What are the methods to classify the data?
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what type of data? please include
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Definition
Classification means arranging the mass of data into different classes or groups on the basis of their similarities and resemblances. All similar items of data are put in one class and all dissimilar items of data are put in different classes. Statistical data is classified according to its characteristics. For example, if we have collected data regarding the number of students admitted to a university in a year, the students can be classified on the basis of sex. In this case, all male students will be put in one class and all female students will be put in another class. The students can also be classified on the basis of age, marks, marital status, height, etc. The set of characteristics we choose for the classification of the data depends upon the objective of the study. For example, if we want to study the religions mix of the students, we classify the students on the basis of religion.
Purpose of Classificaiton:
Classification helps in achieving the following objectives:
1) It helps in presenting the mass of data in a concise and simple form.
2) It divides the mass of data on the basis of similarities and resemblances so as to enable comparison.
3) It is a process of presenting raw data in a systematic manner enabling us to draw meaningful conclusions.
4) It provides a basis for tabulation and analysis of data.
5) It provides us a meaningful pattern in the data and enables us to identify the possible characteristics in the data.
Methods of Classification
There are two methods of classification: i) classification according to attributes, and ii) classification according to variables.
Classification According to Attributes
An attribute is a qualitative characteristic which cannot be expressed numerically. Only the presence or absence of an attribute can be known. For example. intelligence, religion, caste, sex, etc., are attributes. You cannot quantify these characteristics. When classification is to be done on the basis of attributes, groups are differentiated either by the presence or absence of the attribute (e.g. male and female) or by its differing qualities. The qualities of an attribute can easily be differentiated by means of some natural line of demarcation. Based on this natural difference, we can determine the group into which a particular item is placed. For instance, if we select colour of hair as the basis of classification, there will be a group of brown haired people and another group of black haired people. There are two types of classification based on attributes.
1) Simple Classification : In simple classification the data is classified on the basis of only one attribute. The data classified on the basis of sex will be an example of simple classification.
2) Manifold Classification: In this classification the data is classified on the basis of more than one attribute. For example, the data relating to the number of students in a university can be classified on the basis of their sex and marital.
Classification According to Variables
Variables refer to quantifiable characteristics of data and can be expressed numerically. Examples of variable are wages, age, height, weight, marks, distance etc. All these variables can be expressed in quantitative terms. In this form of classification, the data is shown in the form of a frequency distribution. A frequency distribution is a tabular Presentation that generally organises data into classes, and shows the number of observations (frequencies) falling into each of these classes. Based on the number of variables used, there are three categories of frequency distribution: 1) uni-variate frequency distribution, 2) bi-variate fequency distribution, and 3). Multi-variate frequency distribution.
1) Uni-variate Frequency Distribution : The frequency distribution with one variable is called a uni-variate frequency distribution. For example, the students in a class may be classified on the basis of marks obained by them.
2) Bi-variate Frequency Distribution: The frequency distribution with two variable is called bi-variate frequency distribution. If a frequency distribution shows two variables it is known as bi-variate frequency distribution.
3) Multi-variate Frequency Distribution: The frequency & distribution with more than two variables is called multivariate frequency distribution. For example, the students in a class may be classified on the basis of marks, age and sex.
Classification means arranging the mass of data into different classes or groups on the basis of their similarities and resemblances. All similar items of data are put in one class and all dissimilar items of data are put in different classes. Statistical data is classified according to its characteristics. For example, if we have collected data regarding the number of students admitted to a university in a year, the students can be classified on the basis of sex. In this case, all male students will be put in one class and all female students will be put in another class. The students can also be classified on the basis of age, marks, marital status, height, etc. The set of characteristics we choose for the classification of the data depends upon the objective of the study. For example, if we want to study the religions mix of the students, we classify the students on the basis of religion.
Purpose of Classificaiton:
Classification helps in achieving the following objectives:
1) It helps in presenting the mass of data in a concise and simple form.
2) It divides the mass of data on the basis of similarities and resemblances so as to enable comparison.
3) It is a process of presenting raw data in a systematic manner enabling us to draw meaningful conclusions.
4) It provides a basis for tabulation and analysis of data.
5) It provides us a meaningful pattern in the data and enables us to identify the possible characteristics in the data.
Methods of Classification
There are two methods of classification: i) classification according to attributes, and ii) classification according to variables.
Classification According to Attributes
An attribute is a qualitative characteristic which cannot be expressed numerically. Only the presence or absence of an attribute can be known. For example. intelligence, religion, caste, sex, etc., are attributes. You cannot quantify these characteristics. When classification is to be done on the basis of attributes, groups are differentiated either by the presence or absence of the attribute (e.g. male and female) or by its differing qualities. The qualities of an attribute can easily be differentiated by means of some natural line of demarcation. Based on this natural difference, we can determine the group into which a particular item is placed. For instance, if we select colour of hair as the basis of classification, there will be a group of brown haired people and another group of black haired people. There are two types of classification based on attributes.
1) Simple Classification : In simple classification the data is classified on the basis of only one attribute. The data classified on the basis of sex will be an example of simple classification.
2) Manifold Classification: In this classification the data is classified on the basis of more than one attribute. For example, the data relating to the number of students in a university can be classified on the basis of their sex and marital.
Classification According to Variables
Variables refer to quantifiable characteristics of data and can be expressed numerically. Examples of variable are wages, age, height, weight, marks, distance etc. All these variables can be expressed in quantitative terms. In this form of classification, the data is shown in the form of a frequency distribution. A frequency distribution is a tabular Presentation that generally organises data into classes, and shows the number of observations (frequencies) falling into each of these classes. Based on the number of variables used, there are three categories of frequency distribution: 1) uni-variate frequency distribution, 2) bi-variate fequency distribution, and 3). Multi-variate frequency distribution.
1) Uni-variate Frequency Distribution : The frequency distribution with one variable is called a uni-variate frequency distribution. For example, the students in a class may be classified on the basis of marks obained by them.
2) Bi-variate Frequency Distribution: The frequency distribution with two variable is called bi-variate frequency distribution. If a frequency distribution shows two variables it is known as bi-variate frequency distribution.
3) Multi-variate Frequency Distribution: The frequency & distribution with more than two variables is called multivariate frequency distribution. For example, the students in a class may be classified on the basis of marks, age and sex.
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