It includes introduction to quantitative techniques; Meaning, Importance applications and Limitations of statistics. Primary vs Secondary Data and their collection methods, Different graphs and their examples. Classification of data, types of data/series etc.
18. COURSE – UNIT 1
Introduction to statistics: Meaning, scope, importance
and limitations, applications of inferential statistics in
managerial decision-making.
Analysis of data: Source of data, collection,
classification, tabulation, depiction of data.
Measures of Central tendency: Arithmetic, weighted,
geometric mean, median and mode.
Measures of Dispersion: Range, Quartile deviation,
Mean deviation, Standard deviation Coefficient of
variation, Skewness and Kurtosis 18
BirinderSingh,AssistantProfessor,PCTE
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19. COURSE – UNIT 2
Sampling and Sampling Distribution: Concept
and definitions, census and sampling, probability
samples and non-probability samples, relationship
between sample size and errors, simple numerical
only.
Hypothesis Testing: Sampling theory, Formulation
of Hypotheses, Application of Z-test, t-test, F-test
and Chi-Square test, Techniques of association of
attributes & testing. Test of significance for small
sample
19
BirinderSingh,AssistantProfessor,PCTE
Ludhiana
20. COURSE – UNIT 3
Correlation Analysis: Significance, types, methods of
correlation analysis: Scatter diagrams, Graphic method,
Karl Pearson’s correlation co-efficient, Rank correlation
coefficient, Properties of Correlation.
Regression analysis: meaning, application of
regression analysis, difference between correlation &
regression analysis, regression equations, standard error
and Regression coefficients.
Index Number: Definition, and methods of construction,
tests of consistency, base shifting, splicing and deflation,
problems in construction and importance of index number.
20
BirinderSingh,AssistantProfessor,PCTE
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21. COURSE – UNIT 4
Time Series Analysis: Meaning, Components and various
methods of time series analysis, Trend analysis: Least Square
method - Linear and Non- Linear equations, Applications in
business decision-making.
Theory of Probability: Definition, basic concepts, events and
experiments, random variables, expected value, types of
probability, classical approach, relative frequency and subjective
approach to probability, theorems of probability, addition,
Multiplication and Bayes Theorem and its application.
Theoretical Distributions: Difference
between frequency and probability distributions, Binomial,
Poisson and normal distribution
Note: Relevant Case Studies should be discussed in class
21
BirinderSingh,AssistantProfessor,PCTE
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23. STATISTICS
It is set of procedures and rules…for reducing large
masses of data to manageable proportions and for
allowing us to draw conclusions from those data
It helps businessmen in drawing inferences from
the available data and take the decisions
accordingly
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23
24. MEANING
PLURAL SENSE – It refers to numerical statements
of facts relating to any field of enquiry such as data
relating to production, income, expenditure,
population, prices, etc.
SINGULAR SENSE – It refers to a science in which
we deal with the techniques or methods for
collecting, classifying, presenting, analyzing and
interpreting the data.
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25. WHAT CAN STATS DO?
Make data more manageable
Group of numbers:
6, 1, 8, 3, 5, 4, 9
Average is: 36/7 = 5.14
Graphs:
