NCERT Textbook - Organisation of Data | Economics Class 11 - Commerce PDF Download

 Page 1


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 between
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
distributions.
CHAPTER
2024-25
Page 2


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 between
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
distributions.
CHAPTER
2024-25
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 according to the markets for
reused goods. 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
things into groups or classes based
on some criteria.
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
students of your school. If you present
2024-25
Page 3


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 between
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
distributions.
CHAPTER
2024-25
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 according to the markets for
reused goods. 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
things into groups or classes based
on some criteria.
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
students of your school. If you present
2024-25
24 STATISTICS FOR ECONOMICS
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 66 100 49 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
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
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 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 classification.
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 have
numbers are not arranged in any order.
Now if you are asked for the highest
marks in mathematics from Table 3.1
2024-25
Page 4


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 between
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
distributions.
CHAPTER
2024-25
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 according to the markets for
reused goods. 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
things into groups or classes based
on some criteria.
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
students of your school. If you present
2024-25
24 STATISTICS FOR ECONOMICS
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 66 100 49 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
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
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 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 classification.
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 have
numbers are not arranged in any order.
Now if you are asked for the highest
marks in mathematics from Table 3.1
2024-25
ORGANISATION OF DATA 25
studied in Chapter 2 that the
Government of India conducts Census
of population every ten years. About
20 crore persons were contacted in
Census 2001. 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 same data is
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. The raw data
as given in Tables 3.1 and 3.2 consist
of observations on a specific or group
of variables. Look at Table 3.1 for
instance which contains marks in
mathematics scored by 100 students.
How can we make sense of these
marks? The mathematics teacher
looking at these marks would be
thinking– How have my students done?
How many have not passed? How we
classify the data depends upon the
purpose we have in mind. In this case,
the teacher wishes to understand in
some depth– how these students have
done. She would probably choose to
construct the frequency distribution.
This is discussed in the next section.
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
is 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 is classified in
various ways depending on the
purpose. 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
2011 121.0
In Spatial Classification the data
are classified with reference to
geographical locations such as
countries, states, cities, districts, etc.
2024-25
Page 5


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 between
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
distributions.
CHAPTER
2024-25
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 according to the markets for
reused goods. 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
things into groups or classes based
on some criteria.
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
students of your school. If you present
2024-25
24 STATISTICS FOR ECONOMICS
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 66 100 49 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
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
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 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 classification.
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 have
numbers are not arranged in any order.
Now if you are asked for the highest
marks in mathematics from Table 3.1
2024-25
ORGANISATION OF DATA 25
studied in Chapter 2 that the
Government of India conducts Census
of population every ten years. About
20 crore persons were contacted in
Census 2001. 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 same data is
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. The raw data
as given in Tables 3.1 and 3.2 consist
of observations on a specific or group
of variables. Look at Table 3.1 for
instance which contains marks in
mathematics scored by 100 students.
How can we make sense of these
marks? The mathematics teacher
looking at these marks would be
thinking– How have my students done?
How many have not passed? How we
classify the data depends upon the
purpose we have in mind. In this case,
the teacher wishes to understand in
some depth– how these students have
done. She would probably choose to
construct the frequency distribution.
This is discussed in the next section.
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
is 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 is classified in
various ways depending on the
purpose. 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
2011 121.0
In Spatial Classification the data
are classified with reference to
geographical locations such as
countries, states, cities, districts, etc.
2024-25
26 STATISTICS FOR ECONOMICS
Example 2 shows the yeild of wheat in
different countries.
Example 2
Yield of Wheat for Different Countries
(2013)
Country Yield of wheat (kg/hectare)
Canada 3594
China 5055
France 7254
Germany 7998
India 3154
Pakistan 2787
Source: Indian Agricultural Statistics at a Glance, 2015
Activities
• In Example  1, find out the years
in which India’s population was
minimum and 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 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.
Characteristics,  like height, weight,
age, income, marks of students, etc.,
are quantitative in nature. When the
collected data of such characteristics
are grouped into classes, it becomes a
Quantitative Classification.
Activity
• The objects around can be grouped
as either living or non-living. Is it
a quantitative classification?
2024-25