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1. Data Analysis and Presentation

2. Data Type
• Quantitative data is classified as categorical and
numerical data
• Categorical data refer to data whose values
cannot be measured numerically but can be
either classified into sets (categories) such as sex
(male and female), religion, department
• Numerical data, which are sometimes termed
‘quantifiable’, are those whose values are
measured or counted numerically as quantities
• These are analyzed by different techniques

3. Quantitative Data Analysis
Two common types analysis
1. Descriptive statistics
– to describe, summarize, or explain a given set of data
2. Inferential statistics
– use statistics computed from a sample to infer about
the population
– It is concerned by making inferences from the samples
about the populations from which they have been
drawn

4. Common data analysis technique
1. Frequency distribution
2. Measures of central tendency
3. Measures of dispersion
4. Correlation
5. Regression
6. And more

5. Frequency distribution
• Shows the frequency of occurrence of
different values of a single Phenomenon.
• Main purpose
1. To facilitate the analysis of data.
2. To estimate frequencies of the unknown
population distribution from the distribution of
sample data and
3. To facilitate the computation of various statistical
measures

6. Example – Frequency Distribution
• In a survey of 30 organizations, the number of computers
registered in each organizations is given in the following
table
• This data has no meaning unless it is summarized in
some form

7. Example
The following table shows frequency distribution
Number of
computers

8. Example …
• The above table can tell us meaningful
information such as
– How many computers most organizations has?
– How many organizations do not have computers?
– How many organizations have more than five
computers?
– Why the computer distribution is not the same in
all organizations?
– And other questions

9. Continuous frequency distribution
• Continuous frequency distribution constructed when
the values do not have discrete values like number of
computers
• Example is age, salary variables have continuous
values

10. Constructing frequency table
• The number of classes should preferably be between 5
and 20. However there is no rigidity about it.
• As far as possible one should avoid values of class
intervals as 3,7,11,26….etc. preferably one should have
class intervals of either five or multiples of 5 like
10,20,25,100 etc.
• The starting point i.e the lower limit of the first class,
should either be zero or 5 or multiple of 5.
• To ensure continuity and to get correct class interval
we should adopt “exclusive” method.
• Wherever possible, it is desirable to use class interval
of equal sizes.

11. Constructing …
You can create a frequency table with two variables
This is called Bivariate frequency table
IT staff
Type of organizations
<10 10-50 >5
0
Private 15 5 0
Government 0 10 50
Non-government 0 30 5

12. Graphs
• You can plot your frequency distribution using bar
graph, pie chart, frequency polygon and other type of
charts
• Computer Import in Ethiopia in 2010
Country of
Origin
Computer
import
China 62
Japan 47
Germany 35
India 16
USA 6

13. Bar graph
0
10
20
30
40
50
60
70
China Japan Germany India
Computer import

14. Measures of central tendency
• Mode shows values that occurs most
frequently
• is the only measure of central tendency that
can be interpreted sensibly
• Median is used to identify the mid point of
the data

15. Central Tendency ….
• Mean is a measure of central tendency
• includes all data values in its calculation
Mean = sum of observation (sum)/
Total no. of observation (frequency )
• The mean for grouped data is obtained from the following formula:
• where x = the mid-point of individual class
• f = the frequency of individual class
• N = the sum of the frequencies or total frequencies.
N
fx
x



16. Mean
• Mean is used to assess the association between two
variables.
• Assume an organization (X) that uses web based
service sales and the other organization (Y) using the
traditional rented shop sales office
• X average monthly sales is 10, 000 birr while Y monthly
sales is 7, 000 birr
• The mean has significant difference and we conclude
that use of web based sales service increase X
organization sales performance
• You can apply T-test to check its statistical significance

17. Advantages of Mean
• It should be rigidly defined.
• It should be easy to understand and compute.
• It should be based on all items in the data.
• Its definition shall be in the form of a mathematical
• formula.
• It should be capable of further algebraic treatment.
• It should have sampling stability.
• It should be capable of being used in further statistical
computations or processing
• However affected by extreme data values in skewed
distributions
• For Skewed distribution, use median than mean

18. Exercise
• Do the following exercise for the following IT staff
data for 13 organizations named as O1 to O13
• 25, 18, 20, 10, 8, 30, 42, 20, 53, 25, 10, 20, 42
• What is the mode?
• What is the median?
• What is the mean?
• Change into frequency table?
• Plot on bar graph? Pie chart?
• What you interpret from the data?

