Statistics for Economics for Class 11 Chapter 3 Organisation of Data
1. Classification of Data – The method of arranging data into homogeneous classes according to the common features present in the data is known as classification. Classification of data is the process of arranging data into homogeneous (similar) groups according to their common characteristics.
Raw data cannot be easily understood, and it is not fit for further analysis and interpretation. The arrangement of data helps users in comparison and analysis. For example, the population of a town can be grouped according to sex, age, marital status, etc.
2. Raw Data – Raw data is an unorganised form of data and cannot be used the way it is given. It has to be processed and organised and then can be used to get relevant information.
3. Chronological Classification – A classification where data are grouped according to time 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. It is also known as ‘Temporal classification’.
4. Geographical Classification – When data are classified with reference to geographical locations such as countries, states, cities, districts, etc., it is known as geographical classification. It is also known as ‘Spatial classification’.
5. Condition Series – In condition series, the data are classified according to the changes occurring in variables according to a condition, such as height, weight, age, marks, income, etc.
6. Attributes – Attributes can be defined as additional information about the characteristics of each spatial data in the survey. For example, attributes of a population in a survey might include their name, age, height, weight, etc.
7. Qualitative Classification – Qualitative classification of data describes the quality of something or someone. It is descriptive information. For example, skin colour, eye colour, hair texture, etc., give us qualitative information about a person.
8. Continuous Variables – Variables that can take all the possible values (integral as well as fractional) in a given specified range are termed continuous variables. For example, temperature, height, weight, marks, etc.
9. Discreet Variables – Variables that are capable of taking only an exact value and not any fractional value are termed discrete variables. For example, the number of workers or the number of students in a class is a discrete variable as they cannot be in fractions. Similarly, the number of children in a family can be 1, 2, and so on, but cannot be 1.5, 2.75.
10. Variable – The term variable is derived from the word ‘Vary’, which means to differ or change. Hence, variable means the characteristic that varies, differs, or changes from person to person, time to time, place to place, etc. A variable refers to a quantity or attribute whose value varies from one investigation to another.
11. Frequency Distribution – In statistics, a frequency distribution table is a comprehensive way of representing the organisation of raw data of a quantitative variable. This table shows how various values of a variable are distributed and their corresponding frequencies. However, one can make two frequency distribution tables:
(i) Discrete frequency distribution.
(ii) Continuous frequency distribution (Grouped frequency distribution).
12. Class Limits – The numerical figures used to specify the lower and upper limits of a ‘Class interval’ are called class limits. For example, if the class interval is 15-19 years, the lower class limit is 15, and the upper limit is 19 in this case.
13. Class Interval or Class Width – When the whole range of variable values is classified in some groups in the form of intervals, then each such interval is known as a class interval. For example, classifying a group of people according to the same age group. Here, the class intervals are 15-19 years, 20-24 years, 25-29 years, and so on.
14. Class Mid Point or Class Mark – The number in the middle of the class is called the class mark. The class mark is the midpoint between the lower and upper limits of a class. It is found by adding the upper and lower limits and dividing them by two.
15. Frequency Curve – The frequency curve is obtained by joining the points of a frequency polygon through a freehand smoothed curve and not by straight lines.
16. Tally Marking – Tally marks and graphs are used to keep and count the score. The symbol ‘|’ is used to denote the value 1. In earlier days, before the discovery of the numbers, it was tough to keep track of the individual belongings of people. For example, humans used to have domestic animals such as goats and cows; thus, keeping track of their count was very hard if the number was huge. At that time, tally marks were beneficial.
Tally marks are defined in the unary numeral system. It is a form of numeral used for counting. The general way of writing tally marks is as a group or set of five lines. The first four lines are drawn vertically, and each fifth line runs diagonally over the previous four vertical lines, i.e. from the top of the first line to the bottom of the fourth line.
17. Frequency Array – It shows different values of the variables along with their corresponding frequencies. It is a technique of classifying the data for a discrete variable.
18. Bivariate Frequency Distribution – The frequency distribution of two variables is known as bivariate frequency distribution. In other words, bivariate frequency distribution shows the series of statistical data having frequencies of two variables, such as the data on income and expenditure of the households.
19. Univariate Distribution – The word ‘Uni’ means one. A series of statistical data showing the frequency of only one variable is called univariate frequency distribution. In other words, the frequency distribution of a single variable is called univariate frequency distribution. For example, the income of people marks scored by students, etc.
20. Multivariate Distribution – Multivariate distributions show collation between at least two estimations and the connections among them. For each univariate dispersion with one irregular variable, there is a more broad multivariate distribution. For instance, the normal distribution is univariate, and its more broad partner is the multivariate normal distribution. While the multivariate normal model is the most commonly used model for examining multivariate information, there are some more the multivariate lognormal distribution, the multivariate binomial distribution, etc.
We hope that the offered Statistics for Economics Index Terms for Class 11 with respect to Chapter 3: Organisation of Data will help you.
Related Links:
- Class 11 Statistics for Economics Terms – Chapter 1: Introduction
- Class 11 Statistics for Economics Terms – Chapter 2: Collection of Data
- Class 11 Statistics for Economics Terms – Chapter 4: Presentation of Data
- Class 11 Statistics for Economics Terms – Chapter 5: Measures of Central Tendency
- Class 11 Statistics for Economics Terms – Chapter 7: Correlation
- Class 11 Statistics for Economics Terms – Chapter 8: Index Numbers
- Class 11 Statistics for Economics Terms – Chapter 9: Use of Statistical Tools
