NCERT Textbook - Organisation of Data Commerce Notes | EduRev

Statistics for Economics - Class XI

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Commerce : NCERT Textbook - Organisation of Data Commerce Notes | EduRev

 Page 1


between census and sampling. In this
chapter, you will know how the data,
that you collected, are to be classified.
The purpose of classifying raw data is
to bring order in them so that they
can be subjected to further statistical
analysis easily.
Have you ever observed your local
junk dealer or kabadiwallah to whom
you sell old newspapers, broken
household items, empty glass bottles,
plastics etc. He purchases these
things from you and sells them to
those who recycle them. But with so
much junk in his shop it would be very
difficult for him to manage his trade,
if he had not organised them properly.
To ease his situation he suitably
groups or “classifies”  various junk.
He puts old newspapers together and
Organisation of Data
1. INTRODUCTION
In the previous chapter you have
learnt about how data is collected. You
also came to know the difference
Studying this chapter should enable
you to:
• classify the data for further
statistical analysis;
• distinguish between quantitative
and qualitative classification;
• prepare a frequency distribution
table;
• know the technique of forming
classes;
• be familiar with the method of tally
marking;
• differentiate between univariate
and bivariate frequency distribu-
tions.
CHAPTER
Page 2


between census and sampling. In this
chapter, you will know how the data,
that you collected, are to be classified.
The purpose of classifying raw data is
to bring order in them so that they
can be subjected to further statistical
analysis easily.
Have you ever observed your local
junk dealer or kabadiwallah to whom
you sell old newspapers, broken
household items, empty glass bottles,
plastics etc. He purchases these
things from you and sells them to
those who recycle them. But with so
much junk in his shop it would be very
difficult for him to manage his trade,
if he had not organised them properly.
To ease his situation he suitably
groups or “classifies”  various junk.
He puts old newspapers together and
Organisation of Data
1. INTRODUCTION
In the previous chapter you have
learnt about how data is collected. You
also came to know the difference
Studying this chapter should enable
you to:
• classify the data for further
statistical analysis;
• distinguish between quantitative
and qualitative classification;
• prepare a frequency distribution
table;
• know the technique of forming
classes;
• be familiar with the method of tally
marking;
• differentiate between univariate
and bivariate frequency distribu-
tions.
CHAPTER
ORGANISATION OF DATA 23
ties them with a rope. Then collects
all empty glass bottles in a sack.  He
heaps the articles of metals in one
corner of his shop and sorts them into
groups like “iron”, “copper”,
“aluminium”, “brass” etc., and so on.
In this way he groups his junk into
different classes — “newspapers,
“plastics”, “glass”, “metals” etc. — and
brings order in them. Once his junk
is arranged and classified, it becomes
easier for him to find a particular item
that a buyer may demand.
Likewise when you arrange your
schoolbooks in a certain order, it
becomes easier for you to handle
them. You may classify them
according to subjects where each
subject becomes a group or a class.
So, when you need a particular book
on history, for instance, all you need
to do is to search that book in the
group “History”.  Otherwise, you
would have to search through your
entire collection to find the particular
book you are looking for.
While classification of objects or
things saves our valuable time and
effort, it is not done in an arbitrary
manner. The kabadiwallah groups his
junk in such a way that each group
consists of similar items. For example,
under the group “Glass” he would put
empty bottles, broken mirrors and
windowpanes etc. Similarly when you
classify your history books under the
group “History” you would not put a
book of a different subject in that
group. Otherwise the entire purpose
of grouping would be lost.
Classification, therefore, is arranging
or organising similar things into groups
or classes.
Activity
• Visit your local post-office to find
out how letters are sorted. Do
you know what the pin-code in a
letter indicates? Ask your
postman.
2. RAW DATA
Like the kabadiwallah’s junk, the
unclassified data or raw data are
highly disorganised. They are often
very large and cumbersome to handle.
To draw meaningful conclusions from
them is a tedious task because they
do not yield to statistical methods
easily. Therefore proper organisation
and presentation of such data is
needed before any systematic
statistical analysis is undertaken.
Hence after collecting data the next
step is to organise and present them
in a classified form.
Suppose you want to know the
performance of students in
mathematics and you have collected
data on marks in mathematics of 100
Page 3


between census and sampling. In this
chapter, you will know how the data,
that you collected, are to be classified.