19. Measures of Dispersion
• The measure of central tendency serve to locate
the center of the distribution,
• This characteristic of a frequency distribution is
commonly referred to as dispersion.
• Small dispersion indicates high uniformity of the
items,
• Large dispersion indicates less uniformity.
• Less variation or uniformity is a desirable
characteristic

20. Type of measure of dispersion
• There are two types
1. Absolute measure of dispersion
2. Relative measure of dispersion.
• Absolute measure of dispersion indicates the amount
of variation in a set of values in terms of units of
observations. For example, if computers measured by
numbers, it shows dispersion by number
• Relative measures of dispersion are free from the units
of measurements of the observations. You may
measure dispersion by percentage
• Range is an absolute measure while coefficient of
variation is the relative measure

21. Dispersion …
• There are different type of dispersion measures
• We look at Standard Deviation and Coefficient of
variation
• Karl Pearson introduced the concept of standard
deviation in 1893
• Standard deviation is most frequently used one
• The reason is that it is the square–root of the
mean of the squared deviation
• Square of standard deviation is called Variance

22. Standard Deviation
• It is given by the formula
• Calculate the standard deviation from the
following data.
• 14, 22, 9, 15, 20, 17, 12, 11
• The Answer is 4.18
1
)
(
2

 
n
x
x



23. Interpretation
• We expect about two-thirds of the scores in a sample to lie
within one standard deviation of the mean.
• Generally, most of the scores in a normal distribution cluster
fairly close to the mean,
• There are fewer and fewer scores as you move away from the
mean in either direction.
• In a normal distribution, 68.26% of the scores fall within one
standard deviation of the mean,
• 95.44% fall within two standard deviations, and
• 99.73% fall within three standard deviations.

24. Advantage of SD
• Assume the mean is 10.0, and standard deviation is
3.36.
• one standard deviation above the mean is 13.36 and
one standard deviation below the mean is 6.64.
• The standard deviation takes account of all of the
scores and provides a sensitive measure of dispersion.
• it also has the advantage that it describes the spread of
scores in a normal distribution with great precision.
• The most obvious disadvantage of the standard
deviation is that it is much harder to work out than the
other measures of dispersion like rank and percentiles

25. Coefficient of Variation
• The Standard deviation is an absolute measure of
dispersion.
• However, It may not always applicable
• The standard deviation of number of computers cannot be
compared with the standard deviation of computer use of
students, as both are expressed in different units,
• standard deviation must be converted into a relative
measure of dispersion for the purpose of comparison --
coefficient of variation
• The is obtained by dividing the standard deviation by the
mean and multiply it by 100
coefficient of variation = X 100
x


26. Skewness
• skewness means ‘ lack of symmetry’ .
• We study skewness to have an idea about the
shape of the curve which we can draw with
the help of the given data.
• If in a distribution mean = median =mode,
then that distribution is known as symmetrical
distribution.
• The spread of the frequencies is the same on
both sides of the center point of the curve.

27. Symmetrical distribution
• Mean = Median = Mode

28. Negatively
skewed
distribution
Positively skewed distribution

29. Measures of Skewness
1. Karl – Pearason’ s coefficient of skewness
2. Bowley’ s coefficient of skewness
3. Measure of skewness based on moments
We see Karl- Pearson, read others from the textbook
• Karl – Pearson is the absolute measure of skewness = mean –
mode.
• Not suitable for different unit of measures
• Use relative measure of skewness -- Karl – Pearson’ s
coefficient of skewness, i.e
(Mean –Mode)/standard deviation
In case of ill defined mode, we use
3(Mean –median)/standard deviation

30. Kurtosis
• All the frequency curves expose different degrees
of flatness or peskiness – called kurtosis
• Measure of kurtosis tell us the extent to which a
distribution is more peaked or more flat topped
than the normal curve, which is symmetrical and
bell-shaped, is designated as Mesokurtic.
• If a curve is relatively more narrow and peaked at
the top, it is designated as Leptokurtic.
• If the frequency curve is more flat than normal
curve, it is designated as platykurtic.