0
10
20
30
40
50
60
70
80
90
1st Qtr 2nd Qtr 3rd Qtr 4th Qtr
East
West
North
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25
26. WHAT CAN STATS DO?
Allow us to draw conclusions from the data
Sachin’s Scores: 60, 100, 80, 30, 50
Sachin’s Average is 350/5 = 70
Sehwag’s Scores: 10, 150, 25, 5, 110
Sehwag’s Average is 300/5 = 60
Allows us to do this objectively and quantitatively
BirinderSingh,AssistantProfessor,PCTE
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26
27. APPLICATIONS OF STATISTICS IN DAY TO DAY LIFE
& IN BUSINESS
Weather Forecasts
Emergency Preparedness
Political Campaigns
Insurance
Consumer Goods
Family Budgets
Consumer Goods
Stock Market
Quality Testing
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27
28. FEATURES OF STATISTICS AS SCIENCE
(STAGES OF STATISTICS)
Collection of Data
Organization of Data
Presentation of Data
Analysis of Data
Interpretation of Data
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29. SCOPE OF STATISTICS
Scope
Nature
Science Art
Subject
Matter
Descriptive
Statistics
Inferential
Statistics
Limitations
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29
30. FUNCTIONS OF STATISTICS
To Present Facts in Definite Form
Precision to the Facts
Comparisons
Forecasting
Policy Making
Enlarges Knowledge
To Measure Uncertainty
Establishes relationship between facts
Helps other sciences in testing their laws
BirinderSingh,AssistantProfessor,PCTE
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30
32. LIMITATIONS OF STATISTICS
Study of numerical facts only
Study of aggregates only
Homogeneity of Data
Can be used only be experts
Qualitative Aspect Ignored
It does not depict entire story of phenomenon
Misuse of Statistics is possible
Results are true only on average
Statistical results are not always beyond doubt; only
means & not a solution
BirinderSingh,AssistantProfessor,PCTE
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32
34. TYPES OF DATA
Primary Data
• Data collected by the investigator for his own purpose,
for the first time. It is also called first hand data
• Primary data includes information collected from
interviews, experiments, surveys, questionnaires, focus
groups and measurements
Secondary Data
• It is widely available and obtained from another party. It
is also called second hand data
• Secondary data can be found in publications, journals
and newspapers.
BirinderSingh,AssistantProfessor,PCTE
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34
35. PRIMARY VS SECONDARY DATA
Basis for Comparison Primary Data Secondary Data
Data Real time data Past data
Process Very involved Quick and easy
Source
Surveys, observations,
experiments, questionnaire,
personal interview, etc.
Government publications,
websites, books, journal
articles, internal records etc.
Cost effectiveness Expensive Economical
Collection time Long Short
Specific
Always specific to the
researcher's needs.
May or may not be specific to
the researcher's need.
Available in Crude form Refined form
Accuracy and Reliability More Relatively less
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35
36. METHODS OF COLLECTING PRIMARY DATA
Observation Method
• Structured & Unstructured Observation
• Participant, Non Participant & Disguised Observation
• Controlled & Uncontrolled Observation
Interview Method
• Personal Interview
• Telephonic Interview
• Video Conferencing
Questionnaire
Schedules filled through Enumerators
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36
37. OBSERVATION METHOD
The observation method is the most commonly used
method specially in studies relating to behavioral
science.
Observation becomes a scientific tool and the method of
data collection for the researcher, when it serves a
formulated research purpose, is systematically planned
and recorded and is subjected to checks and controls
on validity and reliability.
It is also a process of recording the behavior patterns of
people, objects, and occurrences without questioning or
communicating with them.
BirinderSingh,AssistantProfessor,PCTE
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37
38. OBSERVATION METHOD
Structured Observation: This means observation of an
event personally by the observer when it takes place. This
method is flexible and allows the observer to see and record
subtle aspects of events and behaviour as they occur. He is
also free to shift places, change the focus of the observation.
Example: Observer is physically present to monitor
Unstructured Observation: This does not involve
the physical presence of the observer, and the recording
is done by mechanical, photographic or electronic
devices. Example : Recording customer and employee
movements by a special motion picture camera
mounted in a department of large store.
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38
39. OBSERVATION METHOD
Participant Observation: In this observation, the
observer is a part of the phenomenon or group
which observed and he acts as both an observer
and a participant.
Example: a study of tribal customs by an
anthropologist by taking part in tribal activities like folk
dance. The person who are observed should not be
aware of the researcher’s purpose. Then only their
behaviour will be ‘natural.’
BirinderSingh,AssistantProfessor,PCTE
Ludhiana
39
40. OBSERVATION METHOD
Non - Participant Observation: In this method, the
observer stands apart and does not participate in
the phenomenon observed. Naturally, there is no
emotional involvement on the part of the observer.
This method calls for skill in recording observations
in an unnoticed manner.
Example: Use of recording devices to examine the
details of how people talk and behave together.
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40
41. OBSERVATION METHOD
Disguised Observation: In this method, the
observer observes in such a manner that his
presence is unknown to the people he is observing.
Example: Investigation done in Police Custody
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41
42. OBSERVATION METHOD
Controlled Observation: Controlled observation is
carried out either in the laboratory or in the field. It is
typified by clear and explicit decisions on what, how, and
when to observe. It is primarily used for inferring
causality, and testing casual hypothesis.
