2.  Bilal Khan Niazi 11-Arid-1314
 Naveed Ahmed 11-Arid-1322
 Asad Mehmood 11-Arid-1294
 Arslan Akbar 11-Arid-1293
 Salik Atta 11-Arid-1326
 Zeeshan Gohar 11-Arid-1335
 Ijaz-ull-Hassan 11-Arid-1185

3.  Group-4
 QUESTIONNAIRE
 System Quality of University Computer Service System
 Section I
 Respondent profile
 1. Gender? Male Female
 2. Age? 15-17 18-20 21-23 24-26 More than 35
 3. CGPA: 0.5-1 1-1.5 1.5-2 2-2.5 2.5-3 3-3.5 3.5-4
 4. What is your class?
BBA MBA MS MDM Other

4.  Section II
 (Completely Dissatisfied = 1, Dissatisfied = 2, Neutral = 3,
Satisfied = 4, completely satisfied = 5)
System Quality 1 2 3 4 5
1. The usefulness of system functions.
2. The friendliness of users interfaces.
3. The up-to-date of platforms.
4. The necessity of system functions.
5. The stability of systems.
6. The response time of system.
7. The duration of system update.

5.  Business Statistics:
Statistics is the study of how to collect, organize, analyze, and
interpret numerical information from data. Descriptive statistics
involves methods of organizing, picturing and summarizing
information from data. Inferential statistics involves methods of
using information from a sample to draw conclusions about the
Population.
Individuals and Variables
Individuals are the people or objects included in the study. A
variable is the characteristic of the individual to be measured or
observed.
There is no assumption in the descriptive statistics. It is related
to the facts and figure.
Descriptive statistics measure the central tendency
(Mean median, mode, percentile, and quartile)
Measure of desperation (Range, inter-quarter range, variance,
standard deviation, coefficient of variable)

6. Inferential statistics:
Inferential Statistics: A decision, estimate, prediction, or
generalization about a population, based on a sample.
Inferential deal with the assumption and future forecasting.
 Data and data set:
Data is a raw facts and figure. That are collected, summarized,
analyzed, and interpreted.
The data collected in a particular study are referred to as the
data set.

7.  Scales of measurement:
 Scales of measurement include:
◦ Nominal
◦ Ordinal
◦ Interval
◦ Ratio
 The scale determines the amount of information contained in
the data.
 The scale indicates the data summarization and statistical
analyses that are most appropriate.

8.  Nominal Scale:
 Data that is classified into categories and cannot be arranged
in any particular order. For example male-female, Pakistani
etc.
 Ordinal scale:
 It categorizes and ranks the variables according to the
preferences. For example from best to worst, first to last, a
numeric code may be used.
 Interval scale:
 To put the interval in the order data. It fulfills the
characteristics of nominal and ordinal scale.
 Ratio scale:
 The data have all the properties of interval data and the ratio
of two values is meaningful. Variables such as distance,
height, weight, and time use the ratio scale. This scale must
contain a zero value.

9. I. Qualitative data
II. Quantitative data
 Qualitative data:
Qualitative is related to the non-numeric form of data. For
example, male and female, members of the family, eye color.
 Quantitative data:
Quantitative data is related to the numeric form of data. For
example, age, CGPA, income.
Quantitative data indicate either how many or how much.
Quantitative data are always numeric.

10.  Further qualitative data has two types
I. Discrete qualitative data
II. Continues qualitative data
 Discrete qualitative data: Quantitative data that measure how
many are discrete.(how many students in the class)
 Continues data:
Quantitative data that measure how much are continuous. (GPA,
income)
 Cross-Sectional and Time Series Data:
 Cross-sectional data:
Are collected at the same or approximately the same point in time.
Example: data detailing the number of building permits issued in
June 2000
 Time series data:
Are collected over several time periods.
Example: data detailing the number of building permits issued in
Travis County, Texas in each of the last 36 months

11.  Descriptive Statistics:
 Descriptive statistics are the tabular, graphical, and numerical
methods used to summarize data.
 Statistical Inference:
 Statistical inference is the process of using data obtained
from a small group of elements (the sample) to make
estimates and test hypotheses about the characteristics of a
larger group of elements (the population).

13.  Frequency Distribution
 Relative Frequency distribution
 Percent frequency
 BAR GRAPH
 pie chart

14.  Frequency Distribution
A frequency distribution is tabular summary of showing the
number(frequency) of items in each of several non over
lapping classes.
 Relative Frequency
A Relative Frequency distribution give a tabular summary of
data showing the relative frequency for each class.
 Percent frequency
Percent frequency summarize the percent frequency of data for
each class.
 BAR GRAPH
A bar graph is a graphical device for depicting qualitative data.

15.  Pie Chart:-
The pie chart is a commonly used graphical device for
presenting relative frequency distributions for qualitative
data.

16.  Frequency Distribution
 Relative Frequency
 Percent Frequency Distributions
 Cumulative Distributions
 Dot Plot
 Histogram
 Ogive/ Frequency Polygon

17. Frequency Distribution
A frequency distribution is tabular summary of showing the
number(frequency) of items in each of several non over
lapping classes.
Classes Frequency
Male 36
Female 14
Total 50
 Classes Frequency
21-23 25
24-26 17
>26 8
Total 50

18. Relative Frequency
A Relative Frequency distribution give a
tabular summary of data showing the
relative frequency for each class.