31. Interpretation
• Real word things are usually have a normal
distribution pattern – Bell shape

32. Normal dist…
• This implies that
• 68% of the population is in side 1
• 95% of the population is inside 2
• 99% of the population is 3
• So you need to select a confidence limit to say
your sample is statistically significant or not
• For example, if more than 5% of the population
falls outside 2 standard deviation, the
difference between two groups of population is
not statistically significant

33. Correlation
• Correlation is used to measure the linear
association between two variables
• For example, assume X is IT skill and Y is IT
use. Is there association b/n these two
variables








2
2
)
(
)
(
)
(
)
(
y
y
x
x
y
y
x
x
r

34. Correlation …
• Correlation expresses the inter-dependence of
two sets of variables upon each other.
• One variable may be called as independent
variable (IV) and the other is dependent variable
(DV)
• A change in the IV has an influence in changing
the value of dependent variable
• For example IT use will increase organization
productivity because have better information
access and improve their skills and knowledge

35. Correlation Lines

36. Correlation Lines
Perfect
Correlation
No
Correlation

37. Type of Correlation
1. Simple
2. Multiple correlation
3. Partial correlation
• In simple correlation, we study only two variables.
• For example, number of computers and organization
efficiency
• In multiple correlation we study more than two variables
simultaneously.
• For example, usefulness and easy of use and IT adoption
• Partial correlation, it refers to the study of two variables
excluding some other variables

38. Karl pearson’ s coefficient of
correlation
• Karl pearson, a great biometrician and statistician, suggested
a mathematical method for measuring the magnitude of
linear relationship between the two variables
• Karl pearson’ s coefficient of correlation is the most widely
used method of correlation
where X = x - x , Y = y - y
y
x
XY
r
x
n
XY
r
y
2
2
.








39. Exercise
7 4 6 2 1 9 3 8 5
X 56 78 65 89 93 24 87 44 74
Y 34 65 67 90 86 30 80 50 70
8 6 5 1 2 9 3 7 4
D -1 -2 1 1 -1 0 0 1 1
D2 1 4 1 1 1 0 0 1 1
Calculate the correlation for the following given data

40. Spear Man Rank Correlation
• Developed by Edward Spearman in 1904
• It is studied when no assumption about the
parameters of the population is made.
• This method is based on ranks
• It is useful to study the qualitative measure of
attributes like honesty, colour, beauty,
intelligence, character, morality etc.
• The individuals in the group can be arranged in
order and there on, obtaining for each individual
a number showing his/her rank in the group

41. Formula
• Where D2 = sum of squares of differences between the
pairs of ranks.
• n = number of pairs of observations.
• The value of r lies between –1 and +1. If r = +1, there is
complete agreement in order of ranks and the direction of
ranks is also same. If r = -1, then there is complete
disagreement in order of ranks and they are in opposite
directions.
n
n
D
r




3
2
6
1

42. Advantage of Correlation
• It is a simplest and attractive method of finding the
nature of correlation between the two variables.
• It is a non-mathematical method of studying
correlation. It is easy to understand.
• It is not affected by extreme items.
• It is the first step in finding out the relation between
the two variables.
• We can have a rough idea at a glance whether it is a
positive correlation or negative correlation.
• But we cannot get the exact degree or correlation
between the two variables

43. The Pearson Chi-square
• it is the most common coefficient of association,
which is calculated to assess the significance of
the relationship between categorical variables.
• It is used to test the null hypothesis that
observations are independent of each other.
• It is computed as the difference between
observed frequencies shown in the cells of cross-
tabulation and expected frequencies that would
be obtained if variables were truly independent.

