1. Review of Measures of Central Tendency, Dispersion & Association
Graphical Excellence
Measures of Central Tendency
Mean, Median, Mode
Measures of Dispersion
Variance, Standard Deviation, Range
Measures of Association
Covariance, Correlation Coefficient
Relationship of basic stats to OLS

2. Graphical Excellence
Learning from Monkeys

3. Why Graphs & Stats?
Graphs and descriptive statistics when used properly can summarize lines of data effectively for the reader. What’s a good approximation of the age of students in this class?
We use graphs and basic stats (Mean, Variance, Covariance) etc to highlight trends and to motivate the research question.
We use other tools for analysis – Regression, Case Study, Content Analysis etc.

4. What story does this graph tell? What questions does the graph raise?

5. Graphical Excellence
The graph presents large data sets concisely and coherently – label your axes
The ideas and concepts to be delivered are clearly understood to the viewer – state the units used (EX: $ or $ in Mil. etc.)

6. What’s the problem here?

7. Graphical Excellence
The display induces the viewer to address the substance of the data and not the form of the graph. – Select the appropriate type of graph (bar chart for levels, scatter plot for trends etc.)
There is no distortion of what the data reveal. – Make sure the axes are not stretched or compressed to make a point

8. Do New Stadiums Bring People in?

9. Do New Stadiums Bring People in?

10. Things to be cautious about when observing a graph:
Is there a missing scale on one axis.
Do not be influenced by a graph’s caption.
Are changes presented in absolute values only, or in percent form too.

11. Numerical Descriptive Measures
Measures of Central Tendency
Mean, Median, Mode
Measures of Dispersion
Variance, Standard Deviation
Measures of Association
Covariance, Correlation Coefficient

12. Arithmetic mean
This is the most popular and useful measure of central location
Sum of the measurements
Number of measurements
Mean =
Sample mean
Population mean
Sample size
Population size

13. Example 1
The mean of the sample of six measurements 7, 3, 9, -2, 4, 6 is given by 7 3 9 4 6
4.5
Example 2
Suppose the telephone bills of example 2.1 represent population
of measurements. The population mean is
42.19
15.30
53.21
43.59

14. Example 3 When many of the measurements have the same value, the
measurement can be summarized in a frequency table. Suppose
the number of children in a sample of 16 employees were recorded
as follows:
NUMBER OF CHILDREN
NUMBER OF EMPLOYEES
16 employees

15. The median
The median of a set of measurements is the value that falls in the middle when the measurements are arranged in order of magnitude.
Example 4
Seven employee salaries were recorded
(in 1000s) : 28, 60, 26, 32, 30, 26, 29.
Find the median salary.
Suppose one employee’s salary of $31,000
was added to the group recorded before.
Find the median salary.
Even number of observations
First, sort the salaries.
Then, locate the values
in the middle
First, sort the salaries.
Then, locate the value
in the middle
Odd number of observations
26,26,28,29,30,32,60
There are two middle values!
26,26,28,29, ,32,60,31
26,26,28,29, ,32,60,31
26,26,28,29, 30,32,60,31
26,26,28,29,30,32,60,31
29.5,

16. The mode
The mode of a set of measurements is the value that occurs most frequently.
Set of data may have one mode (or modal class), or two or more modes.
For large data sets
the modal class is
much more relevant
than the a single-
value mode.
The modal class

17. Example 5
The manager of a men’s store observes the waist size (in inches) of trousers sold yesterday: 31, 34, 36, 33, 28, 34, 30, 34, 32, 40.
The mode of this data set is 34 in.
This information seems valuable
(for example, for the design of a new display in the store), much more than “ the median is 33.2 in.”.

18. Relationship among Mean, Median, and Mode
If a distribution is symmetrical, the mean, median and mode coincide
If a distribution is non symmetrical, and skewed to the left or to the right, the three measures differ.
A positively skewed distribution
(“skewed to the right”)
Mode
Mean
Median

19. If a distribution is symmetrical, the mean, median and mode coincide`
If a distribution is symmetrical, the mean, median and mode coincide
If a distribution is non symmetrical, and skewed to the left or to the right, the three measures differ.
A positively skewed distribution
(“skewed to the right”)
A negatively skewed distribution
(“skewed to the left”)
Mode
Mean
Mean
Mode
Median
Median

20. Measures of variability (Looking beyond the average)
Measures of central location fail to tell the whole story about the distribution.
A question of interest still remains unanswered:
How typical is the average value of all
the measurements in the data set? or
How much spread out are the measurements
about the average value?

21. Observe two hypothetical data sets
Low variability data set
The average value provides
a good representation of the
values in the data set.
High variability data set
This is the previous
data set. It is now
changing to...
The same average value does not
provide as good presentation of the
values in the data set as before.