The purpose of classifying raw data is
to bring order in them so that they
can be subjected to further statistical
analysis easily.
Have you ever observed your local
junk dealer or kabadiwallah to whom
you sell old newspapers, broken
household items, empty glass bottles,
plastics etc. He purchases these
things from you and sells them to
those who recycle them. But with so
much junk in his shop it would be very
difficult for him to manage his trade,
if he had not organised them properly.
To ease his situation he suitably
groups or “classifies”  various junk.
He puts old newspapers together and
Organisation of Data
1. INTRODUCTION
In the previous chapter you have
learnt about how data is collected. You
also came to know the difference
Studying this chapter should enable
you to:
• classify the data for further
statistical analysis;
• distinguish between quantitative
and qualitative classification;
• prepare a frequency distribution
table;
• know the technique of forming
classes;
• be familiar with the method of tally
marking;
• differentiate between univariate
and bivariate frequency distribu-
tions.
CHAPTER
ORGANISATION OF DATA 23
ties them with a rope. Then collects
all empty glass bottles in a sack.  He
heaps the articles of metals in one
corner of his shop and sorts them into
groups like “iron”, “copper”,
“aluminium”, “brass” etc., and so on.
In this way he groups his junk into
different classes — “newspapers,
“plastics”, “glass”, “metals” etc. — and
brings order in them. Once his junk
is arranged and classified, it becomes
easier for him to find a particular item
that a buyer may demand.
Likewise when you arrange your
schoolbooks in a certain order, it
becomes easier for you to handle
them. You may classify them
according to subjects where each
subject becomes a group or a class.
So, when you need a particular book
on history, for instance, all you need
to do is to search that book in the
group “History”.  Otherwise, you
would have to search through your
entire collection to find the particular
book you are looking for.
While classification of objects or
things saves our valuable time and
effort, it is not done in an arbitrary
manner. The kabadiwallah groups his
junk in such a way that each group
consists of similar items. For example,
under the group “Glass” he would put
empty bottles, broken mirrors and
windowpanes etc. Similarly when you
classify your history books under the
group “History” you would not put a
book of a different subject in that
group. Otherwise the entire purpose
of grouping would be lost.
Classification, therefore, is arranging
or organising similar things into groups
or classes.
Activity
• Visit your local post-office to find
out how letters are sorted. Do
you know what the pin-code in a
letter indicates? Ask your
postman.
2. RAW DATA
Like the kabadiwallah’s junk, the
unclassified data or raw data are
highly disorganised. They are often
very large and cumbersome to handle.
To draw meaningful conclusions from
them is a tedious task because they
do not yield to statistical methods
easily. Therefore proper organisation
and presentation of such data is
needed before any systematic
statistical analysis is undertaken.
Hence after collecting data the next
step is to organise and present them
in a classified form.
Suppose you want to know the
performance of students in
mathematics and you have collected
data on marks in mathematics of 100
24 STATISTICS FOR ECONOMICS
students of your school. If you present
them as a table, they may appear
something like Table 3.1.
TABLE 3.1
Marks in Mathematics Obtained by 100
Students in an Examination
47 45 10 60 51 56 66 10049 40
60 59 56 55 62 48 59 55 51 41
42 69 64 66 50 59 57 65 62 50
64 30 37 75 17 56 20 14 55 90
62 51 55 14 25 34 90 49 56 54
70 47 49 82 40 82 60 85 65 66
49 44 64 69 70 48 12 28 55 65
49 40 25 41 71 80 0 56 14 22
66 53 46 70 43 61 59 12 30 35
45 44 57 76 82 39 32 14 90 25
Or you could have collected data
on the monthly expenditure on food
of 50 households in your
neighbourhood to know their average
expenditure on food. The data
collected, in that case, had you
presented as a table, would have
resembled Table 3.2. Both Tables 3.1
and 3.2 are raw or unclassified data.