44. Chi-square …
Obse Exp. differe
nce
M 3 6 -3
T 5 6 -1
W 7 6 1
Th 6 6 0
F 9 6 3
Tot 1
Where O is observed value
E is expected value
X2 is the association
Where is X2 value and its significance
level depend on the total number of
observations and the number of cells
in the table
Degree of freedom is no. of variable
minus from no. of observations
DF = (r - 1) * (c - 1)
where r is the number of levels for one categorical
variable, and c is the number of levels for the other
categorical variable.
E
E
O
x  

)
(
2

45. Assumptions
• Ensure that every observation is independent of every other
observation; in other words, each individual should be counted
once and in only one category.
• Make sure that each observation is included in the appropriate
category; it is not permitted to omit some of the observations.
• The total sample should exceed 20; otherwise, the chi-squared test
as described here is not applicable. More precisely, the minimum
expected frequency should be at least 5 in every use.
• Remember that showing that there is an association is not the
same as showing that there is a causal effect; for example, the
association between a healthy diet and low cholesterol does not
demonstrate that a healthy diet causes low cholesterol.

46. Parametric Tests
There are many test but some examples are
• Pearson correlation coefficient
• ANOVA
• Linear regression

47. ANOVA
• Analysis of variance (ANOVA) is one of the statistical
tools developed by Professor R.A. Fisher
• ANOVA is used to test the homogeneity of several
population means.
• Five different software systems were used by five
business organizations to increase the organizations’
profit such by increasing customer satisfaction, sales
and the like.
• In such a situation, we are interested in finding out
whether the effect of these software systems on profit
is significantly different or not.

48. F Statistic
• Like any other test, the ANOVA test has its
own test statistic
• The statistic for ANOVA is called the F
statistic, which we get from the F Test
• The F statistic takes into consideration:
– number of samples taken (I) or groups
– N is total samples for all
groups
– sample size of each sample (n1, n2, …, nI)
– means of the samples ( )
– standard deviations of each sample (s1, s2, …,
sI)
3
X
2,
x
,
1
X

49. F Statistic Equation
I
N
s
n
s
n
s
n
I
x
x
n
x
x
n
x
x
n
F
I
I
I
I














 2
2
2
2
2
1
1
2
2
2
2
2
1
1
)
1
(
...
)
1
(
)
1
(
1
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(
...
)
(
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(
Rewritten as a formula, the F Statistic
looks like this:
Weighing
Weighing
Standard Deviations (Squared)
Means (Squared)

50. Calculate Anova for the Following
group
The Data for three organizations IT use is
Level n Mean StDev
X1 28 27.1 hrs 2.6 hrs
X2 26 20.4 hrs 2.9 hrs
X3 8 23.1 hrs 2.5 hrs

51. The F Statistic

52. Regression
• Regression is used to estimate (predict) the value of one
variable given the value of another.
• The variable predicted on the basis of other variables is
called the “dependent” or the ‘ explained’ variable and
the other the ‘ independent’ or the ‘ predicting’ variable.
• The prediction is based on average relationship derived
statistically by regression analysis.
• For example, if we know that advertising and sales are
correlated we may find out expected amount of sales f or
a given advertising expenditure or the required amount
of expenditure for attaining a given amount of sales.

53. Regression
• Regression is the measure of the average
relationship between two or more variables in
terms of the original units of the data.
• Type of regression
1. Simple and Multiple
2. Linear and Non –Linear
3. Total and Partial

54. Simple and Multiple:
• In case of simple relationship only two variables are
considered, for example, the influence of advertising
expenditure on sales turnover.
• In the case of multiple relationship, more than two
variables are involved. On this while one variable is a
dependent variable the remaining variables are
independent ones.
• For example, the turnover (y) may depend on
advertising expenditure (x) and the income of the
people (z).
• Then the functional relationship can be expressed as
y = f (x,z).

55. Linear and Non-linear
• The linear relationships are based on straight-line trend, the
equation of which has no-power higher than one. But,
remember a linear relationship can be both simple and
multiple.
• Normally a linear relationship is taken into account because
besides its simplicity, it has a better predictive value, a linear
trend can be easily projected into the future.
• In the case of non-linear relationship curved trend lines are
derived. The equations of these are parabolic.

56. Total and Partial
• In the case of total relationships all the important
variables are considered.
• Normally, they take the form of a multiple
relationships because most economic and
business phenomena are affected by multiplicity
of cases.
• In the case of partial relationship one or more
variables are considered, but not all, thus
excluding the influence of those not found
relevant for a given purpose.

