2. Organisation of Data
Organisation of data is the second
statistical tool under which data are
arranged in such a form that comparison
of masses of similar data may be
facilitated a further analysis may be
possible. The most popular way of
organization of data is classification of
data.
3. Meaning of Classification of Data
Classification is the of arranging data in various
groups or classes according to their characteristics.
There are two features of classification of data
1- Data are classified in various groups are classes.
2- The basis of classification of data is their
characteristics (resemblances and affinities).
4. Objectives of Classification:
a] To simplify complex data
b] To facilitate understanding
c] To facilitate comparison
d] To make analysis and interpretation easy.
e] To arrange and put the data according to their common characteristics.
5. Statistical Series Systematic
arrangement of statistical data
Raw data: Data collected in original or
crude form.
Series: Arranging of raw data in
different classes according to a given
order or sequence is called series.
6. Conversion of Raw Data into Series
1. Individual Series without frequency
2. Frequency series or Series with
frequencies.
1] Individual Series: The arrangement of
raw data individually without
frequency
7. TYPES OF CLASSIFICATION OF DATA
⚫Geographical classification of data
⚫Chronological classification
⚫Qualitative classification
⚫Quantitative classification
8. GEOGRAPHICAL CLASSIFICATION
When the data classified according to
geographical location or region (like,
states , cities , regions, Zones , areas ,
etc ) It is called geographical
classification . For example , the
production of food grains in INDIA
may be presented state- wise in
following manner.
10. STATE- WISE ETIMATES OF PRODUCTION OF FOOD GRAINS
S.NO. NAME OF STUDENTS TOTAL FOOD GRAINS
(Thousands Tones)
1 ANDHRA PREDESH 1093.93
2 BIHAR 12899.89
3 KARNATAKA 1834.78
4 PUNJAB 21788.20
5 UTTAR PRESDESH 41828.30
11. CHRONOLOGICAL CLASSIFICATION
When data are observed over a period of time the type of
classification is known as chronological classification ( on
the basis of its time of occurrence ) .
National income figures , annual output of wheat
monthly expenditure of a household daily consumptions
milk , etc . Are some examples of chronological
classification . For examples we may present the figures
of population (or production ,sales etc. ) as follows..... .
12. POPULATION OF INDIA 1941 TO 1991
S.NO. YEAR POPULATION IN
CRORES
1 1941 31.87
2 1951 36.11
3 1961 43.91
4 1971 54.82
5 1981 68.33
14. QUALITATIVE CLASSIFICATION
We may first divide the population to
male and female on the basis of the
attribute “ sex” each of this class may
be further subdivide into “literate and
‘illiterate’ on the basis of attribute
‘literacy’ further classification can be
made on the basis of same other
attribute , say , employment .
15. QUANTITATIVE CLASSIFICATION
When data are classified on the basis of quantity is known
as quantitative classification of data.
WEIGHT (Kg) NO. OF STUDENTS
40-50 60
50-60 50
60-70 28
70-80 20
80-90 12
90-100 170
16. Characteristics of a Good
Classification
1.Comprehensiveness
2.Clarity
3.Homogeneity
4.Suitability
5.Stability
6.Elastic
18. Concept of Variable
A characteristic or a phenomenon which is capable of being
measured and changes its value overtime is called variable.
A) Discrete Variable
Discrete variables are those variables that increase in jumps or
in complete numbers. (No fraction is possible)
Eg. Number of students in a class, Number of cars in a show
room etc. (1,2, 10,or 15 etc.)
B) Continuous Variables
Variables that assume a range of values or increase not in
jumps but continuously or in fractions are called continuous
variables.
Eg. Height of the boys –5’1’’ , 5’3’’ and so on, Marks in any range
0-10, 10-15, 15-20
19. Meaning of Discrete Variable
Which are measured in complete numbers like
numbers of students, teachers, office staff etc.
EMPLOYEE/STUDENTS NO
STUDENTS 500
TEACHERS 32
OFFICE STAFF 6
D-GROUP EMPLOYEE 4
20. Meaning of Continuous Variable
Which are not measured in complete numbers always
like height in meter, weight in Kg etc.
Height in Cm No of Students
110-120 10
120-130 12
130-140 11
140-150 8
22. Individual Series can be expressed in
two ways.
a] According to Serial Numbers
b] Ascending or descending order.