In both the tables you find that
numbers are not arranged in any
order. Now if you are asked what are
the highest marks in mathematics
TABLE 3.2
Monthly Household Expenditure (in
Rupees) on Food of 50 Households
1904 1559 3473 1735 2760
2041 1612 1753 1855 4439
5090 1085 1823 2346 1523
1211 1360 1110 2152 1183
1218 1315 1105 2628 2712
4248 1812 1264 1183 1171
1007 1180 1953 1137 2048
2025 1583 1324 2621 3676
1397 1832 1962 2177 2575
1293 1365 1146 3222 1396
from Table 3.1 then you have to first
arrange the marks of 100 students
either in ascending or in descending
order. That is a tedious task. It
becomes more tedious, if instead of
100 you have the marks of a 1,000
students to handle. Similarly in Table
3.2, you would note that it is difficult
for you to ascertain the average
monthly expenditure of 50
households. And this difficulty will go
up manifold if the number was larger
— say, 5,000 households. Like our
kabadiwallah, who would be
distressed to find a particular item
when his junk becomes large and
disarranged, you would face a similar
situation when you try to get any
information from raw data that are
large. In one word, therefore, it is a
tedious task to pull information from
large unclassified data.
The raw data are summarised, and
made comprehensible by classifi-
cation. When facts of similar
characteristics are placed in the same
class, it enables one to locate them
easily, make comparison, and draw
inferences without any difficulty. You
Page 4


between census and sampling. In this
chapter, you will know how the data,
that you collected, are to be classified.
The purpose of classifying raw data is
to bring order in them so that they
can be subjected to further statistical
analysis easily.
Have you ever observed your local
junk dealer or kabadiwallah to whom
you sell old newspapers, broken
household items, empty glass bottles,
plastics etc. He purchases these
things from you and sells them to
those who recycle them. But with so
much junk in his shop it would be very
difficult for him to manage his trade,
if he had not organised them properly.
To ease his situation he suitably
groups or “classifies”  various junk.
He puts old newspapers together and
Organisation of Data
1. INTRODUCTION
In the previous chapter you have
learnt about how data is collected. You
also came to know the difference
Studying this chapter should enable
you to:
• classify the data for further
statistical analysis;
• distinguish between quantitative
and qualitative classification;
• prepare a frequency distribution
table;
• know the technique of forming
classes;
• be familiar with the method of tally
marking;
• differentiate between univariate
and bivariate frequency distribu-
tions.
CHAPTER
ORGANISATION OF DATA 23
ties them with a rope. Then collects
all empty glass bottles in a sack.  He
heaps the articles of metals in one
corner of his shop and sorts them into
groups like “iron”, “copper”,
“aluminium”, “brass” etc., and so on.
In this way he groups his junk into
different classes — “newspapers,
“plastics”, “glass”, “metals” etc. — and
brings order in them. Once his junk
is arranged and classified, it becomes
easier for him to find a particular item
that a buyer may demand.
Likewise when you arrange your
schoolbooks in a certain order, it
becomes easier for you to handle
them. You may classify them
according to subjects where each
subject becomes a group or a class.
So, when you need a particular book
on history, for instance, all you need
to do is to search that book in the
group “History”.  Otherwise, you
would have to search through your
entire collection to find the particular
book you are looking for.
While classification of objects or
things saves our valuable time and
effort, it is not done in an arbitrary
manner. The kabadiwallah groups his
junk in such a way that each group
consists of similar items. For example,
under the group “Glass” he would put
empty bottles, broken mirrors and
windowpanes etc. Similarly when you
classify your history books under the
group “History” you would not put a
book of a different subject in that
group. Otherwise the entire purpose
of grouping would be lost.
Classification, therefore, is arranging
or organising similar things into groups
or classes.
Activity
• Visit your local post-office to find
out how letters are sorted. Do
you know what the pin-code in a
letter indicates? Ask your
postman.
2. RAW DATA
Like the kabadiwallah’s junk, the
unclassified data or raw data are
highly disorganised. They are often
very large and cumbersome to handle.
To draw meaningful conclusions from
them is a tedious task because they
do not yield to statistical methods
easily. Therefore proper organisation
and presentation of such data is
needed before any systematic
statistical analysis is undertaken.
Hence after collecting data the next
step is to organise and present them
in a classified form.
Suppose you want to know the
performance of students in
mathematics and you have collected
data on marks in mathematics of 100
24 STATISTICS FOR ECONOMICS
students of your school. If you present
them as a table, they may appear
something like Table 3.1.
