Plus One Economics Notes Chapter 13 Organisation of Data

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Kerala Plus One Economics Notes Chapter 13 Organisation of Data

Classification of Data
The groups or classes of classification can be done in various ways. The way you want to classify them would depend on your requirement. Likewise, the raw data could be classified in various ways depending on the purpose at hand.

1. Chronological Classification: 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 variable ‘population’ is a Time Series as it depicts a series of values for different years.

2. Spatial Classification: In Spatial Classification the data are classified with reference to geographical locations such as countries, states, cities, districts, etc.

3. Qualitative Classification: 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.

4. Quantitative Classification: 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, the classification is a Quantitative Classification.

Continuous and discrete variable
A variable is that characteristic whose value is capable of changing from unit to unit. Variables can be continuous or discrete. A continuous variable is that which can take any value in a specified interval. Whereas discrete variables are those which can assume only certain values.

Exclusive and Inclusive Methods
Exclusive Method: Under the method, the upper-class limit is excluded but the lower class limit of a class is included in the interval. Thus an observation that is exactly equal to the upper-class limit, according to the method, would not be included in that class but would be included in the next class. On the other hand, if it were equal to the lower class limit then it would be included in that class.

Inclusive Method: There is another method of forming classes and it is known as the Inclusive Method of classification. In comparison to the exclusive method, the Inclusive Method does not exclude the upper-class limit in a class interval. It includes the upper class in a class. Thus both class limits are parts of the class interval.

Frequency Array
For a discrete variable, the classification of its data is known as a Frequency Array. Since a discrete variable takes values and not intermediate fractional values between two integral values, we have frequencies that correspond to each of its integral values.

Frequency Distribution
A frequency distribution is a comprehensive way to classify raw data of a quantitative variable. It shows how the different values of a variable are distributed in different classes along with their corresponding class frequencies.

Each class in a frequency distribution table is bounded by Class Limits. Class limits are the two ends of a class. The lowest value is called the Lower Class Limit and the highest value the Upper-Class Limit.

Class Interval or Class Width is the difference between the upper-class limit and the lower class limit. For class 60-70, the class interval is 10 (upper-class limit minus lower class limit).

The Class Midpoint or Class Mark is the middle value of a class. It lies halfway between the lower class limit and the upper-class limit of a class and can be ascertained in the following manner:

Class Midpoint or Class Mark = (Upper-Class Limit + Lower Class Limit)/2

The classmark or mid-value of each class is used to represent the class. Once raw data are grouped into classes, individual observations are not used in further calculations. Instead, the classmark is used. Frequency Curve is a Graphic representation of a frequency distribution.

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