Uncontrolled Observation: This does not involve
over extrinsic and intrinsic variables. It is primarily used
for descriptive research. Participant observation is a
typical uncontrolled one.
BirinderSingh,AssistantProfessor,PCTE
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42
43. OBSERVATION METHODS
Not Biased
Data is not affected by
past behavior
Natural behavior of the
group can be recorded
Expensive
Limited Information
Unforeseen factors
may interfere with the
observational task
Merits Demerits
BirinderSingh,AssistantProfessor,PCTE
Ludhiana
43
44. INTERVIEW METHOD
Personal Interview: It is a face to face two way
communication between the interviewer and the
respondents. Generally the personal interview is
carried out in a planned manner and is referred to
as ‘structured interview’. This can be done in many
forms e.g. door to door or as a planned formal
executive meeting.
Telephonic Interview: the information is collected
from the respondent by asking him questions on the
phone is called as telephone interview.
Video Conferencing: The combination of video
camera and computer is used for conducting this
interview.
BirinderSingh,AssistantProfessor,PCTE
Ludhiana
44
45. INTERVIEW METHOD
Accuracy of data
Reliability of data
Flexibility of questions
Originality of data
Biased
Costly
Not proper for wide
areas
Wrong Conclusions
Merits Demerits
BirinderSingh,AssistantProfessor,PCTE
Ludhiana
45
46. QUESTIONNAIRE
In this method, a list of questions relating to the
survey is prepared.
It can be sent to the interviewee in the following
ways:
o Through post
o Through E Mail
o Through personal presence
o Online Surveys
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Ludhiana
46
47. QUESTIONNAIRE
Economical
Originality of data
Wider Area
Lack of Interest
Lack of flexibility
Limited use
Biased
Less Accuracy
Merits Demerits
BirinderSingh,AssistantProfessor,PCTE
Ludhiana
47
48. QUALITIES OF A GOOD QUESTIONNAIRE
Limited number of questions
Simplicity
Proper Order of the Questions
No Undesirable Questions
Avoid Calculations
Pre testing
Clear Instructions
Cross Verifications of Questions
Request for return
BirinderSingh,AssistantProfessor,PCTE
Ludhiana
48
49. SECONDARY DATA
Government / Semi
Government Publications
Reports of Committees &
Commissions
Publications of Trade
Associations
Publications of Research
Associations
Journals & Papers
Publications of Research
Scholars
International Publications
These data are collected
by the government &
private organizations and
is not published. These
are used as secondary
data too.
Published Sources Unpublished Sources
BirinderSingh,AssistantProfessor,PCTE
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49
51. CLASSIFICATION OF DATA
It is the process of arranging the data into different
classes or groups according to their common
characteristics.
According to Spurr & Smith, “Classification is the
grouping of related facts into classes”
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52. OBJECTIVES OF CLASSIFICATION
To make comparisons
To arrange the data in such a way that their
similarities and dissimilarities become very clear
To point out the most important features of the data
at a glance
To present the data in a brief form
To enable statistical treatment of the collected data
To make data attractive and effective
BirinderSingh,AssistantProfessor,PCTE
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52
54. GEOGRAPHICAL CLASSIFICATION
Number of Colleges in 2016
Punjab 70
Haryana 30
Jammu & Kashmir 20
Himachal Pradesh 25
BirinderSingh,AssistantProfessor,PCTE
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54
55. CHRONOLOGICAL CLASSIFICATION
Population of India (in Cr.)
Year 1971 54.8
Year 1981 68.4
Year 1991 84.4
Year 2001 102.8
Year 2011 121.0
BirinderSingh,AssistantProfessor,PCTE
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55
56. QUALITATIVE CLASSIFICATION
In this, data are classified on the basis of some
attribute or quality such as sex, literacy, religion etc.
It is of two types:
Simple Classification: When only one attribute is
studied i.e. Classification of population on the
basis of Male & Female
Manifold Classification: When more than one
attribute is studied i.e. Classification of population
on the basis of Male & Female along with Rural &
Urban.
BirinderSingh,AssistantProfessor,PCTE
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56
57. QUANTITATIVE CLASSIFICATION
When data are classified on the basis of some
characteristics which is capable of direct
quantitative measurement such as height, weight,
income, marks etc.
