Unit : 1 Introduction
What is Economics Meaning, Scope and Importance of statistics in Economics

Economics
T
he word Economics comes from two Greek words – ‘Eco’ meaning home, and ‘nomos’ meaning accounts. Initially, Economics was known as the Science of Wealth. It later came to be known as the study of general methods by which men achieve their material needs. Economics aims to make sure that limited resources are optimally utilized in the best possible manner. In other words, Economics is a social science concerned with the proper use and allocation of resources for the achievement and maintenance of growth stability. Economics is a science that studies human behavior which aims at allocation of scarce resources in such a way that consumer can maximize their satisfaction, producers can maximize their profits and society can maximize its social welfare. It is about making choice in the presence of scarcity.Economics is all about making choices to use goods and services to satisfy wants, with scarce resources.

To understand the meaning of economics, we must first understand the problem of wants and scarcity of resources.
Unlimited Wants
A society is made up of humans who need several goods and services in their lives. The desire to obtain these goods and services is called “Wants”. It is known that human beings have unlimited wants. For example, a person who has just bought a house may want a car after buying the house. Once he gets the car, he may want a bigger car and a bigger house, advanced electronics appliances, clothing, and so on. Not only is the Human Wants unlimited, but they also differ in priority. For every individual, some wants are more necessary than others. For example, for a person who is below the poverty line, Wants like food and shelter would be more important than the Want relating to mobile phones.

Scarcity
Scarcity means shortage of goods and resources in relation to their demand. Scarcity is the root of all Economic problem.

Economics is a social science that studies the way society chooses to use its limited resources, which have alternate uses, to produce goods and services and to distribute them among different groups of people.


Father of Economics Adam Smith provided wealth definition of economics( Book- The Wealth of Nations, 1776):- Economics is an

enquiry into the factors that determine the wealth of a country.

Scarcity definition given by Lionel Robbins(1932):- Economics is a science that studies human behaviour as a relationship between

ends and scarce means which have the alternative uses.

Economic activities

Economic activities are those activities which are related to earn money and wealth for life. These activities generate new income and increase the flow of goods and services. For example production, consumption, investment, distribution. Economic activities are
a. Production b. Consumption c. Investment d. Exchange e. Distribution

Non-economic activities
Non-economic activities are those activities which are not related to earn money and wealth. These activities neither generate income nor increase the flow of goods & services. For example, a teacher teaching his own son. Non-Economics activities are: a. Social b. Religious c. Political d. Charitable e. Parental

Statistics

Statistics, in its broadest sense, refers to data or information expressed in terms of numbers, such as employment statistics and population statistics. Statistics is the science that deals with the methods of collecting, classifying, presenting, comparing, and interpreting numerical data.
Phases of statistical study
It comprises five stages as follows

Stage 1: Collecting Data: To begin the journey of statistical research, we must first collect statistical data. In this category, samples and census techniques are frequently employed.

Stage 2: Organization of the data: Yes, organising raw or chaotic data is challenging. The classification of the collected data is the focus of the second phase for this reason. Statistics bars and data collection are used to edit the data.

Stage 3: Presentation of data: This data needs to be presented correctly after being edited. Tables, graphs, and diagrams are frequently used to present data.

Stage 4: Analysing the data: To draw conclusions about the data, we must first determine the percentages, averages, and so forth before proceeding to the final stage. The data analysis toolbox includes percentages, estimates, correlations, and regression coefficients.

Stage 5: Interpretation of Data: Finally, we must translate data and draw conclusions or construct concepts from data. The percentage size, average, and degree of relationship between various economic variables are used to accomplish this.

Scope of statistics

  • It uses numbers to illustrate the facts. For instance, keeping track of a company’s product sales.

  • It investigates the connection between one or more phenomena. For instance, in the field of medical science, for the collection, presentation, and evaluation of observed facts regarding the incidence, causes, and effects of various drugs.