19. Percent frequency
Percent frequency summarize the percent
frequency of data for each class.
Classes Percent Frequency
Male 72
Female 28
Total 100
Classes Percent Frequency
21-23 50.00
24-26 34.00
>26 16.00
Total 100.00

20.  Cumulative frequency distribution -- shows the number of
items with values less than or equal to the upper limit of each
class.
Classes C.F.D
Male 72
Female 100
Classes C.F.D
21-23 50.0
24-26 84.0
>26 100.0

21.  Cumulative relative frequency distribution -
- shows the proportion of items with values
less than or equal to the upper limit of each
class.
 Cumulative percent frequency distribution -
- shows the percentage of items with values
less than or equal to the upper limit of each
class.

22.  Dot Plot
One of the simplest graphical summaries of data is a dot plot.
A horizontal axis shows the range of data values.
Then each data value is represented by a dot placed above
the axis.
 Histogram
Another common graphical presentation of quantitative data
is a histogram.
The variable of interest is placed on the horizontal axis.
A rectangle is drawn above each class interval’s frequency,
relative frequency, or percent frequency.
Unlike a bar graph, a histogram has no natural separation
between rectangles of classes.

23.  Ogive/ Frequency Polygon
An ogive/ Polygon is a graph of a cumulative distribution.
The data values are shown on the horizontal axis.
Shown on the vertical axis are the:
◦ cumulative frequencies, or
◦ cumulative relative frequencies, or
◦ cumulative percent frequencies
The frequency (one of the above) of each class is plotted as a
point.
The plotted points are connected by straight lines.
 Scatter Diagram:-
Is a graphical presentation of the relationship between two
quantitative variables.

24.  Descriptive Statistics: Numerical Methods:
Measures of Location
Mean
Median
Mode
Percentile
Quartile
Mean:-
Mean are average value of all observation. The mean provides a
measure of central location for the data.
n
xi
Sample Mean= i 1 x1 x2  xn
x
n n

25.  Sample Mean:- xi
x
n
 Where the numerator is the sum of values of n observations,
or:
 Median:-
xi x1 x2 ... xn
 Median is the value in the middle when the data are arranged
in ascending order with an odd number of observations the
mean is the middle value. An even number of observation has
no single middle value in this case simply we average the
middle two observations.
 Mode:-
 The mode is the value that occurs with greatest frequency.
 Value that occurs most often
 There may be no mode
 There may be several modes

26. Percentiles:-
 The pth percentile is a value such that at least p percent of
the observations are less than or equal to this value at least
(100-p) percent of the observations are greater than or equal
to this value.
 Calculating the Pth Percentile:-
 Step 1. Arrange the data in ascending order
 Step 2. Compute an index i
p
 Step 3. 100
n
If i is not integer then round up. The next integer greater than i
denotes the position of the pth percentile .
If i is an integer the pth percentile is the average of the values
in positions i and i+1.

27.  Quartile:-
It is often desirable to divide data in four parts, with each part
containing approximately one-fourth, or 25% of the
observations.
Q1= 25th percentile
Q2= 50th Percentile (also the Median)
Q3= 75th percentile
 Measures of Variability
 Range
 Interquartile Range
 Variance
 Standard Deviation
 Coefficient of Variation

28.  Range:
Range is the difference largest value and smallest value
Range = Largest Value – Smallest Value
 Interquartile Range:
The difference between third quartile Q3 and first quartile Q1
IQR= Q3 – Q1
 Variance:
Variance is based on difference between value of each
observation and the mean.
Population Variance:
2 ( xi x ) 2
 Sample Variance= s
n 1

29.  Standard Deviation:
Standard deviation is defined to be positive square root of the
variance.
 If the data set is a sample, the standard deviation is denoted
s.
2
s s
 If the data set is a population, the standard deviation is
denoted (sigma).
2

30.  Coefficient of Variation:
 In descriptive statistics that indicates how large a standard
deviation is relative to the mean.
s
CV 100%
 Sample= x
σ
Population= CV 100%

μ

31.  Measure of Distribution Shapes:-
 Z-Score
 Outliers
 Z-Score:
Z-score is often called the standardized value. The z-score
can be interpreted as the number of standard deviation is
from the mean.
xi x
zi
s
 Outliers:
Sometimes a data set will have one or more observation with
unusually large or unusually small values. These extreme
values are called outliers. If the value is greater than ±3 then
outlier exists.

