Rajasthan Board RBSE Class 11 Economics Notes Chapter 6 Classification of Data
→ Stored data is often unorganized, complex and unrefined.
→ Unorganized and raw data are like the sundry discarded things of a scrap dealer.
→ The objective of classifying raw data is to streamline it as to make it suitable for statistical analysis.
→ The data collected in the classification is divided into different classes depending on their different characteristics and properties.
→ Classification can be real or imaginary.
→ Making the classification simple and brief, to clarify similarity and dissimilarity, to make logical arrangements, to render it helpful in comparison, etc. are the objectives of data classfication.
→ The ideal classification contains essential elements such as clarity, stability, extensiveness, suitability, elasticity, homogeneity, etc.
→ There are two types of statistical data
- descriptive or qualitative
- numerical data.
→ Qualitative data are not measured directly, they are measured on the sole basis of their being
present absent only
→ Numerical data or facts are those whose direct measurement is possible; such as income, age, height, weight, etc.
→ Variables- are the data items, whose value keeps changing.
→ On the basis of facts of qualitative and numerical data, there are two methods of classification:-
- Qualitative classification
- Numerical Classification
→ When facts are classified according to their description or properties (attributes), the process is called qualitative classification.
→ When facts are classified into two classes on the basis of presence or absence of a certain attribute (property), the process is called ordinary or binomial classification.
→ Multiple-attribute classification involves the categorization of facts on the basis of two or more than two properties (attributes).
→ In numerical classification, the data items are classified on the basis of their qualities/ (that can be measured) into various classes.
→ Each class is made up of two numbers which are called class-limits.
→ The first limit is lower limit (lsub>1) and the second limit is upper limit (l2).
→ The difference between the upper limit and lower limit in any class is called class-interval. It is denoted by (i).
→ The mid-point between the upper and lower limits of a class is called the mid-point of that class.
→ \(\text { Mid value }=\frac{l_{1}+l_{2}}{2} \text { or } \frac{\text { lower limit }+\text { upper limit }}{2}\)
→ The number of data-items occurring within the limits of a certain class, is called tb’B’cfass
frequency of that class.
→ Frequency distribution is a table in which data- items are included in the form of values or classes.
→ Variables are of two kinds and they are denoted by ‘X’:
- Discrete variable
- Continuous or indiscrete variable.
→ Discrete variables are those variables whose values are definite and discrete. These do not have class intervals, and their units are indivisible.
→ Continuous variables are those variables whose values are not definite.
→ Class magnitude or i = (Maximum value – Minimum value)/N Or (T-S)/N, where N = number of classes.
→ The organized sequence obtained by configuring statistical facts on some definite basis is called statistical series.
→ Statistical series are categorized into the following types on the basis of their construction or structure:
- Individual series
- Discrete series
- Continuous series
→ In individual series each unit is measured individually and it is not included in any group of class.
→ The series obtained by arranging discrete variable values in an ordered fashion, is called a discrete serice. ‘
→ Continuous variables have no definite value, instead they can have any value within a certain
limit or class.
→ In exclusive method, the upper limit of one class and the lower limit of the next class is the same.
→ In inclusive method, the upper limit of one class and the lower limit of the next class is not equal and the maximum difference between the two consecutive classes is 1.
→ To convert the inclusive series into exclusive series, the difference between the upper limit of a class and the lower limit of the next class is halved and the result is substacted from the lower limit (l1) and added to the upper limit (l2)
Classification of Data Class 11 RBSE Notes Important Terms
• Qualitative/Attributive Classification: When data are classified on the basis of some attributes or quality like gender, literacy, religion, employment, etc. Such attributes cannot be measured arithmetically.
• Multiple-attribute Classification: The classification, where two or more attributes are considered and several classes are formed, is called a multiple-attribute classification.
• Numerical Classification: When the data are classified on the basis of certain properties/ homogeneity (that can be measured) like age, height, weight, prices, production, income, expenditure, sales, profits, etc.
• Class-Limits: The upper and lower boundaries of the class are called class-limits.
• Class-Intervals: The difference between the upper limit and the lower limit in any class is called class-interval.
• Mid-Point: The centre of the limits of a class is called the mid-point or mid-value.
• Class Frequency: The number of data-items corresponding to a particular class is known as the frequency of the class.
• Discrete Variable Those variables which cannot take all the possible values within a given specified range are termed as discrete variables.
• Continuous Variable: Those variables which can take all the possible values (integer as well as fractional) in a given specified range are termed as continuous variables.
• Individual Series: When the observations are expressed individually, the value of each and every data-item can be observed separately, it is known as individual series.
• Discrete Series: The variable values given in this series are indivisible. Thus, the series . obtained on arranging discrete variable values in an ordered manner is called a discrete series.