TABLE 3.1
Marks in Mathematics Obtained by 100
Students in an Examination
47 45 10 60 51 56 66 10049 40
60 59 56 55 62 48 59 55 51 41
42 69 64 66 50 59 57 65 62 50
64 30 37 75 17 56 20 14 55 90
62 51 55 14 25 34 90 49 56 54
70 47 49 82 40 82 60 85 65 66
49 44 64 69 70 48 12 28 55 65
49 40 25 41 71 80 0 56 14 22
66 53 46 70 43 61 59 12 30 35
45 44 57 76 82 39 32 14 90 25
Or you could have collected data
on the monthly expenditure on food
of 50 households in your
neighbourhood to know their average
expenditure on food. The data
collected, in that case, had you
presented as a table, would have
resembled Table 3.2. Both Tables 3.1
and 3.2 are raw or unclassified data.
In both the tables you find that
numbers are not arranged in any
order. Now if you are asked what are
the highest marks in mathematics
TABLE 3.2
Monthly Household Expenditure (in
Rupees) on Food of 50 Households
1904 1559 3473 1735 2760
2041 1612 1753 1855 4439
5090 1085 1823 2346 1523
1211 1360 1110 2152 1183
1218 1315 1105 2628 2712
4248 1812 1264 1183 1171
1007 1180 1953 1137 2048
2025 1583 1324 2621 3676
1397 1832 1962 2177 2575
1293 1365 1146 3222 1396
from Table 3.1 then you have to first
arrange the marks of 100 students
either in ascending or in descending
order. That is a tedious task. It
becomes more tedious, if instead of
100 you have the marks of a 1,000
students to handle. Similarly in Table
3.2, you would note that it is difficult
for you to ascertain the average
monthly expenditure of 50
households. And this difficulty will go
up manifold if the number was larger
— say, 5,000 households. Like our
kabadiwallah, who would be
distressed to find a particular item
when his junk becomes large and
disarranged, you would face a similar
situation when you try to get any
information from raw data that are
large. In one word, therefore, it is a
tedious task to pull information from
large unclassified data.
The raw data are summarised, and
made comprehensible by classifi-
cation. When facts of similar
characteristics are placed in the same
class, it enables one to locate them
easily, make comparison, and draw
inferences without any difficulty. You
ORGANISATION OF DATA 25
have studied in Chapter 2 that the
Government of India conducts Census
of population every ten years. The raw
data of census are so large and
fragmented that it appears an almost
impossible task to draw any
meaningful conclusion from them.
But when the data of Census are
classified according to gender,
education, marital status, occupation,
etc., the structure and nature of
population of India is, then, easily
understood.
The raw data consist of
observations on variables. Each unit
of raw data is an observation. In Table
3.1 an observation shows a particular
value of the variable “marks of a
student in mathematics”. The raw
data contain 100 observations on
“marks of a student” since there are
100 students. In Table 3.2 it shows a
particular value of the variable
“monthly expenditure of a household
on food”.   The raw data in it contain
50 observations on “monthly
expenditure on food of a household”
because there are 50 households.
Activity
• Collect data of total weekly
expenditure of your family for a
year and arrange it in a table.
See how many observations you
have. Arrange the data monthly
and find the number of
observations.
3. CLASSIFICATION OF DATA
The groups or classes of a
classification can be done in various
ways. Instead of classifying your books
according to subjects — “History”,
“Geography”, “Mathematics”, “Science”
etc. — you could have classified them
author-wise in an alphabetical order.
Or, you could have also classified them
according to the year of publication.
The way you want to classify them
would depend on your requirement.
Likewise the raw data could be
classified in various ways depending
on the purpose in hand. They can be
grouped according to time. Such a
classification is known as a
Chronological Classification. In
such a classification, data are
classified either in ascending or in
descending order with reference to
time such as years, quarters, months,
weeks, etc. The following example
shows the population of India
classified in terms of years. The
variable ‘population’ is a Time Series
as it depicts a series of values for
different years.
Example 1
Population of India (in crores)
Year Population (Crores)
1951 35.7
1961 43.8
1971 54.6
1981 68.4
1991 81.8
2001 102.7
In Spatial Classification the data
are classified with reference to
geographical locations such as
countries, states, cities, districts, etc.
Example 2 shows the yield of wheat in
different countries.
Page 5


between census and sampling. In this
chapter, you will know how the data,
that you collected, are to be classified.
The purpose of classifying raw data is
to bring order in them so that they
can be subjected to further statistical
analysis easily.
Have you ever observed your local
junk dealer or kabadiwallah to whom
you sell old newspapers, broken
household items, empty glass bottles,
plastics etc. He purchases these
things from you and sells them to
those who recycle them. But with so
much junk in his shop it would be very
difficult for him to manage his trade,
if he had not organised them properly.