32.  Exploratory Data Analysis:
 Five Number Summary:
 Smallest Value
 First Quartile
 Median
 Third Quartile
 Largest Value
 Measure of Association between Two Variables:
 Covariance
 Interpretation of Covariance
 Correlation Coefficient

33.  Covariance:-
 The covariance is a measure of the linear association between
two variables.
 Positive values indicate a positive relationship.
 Negative values indicate a negative relationship.
 If the data sets are samples, the covariance is denoted by sxy.
( xi x )( yi y )
s xy
n 1
 If the data sets are populations, the covariance is denoted by
( xi x )( yi y )
xy
N

34.  Interpretation of Covariance:
It tells us the relation between two variables is positive or
negative.
 Correlation Coefficient:
 The coefficient can take on values between -1 and +1.
 Values near -1 indicate a strong negative linear relationship.
 Values near +1 indicate a strong positive linear relationship.
 If the data sets are samples, the coefficient is rxy.
s xy
rxy
sx s y
 If the data sets are populations, the coefficient is
xy
xy
x y

36. Frequency Distribution W R T Gender
CLASSES
Cumulative
Frequency Percent Percent
Male 36 72.0 72.0
Female 14 28.0 100.0
Total 50 100.0

37. Classes Cumulative
Frequency Percent Percent
21-23 25 50.0 50.0
24-26 17 34.0 84.0
>26 8 16.0 100.0
Total 50 100.0

38. Classes Cumulative
Frequency Percent Percent
1-1.5 4 8.0 8.0
1.5-2 6 12.0 20.0
2-2.5 12 24.0 44.0
2.5-3 11 22.0 66.0
3-3.5 1 2.0 68.0
3.5-4 16 32.0 100.0
Total 50 100.0

39. BBA 15 30.0 30.0 30.0
MBA 14 28.0 28.0 58.0
MS 5 10.0 10.0 68.0
MDM 4 8.0 8.0 76.0
OTHERS 12 24.0 24.0 100.0
Total 50 100.0 100.0

40. Classes Cumulative
Frequency Percent Percent
Completely 20 40.0 40.0
Dissatisfied
Disagree 8 16.0 56.0
Neutral 11 22.0 78.0
Satisfied 9 18.0 96.0
completely 2 4.0 100.0
satisfied
Total 50 100.0

41. Classes Cumulative
Frequency Percent Percent
Completely 7 14.0 14.0
Dissatisfied
Disagree 16 32.0 46.0
Neutral 10 20.0 66.0
satisfied 12 24.0 90.0
completely 5 10.0 100.0
satisfied
Total 50 100.0

42. Classes Cumulative
Frequency Percent Percent
Completely 9 18.0 18.0
Dissatisfied
Disagree 8 16.0 34.0
Neutral 21 42.0 76.0
Satisfied 9 18.0 94.0
completely 3 6.0 100.0
satisfied
Total 50 100.0

43. Classes Cumulative
Frequency Percent Percent
Completely 5 10.0 10.0
Dissatisfied
Disagree 11 22.0 32.0
Neutral 14 28.0 60.0
satisfied 12 24.0 84.0
completely 8 16.0 100.0
satisfied
Total 50 100.0

44. Classes Cumulative
Frequency Percent Percent
Completely 7 14.0 14.0
Dissatisfied
Disagree 11 22.0 36.0
Neutral 14 28.0 64.0
Satisfied 9 18.0 82.0
completely 9 18.0 100.0
satisfied
Total 50 100.0

45.  GENDER
Std.
Minim Maxim Deviati Varian
Classes N Range um um Mean on ce
Freque
ncy
Distribu 50 1.00 1.00 2.00 1.2800 .45356 .206
tion W
RT
Gender
Valid N
(listwis 50
e)

46. Classes Std.
Minimu Maximu Deviatio Varianc
N Range m m Mean n e
Frequen
cy
Distributi 50 2.00 3.00 5.00 3.6600 .74533 .556
on W R
T Age
Valid N 50
(listwise)

47. Classes Std.
Minimu Maximu Deviatio Varianc
N Range m m Mean n e
Frequen
cy
Distributi 50 5.00 2.00 7.00 4.9400 1.67100 2.792
on W R
T CGPA
Valid N 50
(listwise)

48. Classes Std.
Minimu Maximu Deviatio Varianc
N Range m m Mean n e
Frequen
cy
Distributi 50 4.00 1.00 5.00 2.6800 1.57065 2.467
on W R
T Class
Valid N 50
(listwise)

49. Classes Std.
Minimu Maximu Deviatio Varianc
N Range m m Mean n e
The
usefulne
ss of 50 4.00 1.00 5.00 2.3000 1.28174 1.643
system
function
s.
Valid N 50
(listwise)

50. Classes Std.
Minimu Maximu Deviatio Varianc
N Range m m Mean n e
The up-
to-date
of 50 4.00 1.00 5.00 2.7800 1.13011 1.277
platform
s.
Valid N 50
(listwise)

51. Classes Std.
Minimu Maximu Deviatio Varianc
N Range m m Mean n e
The
necessit
y of 50 4.00 1.00 5.00 3.1400 1.22907 1.511
system
function
s.
Valid N 50
(listwise)

52. Clas Std.
Minimu Maximu Deviatio Varianc
ses N Range m m Mean n e
The
stability
of 50 4.00 1.00 5.00 3.0400 1.30868 1.713
systems
.
Valid N 50
(listwise)

53. The Usefulness of System Functions
Completely Dissatisfied
Disagree
Neutral
Satisfied
Completely Satisfied