PPT - Classification and Tabulation, B Com Notes | EduRev

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B Com : PPT - Classification and Tabulation, B Com Notes | EduRev

 Page 1


CLASSIFICATION 
CLASSIFICATION 
AND 
AND 
TABULATION OF DATA
TABULATION OF DATA
Dr.
Dr.  
Bijaya Bhusan Nanda, 
Bijaya Bhusan Nanda, 
M. Sc (Gold Medalist) Ph. D. (Stat.)
M. Sc (Gold Medalist) Ph. D. (Stat.)
Topper Orissa Statistics & Economics 
Topper Orissa Statistics & Economics 
Services, 1988
Services, 1988
bijayabnanda@yahoo.com
bijayabnanda@yahoo.com
Lecture Series on 
Lecture Series on 
Biostatistics
Biostatistics
No. Bio-Stat_3 No. Bio-Stat_3
Date – 03.08.2008 Date – 03.08.2008
Page 2


CLASSIFICATION 
CLASSIFICATION 
AND 
AND 
TABULATION OF DATA
TABULATION OF DATA
Dr.
Dr.  
Bijaya Bhusan Nanda, 
Bijaya Bhusan Nanda, 
M. Sc (Gold Medalist) Ph. D. (Stat.)
M. Sc (Gold Medalist) Ph. D. (Stat.)
Topper Orissa Statistics & Economics 
Topper Orissa Statistics & Economics 
Services, 1988
Services, 1988
bijayabnanda@yahoo.com
bijayabnanda@yahoo.com
Lecture Series on 
Lecture Series on 
Biostatistics
Biostatistics
No. Bio-Stat_3 No. Bio-Stat_3
Date – 03.08.2008 Date – 03.08.2008
LEARNING OBJECTIVES
LEARNING OBJECTIVES
?
The trainees will be able to do 
The trainees will be able to do 
meaningful classification of large 
meaningful classification of large 
mass of data and interpret the same.
mass of data and interpret the same.
?
They will be able to construct 
They will be able to construct 
frequency distribution table and 
frequency distribution table and 
interpret the same.
interpret the same.
?
They will be able to describe different 
They will be able to describe different 
parts of tables and types of table   
parts of tables and types of table   
Page 3


CLASSIFICATION 
CLASSIFICATION 
AND 
AND 
TABULATION OF DATA
TABULATION OF DATA
Dr.
Dr.  
Bijaya Bhusan Nanda, 
Bijaya Bhusan Nanda, 
M. Sc (Gold Medalist) Ph. D. (Stat.)
M. Sc (Gold Medalist) Ph. D. (Stat.)
Topper Orissa Statistics & Economics 
Topper Orissa Statistics & Economics 
Services, 1988
Services, 1988
bijayabnanda@yahoo.com
bijayabnanda@yahoo.com
Lecture Series on 
Lecture Series on 
Biostatistics
Biostatistics
No. Bio-Stat_3 No. Bio-Stat_3
Date – 03.08.2008 Date – 03.08.2008
LEARNING OBJECTIVES
LEARNING OBJECTIVES
?
The trainees will be able to do 
The trainees will be able to do 
meaningful classification of large 
meaningful classification of large 
mass of data and interpret the same.
mass of data and interpret the same.
?
They will be able to construct 
They will be able to construct 
frequency distribution table and 
frequency distribution table and 
interpret the same.
interpret the same.
?
They will be able to describe different 
They will be able to describe different 
parts of tables and types of table   
parts of tables and types of table   
?
Concept of Variable
Concept of Variable
?
Ordered array
Ordered array
?
What is data Classification ?
What is data Classification ?
?
Objectives of Classification
Objectives of Classification
?
Frequency distributions
Frequency distributions
?
Variables and attributes
Variables and attributes
 
 
?
Tabulation of data
Tabulation of data
?
Parts of a table 
Parts of a table 
?
Type of tables 
Type of tables 
C O N T E N T
C O N T E N T
Page 4


CLASSIFICATION 
CLASSIFICATION 
AND 
AND 
TABULATION OF DATA
TABULATION OF DATA
Dr.
Dr.  
Bijaya Bhusan Nanda, 
Bijaya Bhusan Nanda, 
M. Sc (Gold Medalist) Ph. D. (Stat.)
M. Sc (Gold Medalist) Ph. D. (Stat.)
Topper Orissa Statistics & Economics 
Topper Orissa Statistics & Economics 
Services, 1988
Services, 1988
bijayabnanda@yahoo.com
bijayabnanda@yahoo.com
Lecture Series on 
Lecture Series on 
Biostatistics
Biostatistics
No. Bio-Stat_3 No. Bio-Stat_3
Date – 03.08.2008 Date – 03.08.2008
LEARNING OBJECTIVES
LEARNING OBJECTIVES
?
The trainees will be able to do 
The trainees will be able to do 
meaningful classification of large 
meaningful classification of large 
mass of data and interpret the same.
mass of data and interpret the same.
?
They will be able to construct 
They will be able to construct 
frequency distribution table and 
frequency distribution table and 
interpret the same.
interpret the same.
?
They will be able to describe different 
They will be able to describe different 
parts of tables and types of table   
parts of tables and types of table   
?
Concept of Variable
Concept of Variable
?
Ordered array
Ordered array
?
What is data Classification ?
What is data Classification ?
?
Objectives of Classification
Objectives of Classification
?
Frequency distributions
Frequency distributions
?
Variables and attributes
Variables and attributes
 