To ease his situation he suitably
groups or “classifies”  various junk.
He puts old newspapers together and
Organisation of Data
1. INTRODUCTION
In the previous chapter you have
learnt about how data is collected. You
also came to know the difference
Studying this chapter should enable
you to:
• classify the data for further
statistical analysis;
• distinguish between quantitative
and qualitative classification;
• prepare a frequency distribution
table;
• know the technique of forming
classes;
• be familiar with the method of tally
marking;
• differentiate between univariate
and bivariate frequency distribu-
tions.
CHAPTER
ORGANISATION OF DATA 23
ties them with a rope. Then collects
all empty glass bottles in a sack.  He
heaps the articles of metals in one
corner of his shop and sorts them into
groups like “iron”, “copper”,
“aluminium”, “brass” etc., and so on.
In this way he groups his junk into
different classes — “newspapers,
“plastics”, “glass”, “metals” etc. — and
brings order in them. Once his junk
is arranged and classified, it becomes
easier for him to find a particular item
that a buyer may demand.
Likewise when you arrange your
schoolbooks in a certain order, it
becomes easier for you to handle
them. You may classify them
according to subjects where each
subject becomes a group or a class.
So, when you need a particular book
on history, for instance, all you need
to do is to search that book in the
group “History”.  Otherwise, you
would have to search through your
entire collection to find the particular
book you are looking for.
While classification of objects or
things saves our valuable time and
effort, it is not done in an arbitrary
manner. The kabadiwallah groups his
junk in such a way that each group
consists of similar items. For example,
under the group “Glass” he would put
empty bottles, broken mirrors and
windowpanes etc. Similarly when you
classify your history books under the
group “History” you would not put a
book of a different subject in that
group. Otherwise the entire purpose
of grouping would be lost.
Classification, therefore, is arranging
or organising similar things into groups
or classes.
Activity
• Visit your local post-office to find
out how letters are sorted. Do
you know what the pin-code in a
letter indicates? Ask your
postman.
2. RAW DATA
Like the kabadiwallah’s junk, the
unclassified data or raw data are
highly disorganised. They are often
very large and cumbersome to handle.
To draw meaningful conclusions from
them is a tedious task because they
do not yield to statistical methods
easily. Therefore proper organisation
and presentation of such data is
needed before any systematic
statistical analysis is undertaken.
Hence after collecting data the next
step is to organise and present them
in a classified form.
Suppose you want to know the
performance of students in
mathematics and you have collected
data on marks in mathematics of 100
24 STATISTICS FOR ECONOMICS
students of your school. If you present
them as a table, they may appear
something like Table 3.1.
TABLE 3.1
Marks in Mathematics Obtained by 100
Students in an Examination
47 45 10 60 51 56 66 10049 40
60 59 56 55 62 48 59 55 51 41
42 69 64 66 50 59 57 65 62 50
64 30 37 75 17 56 20 14 55 90
62 51 55 14 25 34 90 49 56 54
70 47 49 82 40 82 60 85 65 66
49 44 64 69 70 48 12 28 55 65
49 40 25 41 71 80 0 56 14 22
66 53 46 70 43 61 59 12 30 35
45 44 57 76 82 39 32 14 90 25
Or you could have collected data
on the monthly expenditure on food
of 50 households in your
neighbourhood to know their average
expenditure on food. The data
collected, in that case, had you
presented as a table, would have
resembled Table 3.2. Both Tables 3.1
and 3.2 are raw or unclassified data.
In both the tables you find that
numbers are not arranged in any
order. Now if you are asked what are
the highest marks in mathematics
TABLE 3.2
Monthly Household Expenditure (in
Rupees) on Food of 50 Households
1904 1559 3473 1735 2760
2041 1612 1753 1855 4439
5090 1085 1823 2346 1523
1211 1360 1110 2152 1183
1218 1315 1105 2628 2712
4248 1812 1264 1183 1171
1007 1180 1953 1137 2048
2025 1583 1324 2621 3676
1397 1832 1962 2177 2575
1293 1365 1146 3222 1396
from Table 3.1 then you have to first
arrange the marks of 100 students
either in ascending or in descending
order. That is a tedious task. It
becomes more tedious, if instead of
100 you have the marks of a 1,000
students to handle. Similarly in Table
3.2, you would note that it is difficult
for you to ascertain the average
monthly expenditure of 50
households. And this difficulty will go
up manifold if the number was larger
— say, 5,000 households. Like our
kabadiwallah, who would be
distressed to find a particular item
when his junk becomes large and
disarranged, you would face a similar
situation when you try to get any
information from raw data that are
large. In one word, therefore, it is a
tedious task to pull information from
large unclassified data.