22. The range
The range of a set of measurements is the difference between the largest and smallest measurements.
Its major advantage is the ease with which it can be computed.
Its major shortcoming is its failure to provide information on the dispersion of the values between the two end points.
But, how do all the measurements spread out? ? ? ?
The range cannot assist in answering this question
Range
Smallest
measurement
Largest
measurement

23. The variance
This measure of dispersion reflects the values of all the measurements.
The variance of a population of N measurements x1, x2,…,xN having a mean m is defined as
The variance of a sample of n measurements x1, x2, …,xn having a mean is defined as
Excel uses Varp formula
Excel uses Var formula

24. A B Consider two small populations: Population A: 8, 9, 10, 11, 12
Population B: 4, 7, 10, 13, 16
9-10= -1
11-10= +1
8-10= -2
12-10= +2
Thus, a measure of dispersion
is needed that agrees with this
observation.
Sum = 0
Let us start by calculating
the sum of deviations
The sum of deviations
is zero in both cases,
therefore, another
measure is needed. A 8 9 10 11 12
…but measurements in B
are much more dispersed
then those in A.
The mean of both
populations is 10...
4-10 = - 6
16-10 = +6 B
7-10 = -3 4 7 10 13 16
13-10 = +3

25. A B The sum of squared deviations is used in calculating the variance.
9-10= -1
The sum of squared deviations
is used in calculating the variance.
See example next.
11-10= +1
8-10= -2
12-10= +2
Sum = 0
The sum of deviations
is zero in both cases,
therefore, another
measure is needed. A 8 9 10 11 12
4-10 = - 6
16-10 = +6 B
7-10 = -3 4 7 10 13 16
13-10 = +3

26. Let us calculate the variance of the two populations
Why is the variance defined as
the average squared deviation?
Why not use the sum of squared
deviations as a measure of
dispersion instead?
After all, the sum of squared
deviations increases in
magnitude when the dispersion
of a data set increases!!

27. Which data set has a larger dispersion?
Let us calculate the sum of squared deviations for both data sets
However, when calculated on
“per observation” basis (variance),
the data set dispersions are properly ranked
Data set B
is more dispersed
around the mean A B 1 2 3 1 3 5
sA2 = SumA/N = 10/5 = 2
sB2 = SumB/N = 8/2 = 4
SumA = (1-2)2 +…+(1-2)2 +(3-2)2 +… +(3-2)2= 10
5 times
5 times !
SumB = (1-3)2 + (5-3)2 = 8

28. =[3.42+2.52+…+3.72]-[(17.7)2/6] = 1.075 (years)2
Example 6
Find the mean and the variance of the following sample of measurements (in years).
3.4, 2.5, 4.1, 1.2, 2.8, 3.7
Solution
A shortcut formula
=[ …+3.72]-[(17.7)2/6] = (years)2

29. The standard deviation of a set of measurements is the square root of the variance of the measurements.
Example 4.9
Rates of return over the past 10 years for two mutual funds are shown below. Which one have a higher level of risk?
Fund A: 8.3, -6.2, 20.9, -2.7, 33.6, 42.9, 24.4, 5.2, 3.1, 30.05
Fund B: 12.1, -2.8, 6.4, 12.2, 27.8, 25.3, 18.2, 10.7, -1.3, 11.4

30. Solution
Let us use the Excel printout that is run from the “Descriptive statistics” sub-menu (use file Xm04-10)
Fund A should be considered
riskier because its standard
deviation is larger

31. The coefficient of variation
The coefficient of variation of a set of measurements is the standard deviation divided by the mean value.
This coefficient provides a proportionate measure of variation.
A standard deviation of 10 may be perceived
as large when the mean value is 100, but only
moderately large when the mean value is 500

32. Interpreting Standard Deviation
The standard deviation can be used to
compare the variability of several distributions
make a statement about the general shape of a distribution.

33. Measures of Association
Two numerical measures are presented, for the description of linear relationship between two variables depicted in the scatter diagram.
Covariance - is there any pattern to the way two variables move together?
Correlation coefficient - how strong is the linear relationship between two variables

34. The covariance Excel uses this formula to calculate Cov
mx (my) is the population mean of the variable X (Y)
N is the population size. n is the sample size.
NOTE: The formula in Excel does not give you sample covariance

35. If the two variables move the same direction, (both increase or both decrease), the covariance is a large positive number.
If the two variables move in two opposite directions, (one increases when the other one decreases), the covariance is a large negative number.
If the two variables are unrelated, the covariance will be close to zero.

36. The coefficient of correlation
This coefficient answers the question: How strong is the association between X and Y.

37. r or r = +1 Strong positive linear relationship or -1-1
Strong positive linear relationship
COV(X,Y)>0 or
r or r =
No linear relationship
COV(X,Y)=0
Strong negative linear relationship
COV(X,Y)<0

38. If the two variables are very strongly positively related, the coefficient value is close to +1 (strong positive linear relationship).
If the two variables are very strongly negatively related, the coefficient value is close to -1 (strong negative linear relationship).
No straight line relationship is indicated by a coefficient close to zero.

39. Example 7
Compute the covariance and the coefficient of correlation to measure how advertising expenditure and sales level are related to one another.
Base your calculation on the data provided in example 2.3

40. Use the procedure below to obtain the required summationsx y xy x2 y2
Similarly, sy = 8.839

41. Excel printout Interpretation
The covariance ( ) indicates that advertisement expenditure and sales levelare positively related
The coefficient of correlation (.797) indicates that there is a strong positive linear relationship between advertisement expenditure and sales level.
Covariance matrix
Correlation matrix

42. The actual y value of point i
The Least Squares Method
We are seeking a line that best fit the data
We define “best fit line” as a line for which the sum of squared differences between it and the data points is minimized.
The y value of point i
calculated from the
equation of the line
The actual y value of point i

43. Different lines generate different errors, Y
Errors X
Different lines generate different errors,
thus different sum of squares of errors.

44. The coefficients b0 and b1 of the line
that minimizes the sum of squares of errors are calculated from the data.