum
usual value)
Range  4s or
Reminder: range is the highest score minus the lowest score
Range 4
highest value - lowest value
s  = 4

46. Usual Sample Values

47. minimum ‘usual’ value  (mean) - 2 (standard deviation)
Usual Sample Values
minimum ‘usual’ value  (mean) (standard deviation)
minimum  x - 2(s)

48. Usual Sample Values minimum  x - 2(s) maximum  x + 2(s)
minimum ‘usual’ value  (mean) (standard deviation)
minimum  x - 2(s)
maximum ‘usual’ value  (mean) (standard deviation)
maximum  x + 2(s)
These ideas will be used repeatedly throughout the course.

49. (applies to bell-shaped distributions)
The Empirical Rule
(applies to bell-shaped distributions)
FIGURE 2-15
page 79 of text x

50. (applies to bell-shaped distributions)
The Empirical Rule
(applies to bell-shaped distributions)
FIGURE 2-15
68% within
1 standard deviation
Some student have difficulty understand the idea of ‘within one standard deviation of the mean’. Emphasize that this means the interval from one standard deviation below the mean to one standard deviation above the mean.
34%
34%
x - s x
x + s

51. (applies to bell-shaped distributions)
The Empirical Rule
(applies to bell-shaped distributions)
FIGURE 2-15
95% within
2 standard deviations
68% within
1 standard deviation
34%
34%
13.5%
13.5%
x - 2s
x - s x
x + s
x + 2s

52. The Empirical Rule x - 3s x - 2s x - s x x + s x + 2s x + 3s
(applies to bell-shaped distributions)
FIGURE 2-15
99.7% of data are within 3 standard deviations of the mean
95% within
2 standard deviations
68% within
1 standard deviation
These percentages will be verified by the concepts learned in Chapter 5.
Emphasize the Empirical Rule is appropriate for data that is in a BELL-SHAPED distribution.
34%
34%
2.4%
2.4%
0.1%
0.1%
13.5%
13.5%
x - 3s
x - 2s
x - s x
x + s
x + 2s
x + 3s

53. Example Application of the Empirical Rule
A set of 1000 test scores has a symmetric, mound-shaped distribution. The mean is 175 and the standard deviation is 10.
Approximately what percent of the scores are between 175 and 195?

54. of the area within 2 standard deviations
95% of scores are between
The shaded area is half
of the area within 2 standard deviations
of the mean .... so (.5)(.95)=.475
175
185
195

55. Example Application of the Empirical Rule
A set of 1000 test scores has a symmetric, mound-shaped distribution. The mean is 175 and the standard deviation is 10.
Approximately how many scores are between 155 and 165?

56. Example Application of the Empirical Rule
The area from 155 to 195 is 0.95
The area from 165 to 185 is 0.68
Subtracting these values gives the area from 155 to 165
and 185 to 195 combined. We need to divide the result by 2
because the symmetry splits this area equally.
= 0.27
0.27 divided by 2 = 0.135
Example Application of the Empirical Rule
165
195
155
175
185

57. Chebyshev’s Theorem applies to distributions of any shape.
the proportion (or fraction) of any set of data lying within K standard deviations of the mean is always at least 1 - 1/K2 , where K is any positive number greater than 1.
at least 3/4 (75%) of all values lie within 2  standard deviations of the mean.
at least 8/9 (89%) of all values lie within 3  standard deviations of the mean.
Emphasize Chebyshev’s Theorem applies to data that is in a distribution of any shape - that is, it is less prescriptive than the Empirical Rule.
page 80 of text

58. Measures of Variation Summary
For typical data sets, it is unusual for a score to differ from the mean by more than 2 or 3 standard deviations.
This idea will be revisited throughout the study of Elementary Statistics.

59. Homework
Page , 20, 21, 22, 23, 24, 25, 30 a and b