The raw data are summarised, and
made comprehensible by classifi-
cation. When facts of similar
characteristics are placed in the same
class, it enables one to locate them
easily, make comparison, and draw
inferences without any difficulty. You
ORGANISATION OF DATA 25
have studied in Chapter 2 that the
Government of India conducts Census
of population every ten years. The raw
data of census are so large and
fragmented that it appears an almost
impossible task to draw any
meaningful conclusion from them.
But when the data of Census are
classified according to gender,
education, marital status, occupation,
etc., the structure and nature of
population of India is, then, easily
understood.
The raw data consist of
observations on variables. Each unit
of raw data is an observation. In Table
3.1 an observation shows a particular
value of the variable “marks of a
student in mathematics”. The raw
data contain 100 observations on
“marks of a student” since there are
100 students. In Table 3.2 it shows a
particular value of the variable
“monthly expenditure of a household
on food”.   The raw data in it contain
50 observations on “monthly
expenditure on food of a household”
because there are 50 households.
Activity
• Collect data of total weekly
expenditure of your family for a
year and arrange it in a table.
See how many observations you
have. Arrange the data monthly
and find the number of
observations.
3. CLASSIFICATION OF DATA
The groups or classes of a
classification can be done in various
ways. Instead of classifying your books
according to subjects — “History”,
“Geography”, “Mathematics”, “Science”
etc. — you could have classified them
author-wise in an alphabetical order.
Or, you could have also classified them
according to the year of publication.
The way you want to classify them
would depend on your requirement.
Likewise the raw data could be
classified in various ways depending
on the purpose in hand. They can be
grouped according to time. Such a
classification is known as a
Chronological Classification. In
such a classification, data are
classified either in ascending or in
descending order with reference to
time such as years, quarters, months,
weeks, etc. The following example
shows the population of India
classified in terms of years. The
variable ‘population’ is a Time Series
as it depicts a series of values for
different years.
Example 1
Population of India (in crores)
Year Population (Crores)
1951 35.7
1961 43.8
1971 54.6
1981 68.4
1991 81.8
2001 102.7
In Spatial Classification the data
are classified with reference to
geographical locations such as
countries, states, cities, districts, etc.
Example 2 shows the yield of wheat in
different countries.
26 STATISTICS FOR ECONOMICS
Example 2
Yield of Wheat for Different Countries
Country Yield of wheat (kg/acre)
America 1925
Brazil 127
China 893
Denmark 225
France 439
India 862
Activities
• In the time-series of Example  1,
in which year do you find the
population of India to be the
minimum. Find the year when it
is the maximum.
• In Example 2, find the country
whose yield of wheat is slightly
more than that of India’s. How
much would that be in terms of
percentage?
• Arrange the countries of
Example 2 in the ascending
order of yield. Do the same
exercise for the descending order
of yield.
Sometimes you come across
characteristics that cannot be
expressed quantitatively. Such
characteristics are called Qualities or
Attributes. For example, nationality,
literacy, religion, gender, marital
status, etc. They cannot be measured.
Yet these attributes can be classified
on the basis of either the presence or
the absence of a qualitative
characteristic. Such a classification of
data on attributes is called a
Qualitative Classification. In the
following example, we find population
of a country is grouped on the basis
of the qualitative variable “gender”. An
observation could either be a male or
a female. These two characteristics
could be further classified on the basis
of marital status (a qualitative
variable) as given below:
Example 3
Population
Male     Female
  Married   Unmarried  Married   Unmarried
The classification at the first stage
is based on the presence and absence
of an attribute i.e. male or not male
(female). At the second stage, each
class — male and female, is further sub
divided on the basis of the presence or
absence of another attribute i.e.
whether married or unmarried. On the
Activity
• The objects around can be
grouped as either living or non-
living. Is it a quantitative
classification?
other hand, characteristics like height,
weight, age, income, marks of
students, etc. are quantitative in
nature. When the collected data of
such characteristics are grouped into
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