57. Regression analysis
• The goal of regression analysis is to develop a
regression equation from which we can
predict one score on the basis of one or more
other scores.
• For example, it can be used to predict a job
applicant's potential job performance on the
basis of test scores and other factors

58. Linear regression equation
• Linear regression equation of Y on X is
Y = a + bX ……. (1)
• And X on Y is
X = a + bY……. (2)
Where a, b are constants.
• In a regression equation, y is the dependent
variable or criterion variable, or outcome
variable we would like to predict.
• X represents the variable we are using to
predict y; x is called the predictor variable.

59. • a is called the regression constant (or beta-
zero), and is the y-intercept of the line that best
fits the data in the scatter plot;
• It is the regression coefficient,
• B is the slope of the line that best represents the
relationship between the predictor variable (x)
and the criterion variable (y).
• You can use multiple regression
Y= a+bx1+bx2+….+bxn

60. Example
• Find the Two Equation Regression for the
following data
X 6 2 10 4 8
Y 9 11 5 8 7

62. Solution
• Regression equation of Y on X is Y = a + bX and
• the normal equations are
∑Y = na + b ∑X
∑XY = a ∑X + b ∑X2
• Where n= 5
∑Y= 40
∑X= 30
∑XY = 214
∑X2 = 220

63. Regression
• Substituting the values, we get
• 40 = 5a + 30b …… ( equation 1)
• 214 = 30a + 220b ……. ( equation 2)
• Multiplying (equation 1) by 6
• 240 = 30a + 180b……. ( equation 3)
• Subtract equation 3 from equation 2
• You get - 26 = 40b or b = - 26/40
b = - 0.65
• Now, substituting the value of ‘ b’ in equation (1)
• 40 = 5a – 19.5
• 5a = 59.5
• a = 59.5/5 or a = 11.9

64. Regression
• Hence, required regression line Y on X is
• Y = 11.9 – 0.65 X.
• This implies that
• 11.9 is a constant or intercept. When X is zero, the
Value of Y is 11.9
• 0.65 is the slope. This implies that 1 unit change in X
brings 0.65 minus the constant (11.9) change on Y
• Likewise 2 units change in X results 1.30 minus the
constant change in Y
• This is generalized for the population by checking the
statistical significance

65. Statistical significance
• What happens after we have chosen a statistical test,
and analysed our data, and want to interpret our
findings? We use the results of the test to choose
between the following:
1. Alternative (Experimental) hypothesis (e.g. loud noise
disrupts learning).
2. Null hypothesis, which asserts that there is no difference
between conditions (e.g. loud noise has no effect on
learning).
• If the statistical test indicates that there is only a small
probability of the difference between two conditions
(e.g. loud noise vs. learing), then we reject the null
hypothesis in favor of the experimental hypothesis

66. Statistical ….
• Psychologists generally use the 5% (0.05) level of statistical
significance. What this means is that the null hypothesis is rejected
(and the experimental hypothesis is accepted) if the probability that
the results were due to chance alone is 5% or less. This is often
expressed as p <= 0.05
• It is possible to use other statistical significance such as 10%, when we
have greater confidence
• But the null hypothesis can be rejected with greater confidence
• This leads to Type I and Type II errors
– Type I error: we may reject the null hypothesis in favour of the
experimental hypothesis even though the findings are actually due to
chance; the probability of this happening is given by the level of statistical
significance that is selected.
– Type II error: we may retain the null hypothesis even though the
experimental hypothesis is actually correct.

67. Statistical significance …
• Researchers are interested not only in the correlation
between two variables, but also in whether the value of r
they obtain is statistically significant.
• Statistical significance exists when a correlation coefficient
calculated on a sample has a very low probability of being
zero in the population.
• Assume we get a correlation between X and Y is 0.4 in our
sample.
• How do we now if this r is not zero (r=0.0) if we take the
census of the entire population.
• The probability that our correlation is truly zero in the
population is sufficiently low (usually less than .05),
• we refer this probability as statistically significant

68. Factors affecting statistical significance
• Sample size
• Assume that, unknown to each other, you and I independently
calculated the correlation between shyness and self-esteem and
that we both obtained a correlation of -.50.
• However, your calculation was based on data from 300 participants,
whereas my calculation was based on data from 30 participants.
• Which of us should feel more confident that the true correlation
between shyness and self-esteem in the population is not .OO?
• You can probably guess that your sample of 300 should give you
more confidence in the value of r you obtained than my sample of
30.
• Thus, all other things being equal, we are more likely to conclude
that a particular correlation is statistically significant the larger our
sample is.