 
?
Tabulation of data
Tabulation of data
?
Parts of a table 
Parts of a table 
?
Type of tables 
Type of tables 
C O N T E N T
C O N T E N T
Concept of Variable
Concept of Variable
?
Variable
Variable
A characteristic which takes on different 
A characteristic which takes on different 
values in different persons, place or 
values in different persons, place or 
things.
things.
Example
Example
: Diastolic/Systolic blood 
: Diastolic/Systolic blood 
pressure, heart rate, the heights of adult 
pressure, heart rate, the heights of adult 
males, the weights of preschool children 
males, the weights of preschool children 
and the ages of patients seen in a dental 
and the ages of patients seen in a dental 
clinic
clinic
Page 5


CLASSIFICATION 
CLASSIFICATION 
AND 
AND 
TABULATION OF DATA
TABULATION OF DATA
Dr.
Dr.  
Bijaya Bhusan Nanda, 
Bijaya Bhusan Nanda, 
M. Sc (Gold Medalist) Ph. D. (Stat.)
M. Sc (Gold Medalist) Ph. D. (Stat.)
Topper Orissa Statistics & Economics 
Topper Orissa Statistics & Economics 
Services, 1988
Services, 1988
bijayabnanda@yahoo.com
bijayabnanda@yahoo.com
Lecture Series on 
Lecture Series on 
Biostatistics
Biostatistics
No. Bio-Stat_3 No. Bio-Stat_3
Date – 03.08.2008 Date – 03.08.2008
LEARNING OBJECTIVES
LEARNING OBJECTIVES
?
The trainees will be able to do 
The trainees will be able to do 
meaningful classification of large 
meaningful classification of large 
mass of data and interpret the same.
mass of data and interpret the same.
?
They will be able to construct 
They will be able to construct 
frequency distribution table and 
frequency distribution table and 
interpret the same.
interpret the same.
?
They will be able to describe different 
They will be able to describe different 
parts of tables and types of table   
parts of tables and types of table   
?
Concept of Variable
Concept of Variable
?
Ordered array
Ordered array
?
What is data Classification ?
What is data Classification ?
?
Objectives of Classification
Objectives of Classification
?
Frequency distributions
Frequency distributions
?
Variables and attributes
Variables and attributes
 
 
?
Tabulation of data
Tabulation of data
?
Parts of a table 
Parts of a table 
?
Type of tables 
Type of tables 
C O N T E N T
C O N T E N T
Concept of Variable
Concept of Variable
?
Variable
Variable
A characteristic which takes on different 
A characteristic which takes on different 
values in different persons, place or 
values in different persons, place or 
things.
things.
Example
Example
: Diastolic/Systolic blood 
: Diastolic/Systolic blood 
pressure, heart rate, the heights of adult 
pressure, heart rate, the heights of adult 
males, the weights of preschool children 
males, the weights of preschool children 
and the ages of patients seen in a dental 
and the ages of patients seen in a dental 
clinic
clinic
?
Quantitative Variable:-
Quantitative Variable:-
 One that can be 
 One that can be 
measured and expressed numerically. The 
measured and expressed numerically. The 
measurements convey information regarding 
measurements convey information regarding 
amount.
amount.
Example
Example
:
:
 Diastolic/Systolic blood pressure, heart 
 Diastolic/Systolic blood pressure, heart 
rate, the heights of adult males, the weights of 
rate, the heights of adult males, the weights of 
preschool children and the ages of patients seen 
preschool children and the ages of patients seen 
in a dental clinic
in a dental clinic
?
Qualitative Variable:-
Qualitative Variable:-
 The characteristics that 
 The characteristics that 
can’t be measured quantitatively but can be 
can’t be measured quantitatively but can be 
categorized. The measurement convey 
categorized. The measurement convey 
information regarding the attribute. The 
information regarding the attribute. The 
measurement in real sense can’t be achieved but 
measurement in real sense can’t be achieved but 
persons, places or things belonging to different 
persons, places or things belonging to different 
categories can be counted.
categories can be counted.
Example:
Example:
 sex of a patient, colure and odour of  
 sex of a patient, colure and odour of  
stool and urine samples etc.
stool and urine samples etc.
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