  • It aids in policy formulation; Governments, for instance, use data on assets and liabilities, income and expenditures, profits and dividends, and so on to make economic policy.

  • It simplifies complicated information. For instance, in astronomy, using observations to determine the most likely measurements of heavenly bodies’ distances, sizes, masses, and densities.

  • It makes forecasting easier, such as the outcomes of the stock market, sales, GDP, etc.

  • It offers methods for evaluating the hypothesis. For instance, when developing marketing strategies.

  • It offers strategies for making decisions when there is uncertainty. For instance, determining the likelihood of a shift in customer demand.
    Importance of statistics.

Importance of statistics

  • Quantitative expression of economic problems- Quantification may be required for further research in Economics to explain the various parameters. Based on such quantitative data, comparisons can be made between various economic sectors and plan periods.

  • Working out cause and effect relationship: To formulate policies, causal relationships between various phenomena must be presented using data sets.

  • Construction of economic theories or economic models: Concrete data sets are required for the development of economic theories like price variation and product demand.

  • Economic forecasting: Forecasting changes in economic influence factors becomes more dependent on statistical data.

  • Formulation of policies: National policies are created through the process of policy formulation. When devising an efficient policy, statistics are of great assistance. Numerous economic statistics assist you in developing better policies. In this field, numerous economists employ mathematical tools.

Collection of Data - Includes sources of data: Primary and Secondary, Methods of Collecting these data.

Organisation of Data - Meaning of variables and its types, Frequency Distribution.

Presentation of Data - In tabular and diagrammatic Format

Data
A collection of facts and measurements is called data.

By providing information, data is a tool that aids in reaching a sound conclusion.
The primary step in any statistical investigation is the collection of data.

1. Collection of Data :-Data is a collection of facts and measurement. Data is a tool which helps in reaching a sound conclusion by providing information. For statistical investigation, collection of data is the first and foremost.
Sources of Data
1. Primary Source
2. Secondary Sources
a. Published sources
b. Un-published sources

Primary Data– Data originally collected in the process of investigation are known as primary data.Primary data refer to the first hand data gathered by the researcher himself.Secondary data means data collected by someone else earlier. This is original form of data which are collected for the first time.It is collected directly from its source of origin.

Methods of collecting primary data

There are three basic ways of collecting data :
(i) Personal interview Or Direct Personal Investigation
(ii) Mailing (questionnaire surveys)
(iii) Telephone interviews
(iv) Indirect verbal investigation
(v) Information from local sources
(vi) Enumerator method

Secondary data- It refers to collection of data by some agency, which already collected the data and processed. The data thus collected is called secondary data.
Primary Data Collection Techniques

  1. Direct Personal Investigation; This is the method by which the investigator personally collects data from the information holder.

These are the method’s advantages and disadvantages.

(a) Merits

  • Originality, as it is coming directly from the person who possesses the information.

  • Reliability, as the data is original and directly coming from the source it is reliable.

  • Uniformity, as the information collected is constant and direct.

  • Accuracy, as information holder provides the facts and figures with his raw intelligence so the data collected is very accurate.

  • Elastic; This method is very flexible.

(b) Demerits

  • Difficult to cover wide areas; As the investigation directly takes place it is impossible to collect the information from a large area due to limited resources.

  • Costly; This method needs investigating agents and related resources therefore it is a bit costly.

  • Personal bias; The investigator or the information provider may have some personal bias towards persons or events that need to be told exactly.

  • Limited coverage; The direct investigation is time-consuming and needs effort therefore it can cover only a limited area.