It is also called numerical or grouped classification
Weight (in kgs.) No. of persons
70-80 50
80-90 30
90-100 15
100-110 5
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57
58. QUANTITATIVE CLASSIFICATION
Variable
• Characteristic capable
of direct quantitative
measurement
• Eg. Height, Weight,
Marks, Production,
Consumption etc.
Frequency
• It is the quantity linked
with the variable
• Eg. No. of persons in
weight range 70-80
kgs is 50
Weight
(in kgs.)
No. of
persons
70-80 50
80-90 30
90-100 15
100-110 5
BirinderSingh,AssistantProfessor,PCTE
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59. FREQUENCY DISTRIBUTION
Marks No. of students
10 5
12 6
15 10
18 3
20 1
Discrete Frequency
Distribution
Grouped Frequency
Distribution
Marks: Out of 20
Total Students: 25
Marks No. of students
0-20 1
20-40 6
40-60 10
60-80 5
80-100 3
Marks: Out of 100
Total Students: 25
BirinderSingh,AssistantProfessor,PCTE
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59
60. TERMINOLOGY ASSOCIATED WITH GROUPED
FREQUENCY DISTRIBUTION
Class Interval/Class: Its
group of numbers in which
items are placed. Eg. 0-20,
20-40, 40-60 etc.
Class Frequency (f): The
number of observation
falling within a class. Eg.
“10” against 40-60
Class Limits: Each class is
located between two limits.
The lower value of a class is
called lower limit & higher
value is called upper limit.
Eg. In 10-20: 10 is LL (l1) &
20 is UL.(l2)
Marks No. of students
0-20 1
20-40 6
40-60 10
60-80 5
80-100 3
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61. TERMINOLOGY ASSOCIATED WITH GROUPED
FREQUENCY DISTRIBUTION
Class Mark/ Mid
Value: It is the average
value of l1 & l2. MV = (l1
+ l2)/2
Class Size: The width
or class size or
magnitude of a class is
the difference between
its lower and upper
class limits. It is
denoted by i= l2 – l1
Marks No. of students
0-20 1
20-40 6
40-60 10
60-80 5
80-100 3
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61
62. TYPES OF SERIES IN GROUPED FREQUENCY
DISTRIBUTION
Exclusive
Series
Inclusive
Series
Open End
Series
Mid Value
Series
Cumulative
Frequency
Series
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63. EXCLUSIVE SERIES
Marks No. of students
0-20 1
20-40 6
40-60 10
60-80 5
80-100 3
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64. INCLUSIVE SERIES
Marks Frequency
10-14 4
15-19 5
20-24 8
25-29 5
30-34 4
Total 26
Marks Frequency
9.5-14.5 4
14.5-19.5 5
19.5-24.5 8
24.5-29.5 5
29.5-34.5 4
Total 26
Inclusive Series To Exclusive Series
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64
65. OPEN ENDED SERIES
Marks Frequency
Less than 5 4
5-10 5
10-15 8
15-20 5
20 and above 4
Total 26
Marks Frequency
0-5 4
5-10 5
10-15 8
15-20 5
20-25 4
Total 26
Open Ended Series To Exclusive Series
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65
66. FREQUENCY SERIES CONTAINING
MID VALUE
Mid Value Frequency
5 4
15 5
25 8
35 5
45 4
Total 26
Marks Frequency
0-10 4
10-20 5
20-30 8
30-40 5
40-50 4
Total 26
Mid Value Series To Exclusive Series
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66
67. CUMULATIVE FREQUENCY SERIES
Marks Frequency
Less than 10 4
Less than 20 20
Less than 30 40
Less than 40 48
Less than 50 50
Marks Frequency
0-10 4
10-20 20 – 4 = 16
20-30 40 – 20 = 20
30-40 48 – 40 = 8
40-50 50 – 48 = 2
Total 50
Less Than Type Series To Exclusive Series
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67
68. CUMULATIVE FREQUENCY SERIES
Marks Frequency
More than 0 50
More than 20 48
More than 40 40
More than 60 20
More than 80 6
Marks Frequency
0-20 50 – 48 = 2
20-40 48 – 40 = 8
40-60 40 – 20 = 20
60-80 20 – 6 = 14
80-100 6
Total 50
More Than Type Series To Exclusive Series
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70. INTRODUCTION
Whenever verbal problems involving a certain
situation is presented visually before the learners, it
makes easier for the learner to understand the
problem and attempt its solution.