In ascending order, smallest number is placed first
In descending order, the highest number is placed first.
Roll no. Marks
1 30
2 25
3 15
4 30
5 25
25. Discrete Series or Frequency
Array-
Discrete series in which data are presented in a way that
exact measurement of items are clearly shown.
Value Frequency
10 4
11 6
12 6
13 4
14 3
26. Continuous Series-
It is that series in which item cannot be exactly
measured; they are placed in a class.
Class
Interval
Frequency
0-10 10
10-20 15
20-30 20
30-40 18
40-50 15
50-60 9
27. Types of Continuous Series-
1- Exclusive Series
2- Inclusive Series
3-Open End Series
4- Mid Value Frequency Series
5- Cumulative frequency series
28. In constructing continuous series we come across terms like:
a] Class : Each given internal is called a class e.g., 0-5, 5-10.
b] Class limit: There are two limits upper limit and lower
limit.
c] Class interval: Difference between upper limit and lower
limit.
d] Range: Difference between upper limit and lower limit.
e] Mid-point or Mid Value: Upper limit -Lower limit
2
f] Frequency: Number of items [observations] falling within
a particular class.
29. 1 Exclusive Series
It such types of statistical series in which upper
limit of a class are the lower limit of just next class
ObtainedMarks No of Students
00-10 8
10-20 9
20-30 10
30-40 9
40-50 8
30. 2 Inclusive Series
It is such types of statistical series in which all
frequencies of class are included in the same class.
C.I. Frequency
1-10 8
11-20 9
21-30 10
31-40 9
41-50 8
31. Conversion of Inclusive into Exclusive
Series
C.I Frequency
1-10 8
11-20 9
21-30 10
31-40 9
41-50 8
C.I. Frequency
0.5-10.5 8
10.5-20.5 9
20.5-30.5 10
30.5-40.5 9
40.5-50.5 8
32. Open End Series
The lowest value of highest value of the
distribution are not defined.
Obtained Marks No of Students
Below 10 8
10-20 9
20-30 10
30-40 9
Above 40 8
33. Mid Value Frequency Series
The class interval are not given only mid values and
their respective frequencies are given.
Mid Value Frequency
5 8
15 9
25 10
35 9
45 8
35. iii] Cumulative Frequency Series:
It is obtained by successively adding the frequencies of
the values of the classes according to a certain law.
a] ‘Less than’ Cumulative Frequency Distribution :
The frequencies of each class-internal are added
successively.
b] ‘More than’ Cumulative Frequency Distribution:
The more than cumulative frequency is obtained by
finding the cumulative totals of frequencies starting
from the highest value of the variable to the lowest
value.
36. Less Than Cumulative Frequency
Series
Less Than No of
Items
10 8
20 8+9=17
30 17+10=27
40 27+9=36
50 36+8=44
C.I Frequency
00-10 8
10-20 9
20-30 10
30-40 9
40-50 8
37. More Than Cumulative Frequency Series
More
Than
No of
Items
0 44
10 44-8=36
20 36-9=27
30 27-10=17
40 17-9=8
50 8-8=0
C.I Frequency
00-10 8
10-20 9
20-30 10
30-40 9
40-50 8
38. LOSS OF INFORMATION
•The frequency distribution summarizes the raw data by
making it concise and comprehensible. However, it does not
show the details that are found in your data and leads to loss
of information.
•When the raw data is grouped into classes, an individual
observation has no significance in further statistical
calculations.
•For example, suppose class 10-20 contains 6 values: 12,
15,16,18,14,19. When such data is grouped as a class 10-20,
study material and individual values have no significance
and only frequency, i.e.6 is recorded and not their actual
values.
•Statistical calculations are based only on the values of the
class mark instead of the actual values. As a result, it leads to
considerable loss of information.
39. FREQUENCY ARRAY
•A discrete variable, the classification of its data is known as
a frequency array.
Univariate frequency distribution –
When data is classified on the basis of a single variable are
known as univariate frequency distributions. And one way
frequency distribution.
Bivariate frequency distribution –
When data is classified on the basis of two variables such as
height and weight, marks in statics and economics etc., the
distribution is known as bivariate frequency distribution or
two way frequency distribution.