Secondary Data Collection Techniques

Secondary data is data that has already been collected and processed by another organisation.
There are two main types of secondary data - those that have been published and those that have not been published

Published sources

1. Govt. publication
2. semi-Govt. Publication
3. Reports of committees & commissions
4. Private publications e.g., Journals and News papers research institute, publication of trade association.
5. International publications
Unpublished Sources
The statistical data needn’t always be published. There are various sources of unpublished statistical material such as the records maintained by private firms, business enterprises, scholars, research workers, etc. They may not like to release their data to any outside agency.
Other source : web-site


Methods of sampling:
1. Random sampling
a. Simple or unrestricted random sampling
b. Restricted random sampling
i. Stratified -
This method of sampling divides the population into distinct strata with distinct characteristics, and some of the items are chosen from each stratum to ensure that the entire population is represented.
ii. systematic -
In this method, population units are arranged alphabetically, geographically, and numerically. As a sample, every nth item in the number is chosen.
iii. multistage or cluster sampling -
2. Non-Random Sampling
a. Judgment sampling
b. Quota sampling -
In this method, the population is broken up into various groups or classes based on various characteristics.
c. Convenience sampling
Census survey : In this method every element of population is included in the investigation.
Sample survey : In this method a group of units representing all the units of the population is investigated.

Population or universe
In Statistics, population or universe simply refers to an aggregate of items to be studied for an investigation.
Sample: A group of items taken from the population for investigation and representative of all the items.
Sampling Errors: Sampling error is the difference between the result of studying a sample and the result of the census of the whole population. 1. Biased errors 2. Unbiased errors
Non-Sampling Error: Can occur in any type of survey whether it be a census or sample survey. 1. Error in data acquisition 2. Non Response error
3. Sampling Bias.

Census of India
The census of India provides the complete and continuous demographic record of population.
ince 1881, the Census has been taken regularly every ten years. In 1951, the first Census since independence was taken. The Office of the Registrar General and Census Commissioner, India, which is part of the Ministry of Home Affairs, Government of India, is in charge of carrying out the Census. The population’s size, density, sex ratio, literacy, migration, rural-to-urban distribution, and other details are all gathered by the census. India’s most recent census was conducted in 2011. It was the seventh census since independence in 1947.

National Sample Survey Organization:

The NSSO was established by the Govt. of India in 1950 to conduct nation wide survey on socio-economic issues like employment, literacy, maternity, child care, utilisation of public distribution system etc. In March 1970, the National Sample Survey Organization (NSSO) was renamed the NSS. It is the largest organisation in South India and conducts socioeconomic survregularly.The three kinds of surveys that NSSO involves economic and social surveys, industry survey and surveys of agriculture. The data-collected by NSSO survey are released through reports and its quarterly journal ‘’Sarvekshana’’. Eg. Size, growth rate, distribution of population, density, population, projections, sex composition and literacy.
These data are used by govt. of India for planning purpose.

3.Organization of data - It refers to the systematic arrangement of collected figures (raw data), so that the data becomes easy to understand and more convenient for further statistical treatment .

Classification is the process of arranging data into sequences and groups according to their common characteristics of separating them in to different but related parts.
Characteristics of classification:
1. Homogeneity
2.Suitability
3. Clarity
4. Flexibility
5. Diversification

Basis of classification:
Raw data can be classified as:
1. 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.
2. Geographical/Spatial classification: The data are classified with reference to geographical location/place such as countries, states , cities, districts, block etc.
3. Qualitative classification: Data are classified with reference to descriptive characteristics like sex, caste, religion literacy etc.
4. Quantitative classification: Data are classified on the basis of some measurable characteristics such as height, age, weight, income, marks of students.
5. conditional classification: When data are classified with respect to condition, the type of classification is called conditional classification.
A mass of data in its original form is called raw data. It is an unorganized mass of various items.

Meaning and types of variables

A characteristic which is capable of being measured and changes its value overtime is called a variable. A variable is a characteristic which is capable of being measured and capable of change in its value from time to time.It is of two types.
(a) Discrete
(b) Continuous

Discrete: Discrete variable are those variables that increase in jumps or in complete numbers and are not fractional. Ex.-number of student in a class could be 2, 4, 10, 15,, 20, 25, etc. It does not take any fractional value between them.
Continuous variable: Continuous variables are those variables that can takes any value i.e. integral value or fractional value in a specified interval.Ex- Wages of workers in a factory.