Similarly, when the data are presented pictorially (or
graphically) before the learners, it makes the
presentation eye-catching and more intelligible.
The learners can easily see the salient features of
the data and interpret them.
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71. INVENTOR OF GRAPHS
William Playfair (1759-1823), Scottish engineer and
political economist, is the principal inventor of
statistical graphs.
In 1786, he published “Commercial and Political
Atlas” that contained 44 charts.
He invented three of the four basic forms of graph:
o The statistical line graph
o The bar graph
o The pie graph
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71
72. TYPES OF GRAPHS
Line graphs
-Polygraph
Bar graphs
Pie graphs
Flow Charts
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73. LINE GRAPH
The line graphs are usually drawn to represent the
time series data
Example: temperature, rainfall, population growth,
birth rates and the death rates.
1000
1500
1750
1250
1000
2000
0
500
1000
1500
2000
2500
2011 2012 2013 2014 2015 2016
P
r
i
c
e
Year
Share Price of Co. ABC Ltd.
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76. 1.2. POLYGRAPH
• Polygraph is a line graph in which two or more than
two variables are shown on a same diagram by
different lines. It helps in comparing the data.
Examples which can be shown as polygraph are:
– The growth rate of different crops like rice, wheat,
pulses in one diagram.
– The birth rates, death rates and life expectancy in one
diagram.
– Sex ratio in different states or countries in one diagram.
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79. BAR GRAPHS
It is also called a columnar diagram. The bar
diagrams are drawn through columns of equal width.
Following rules were observed while constructing a
bar diagram:
(a) The width of all the bars or columns is similar.
(b) All the bars should be placed on equal
intervals/distance.
(c) Bars are shaded with colors or patterns to make
them distinct and attractive.
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80. TYPES OF BAR GRAPHS
Three types of Bar Graphs are used to represent
different data sets:
o The simple bar diagram
o Compound bar diagram
o Polybar diagram
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81. THE SIMPLE BAR DIAGRAM
A simple bar diagram is constructed for an
immediate comparison. It is advisable to arrange
the given data set in an ascending or descending
order and plot the data variables accordingly.
However, time series data are represented
according to the sequencing of the time period.
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83. THE SIMPLE BAR DIAGRAM
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84. COMPOUND BAR DIAGRAM
When different components are grouped in one set
of variable or different variables of one component
are put together, their representation is made by a
compound bar diagram. In this method, different
variables are shown in a single bar with different
rectangles.
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84
85. COMPOUND BAR GRAPH
0
50
100
150
200
250
300
2013 2014 2015 2016
80 75 85 100
85 81
88
90
90
82
95
100
Scoresachievedineachsubject
Year
Subject A Subject B Subject C
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87. POLYBAR DIAGRAM
The line and bar graphs as drawn separately may
also be combined to depict the data related to some
of the closely associated characteristics such as the
climatic data of mean monthly temperatures and
rainfall.
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89. PIE GRAPHS
Pie diagram is another graphical method of the
representation of data. It is drawn to depict the total
value of the given attribute using a circle. Dividing
the circle into corresponding degrees of angle then
represent the sub– sets of the data. Hence, it is
also called as Divided Circle Diagram.
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90. PIE CHART
29%
23%
22%
26%
Sales in Cr. of ABC Ltd. in 2015-16
1st Qtr 2nd Qtr 3rd Qtr 4th Qtr
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92. FLOW MAPS/CHART
Flow chart is a combination of graph and map. It is
drawn to show the flow of commodities or people
between the places of origin and destination. It is
also called as Dynamic Map.
Transport map, which shows number of
passengers, vehicles, etc., is the best example of a
flow chart.
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93. CONCLUSION
If the information is presented in tabular form or in a
descriptive record, it becomes difficult to draw
results.
Graphical form makes it possible to easily draw
visual impressions of data.
The graphic method of the representation of data
enhances our understanding.
It makes the comparisons easy.
Besides, such methods create an imprint on mind
for a longer time
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Ludhiana
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