Short Questions with Answers (Part - 1) - Organisation of Data Commerce Notes | EduRev

Economics Class 11

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Commerce : Short Questions with Answers (Part - 1) - Organisation of Data Commerce Notes | EduRev

The document Short Questions with Answers (Part - 1) - Organisation of Data Commerce Notes | EduRev is a part of the Commerce Course Economics Class 11.
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Q.1. Define classification of data.
Ans.
Classification of data is the process of organising the raw data into groups or classes to facilitate further statistical analysis.

Q.2. List the methods of classification of data.
Ans. 
The methods of classification of data are:
(i) Chronological classification
(ii) Geographical classification
(iii) Qualitative classification
(iv) Quantitative classification

Q.3. When is data said to be raw?
Ans.
Data is said to be raw when it is not arranged in a systematic order.

Q.4. Give an example of geographical classification.
Ans. 
An example of geographical classification – Data related to the sugar production in various states of India.

Q.5. Give one point of difference between qualitative and quantitative classification.
Ans. 
In qualitative classification, data is classified on the basis of certain attributes; while in quantitative classification, data is classified in numerical terms.

Q.6. State one point of difference between chronological and spatial classification.
Ans.
In chronological classification, data is classified in ascending or descending order with reference to time; while in spatial classification, data is classified according to geographical locations.

Q.7. What are the objectives of classification?
Ans. 
The following are the main objectives of classification:
(i) It makes data comparable.
(ii) It makes data more attractive and effective.
(iii) It presents the data into brief, simple and logical forms.
(iv) It enhances the utility of data as it brings similarity in the diverse set of data.
(v) It draws differences among the data.

Q.8. State the features of a good classification.
Ans. 
The following are the features of a good classification:
(i) Classification should be widespread so that the collected data can be grouped.
(ii) It should clearly indicate the group to which it belongs.
(iii) It must be homogeneous, i.e. in a group each item must be similar.
(iv) A good classification must be stable. It means that during the whole investigation, internal classification remains the same.
(v) A good classification is done as per the objective of investigation.

Q.9. What is a variable?
Ans. 
Variables are those facts which can be presented in numeric form and may assume more than one set of values.

Q.10. Define discrete variable.
Ans. 
A discrete variable can take only certain values. Its value changes only by finite ‘jumps’ from one value to another but does not take any intermediate value between them.

Q.11. What is a continuous variable?
Ans.
A continuous variable can take any numerical value. Continuous variable may take integral values, fractional values and values that are not exact fractions.

Q.12. Give two examples each of a discrete and continuous variable.
Ans. 
Examples of a discrete variable: Number of members in family, results of rolling a dice, goals in a hockey match Examples of a continuous variable: Height, weight, temperature

Q.13. State some features of discrete variable.
Ans. 
The following are some features of discrete variable:
(i) A discrete variable can take only certain values.
(ii) Its value changes by finite “jumps”.
(iii) It does not take any intermediate value.

Q.14. When is frequency distribution with unequal classes more appropriate?
Ans. 
When the classes are to be formed in such a way that class marks coincide to a value around which the observations in a class tend to concentrate then it is more appropriate to use unequal class interval.

Q.15. Why is there no class mark in a discrete frequency distribution?
Ans. 
Frequency array is the classification of data related to discrete variables. A discrete variable takes only integral values, that is, it does not take any fractional value between two adjacent integral values. Thus, there are no classes in a frequency array. Absence of classes implies no class intervals. Since the classes are absent in a discrete frequency distribution, there is no class mark as well.

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