69. Factors …
• Magnitude of the correlation. For a given sample
size, the larger the value of r we obtain, the less
likely it is to be .00 in the population.
• Imagine you and I both calculated a correlation
coefficient based on data from 300 participants;
• your calculated value of r was .75, whereas my
value of r was .20. You would be more confident
that your correlation was not truly .00 in the
population than I would be.

70. Factors ..
• Level of confidence
• It indicates how we are careful we want to be not to
draw an incorrect conclusion about whether the
correlation we obtain could be zero in the population.
• Typically, researchers decide that they will consider a
correlation to be significantly different from zero if
there is less than a 5% chance (that is, less than 5
chances out of 100) that a correlation as large as the
one they obtained could have come from a population
with a true correlation of zero.

71. Techniques
• There are two methods to make
generalizations about the population
1. Mean Method – by confidence Interval
2. Statistical significance method – using different
inferential statistics such as Chai Square, ANOVA,
regression, etc

72. Confidence interval Method
• When you compute a confidence interval on the mean, you compute the
mean of a sample in order to estimate the mean of the population.
• Clearly, if you already knew the population mean, there would be no need
for a confidence interval.
• Assume that the weights of 10-year-old children are normally distributed
with a mean of 90 and a standard deviation of 36.
• What is the sampling distribution of the mean for a sample size of 9?
• The formula (standard error ) is given by
• The sampling distribution of the mean has a mean of 90 and a standard
deviation of 36/3 = 12.
• Note that the standard deviation of a sampling distribution is its standard
error.

73. Confidence Interval
• The shaded area represents the middle 95% of the
distribution and stretches from 66.48 to 113.52. These
confidence limits were computed by adding and subtracting
1.96 standard deviations to/from the mean of 90 as follows:
90 - (1.96)(12) = 66.48
90 + (1.96)(12) = 113.52
The value of 1.96 is based on the fact that 95% of the area of
a normal distribution is within 1.96 standard deviations of the
mean; 12 is the standard error of the mean.

74. Statistical Significance
• Formulas and tables for testing the statistical significance of
correlation coefficients can be found in many statistics
books
• Imagine that you obtained a value of r = .32 based on a
sample of 100 participants.
• Looking down the left-hand column, find the number of
participants (100).
• Looking at the other column, we see that the minimum
value of r that is significant with 100 participants is .16.
• Because our correlation coefficient (.32) exceeds .16, we
conclude that the population correlation is very unlikely to
be zero (in fact, there is less than a 5% chance that the
population correlation is zero).

75. Statistical …
• Keep in mind that, with large samples, even very small
correlations are statistically significant
• Thus, finding that a particular r is significant tells us only
that it is very unlikely to be .00 in the population; it does
not tell us whether the relationship between the two
variables is a strong or an important one.
• The strength of a correlation is assessed only by its
magnitude, not whether it is statistically significant.
• As a rule of thumb, behavioral researchers tend to regard
correlations at or below about .10 as weak in magnitude
(they account for only 1 % of the variance), correlations
around .30 as moderate in magnitude, and correlations
over .50 as strong in magnitude

76. Exercise on Data Analysis
X 4 6 3 10 8
Y 5 8 4 7 9
The following data shows number of hours used study on Internet and the
students score on a test out of 10.
1. calculate the correlation between the two variables.
2. Compute the regression equation and estimate the value of Y for a given
value of X in table 2
3. Calculate the mean of student test
4. Calculate the standard deviation
5. Calculate the confidence interval for the total student population mean
score
6. Do you think that Internet use has an impact on student performance
X 8 10 7 20 15

77. References
• http://onlinestatbook.com/2/normal_distribut
ion/normal_distribution.html