Frequency distribution

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

The class mid-point 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 mid-point = upper class limit + lower class limit / 2.
Class frequency: It means the number of values in a particular class.
Class width:- It is the difference between the upper class limit and lower class limit
Class width = upper class Limit – Lower class Limit
Class Limits:- There are two ends of a class. The lowest value is called lower class limit and highest value is called upper class limit.

Broadly statistical series are of two types.

1. Individual series
2. Frequency series
a. Discrete series Or frequency array
b. Frequency distribution or continuous series

Individual series are those series in which the items are listed singly. For example:

Sr. No. of workers Daily wages(in Rs.)
1 25
2 50
3 35
4 40
5 20
6 45

A discrete series or frequency array is that series in which data are prescribed in a way that exact measurements of items are clearly shown. The example in following table illustrates a frequency array.
Frequency array of the size of household

Size of the household Number of household (Frequency)
1 5
2 15
3 25
4 35
5 10
6 5

A continuous series: It is that series in which items cannot be exactly measured. The items assume a range of values and are placed within the range of limits. In other words, data are classified into different classes with a range, the range is called class-intervals.
Frequency distribution or continuous series

Marks Frequency
10-20 4
20-30 5
30-40 8
40-50 5
50-60 4
60-70 3

Presentation of Data

The presentation of data means exhibition of data in such a clear and attractive manner that these can be easily understood and analysed.

Forms of Presentation of data:
1. Textual/Descriptive Presentation
2. Tabular Presentation
3. Diagrammatic Presentation
4. Graphical Presentation

1. Textual/Descriptive Presentation of Data:- In this, data is presented in the form of text. This is suitable when quantity of data is not too large.
2. TabulationIt is the process of presenting data in the form of a table.

Classification of tabular presentation of data
1. Qualitative Classification:- When classification is done according to attributes such as social status, nationality, etc. It is called qualitative classification.
2. Quantitative Classification:- In this, the data are classified on the basis of characteristics which are quantitative in nature. e.g., age, height, income, etc.
3. Temporal classification:- In this, time becomes the becomes the classifying variable and data are categorised according to time. Time may be in hours, weeks, years, etc.
4. Spatial classification:- When classification is done on the basis of place, it is called spatial classification. The place may be village, town, state, country, etc.
3. Diagrammatic Presentation: When data is presented in a simple and attractive manner in the form of diagrams is called diagrammatic presentation of data.
Types of Diagrammatic Presentation
:
1. Geometric Form
a. Pie Diagram - Pie diagram is a circle divided into various segments showing the per cent values of a series. This diagram does not show absolute values.
b. Bar Diagram - Bar diagrams are these diagrams in which data are presented in the form of bars or rectangles.
i. Simple - Simple bar diagrams are those diagrams which are based on a single set of numerical data.
ii. Multiple - These are those diagram which show two or more sets of data simultaneously.
iii. Sub Divided - Sub-divided bar diagram are those diagrams which simultaneously present total values as well as part values of a set of data.
iv. Percentage - Percentage bar diagrams are those diagrams which show simultaneously, different parts of the values of a set of data in terms of percentages.
2. Frequency Diagram - Frequency Diagram Data in the form of grouped frequency distributions are generally represented by frequency diagram like histogram, frequency polygon, frequency curve and ogive.
a. Histogram - A histogram is a two dimensional diagram. It is a set of rectangles with passes as the intervals between class boundaries and with areas proportional to the class frequency
Histogram frequency distribution are of two types
Histogram of equal class intervals Histogram of unequal class intervals
b. Frequency Polygon - Polygon is another form of diagrammatic presentation of data. It is formed by joining mid points of the tops of all rectangles in a histogram. However, a polygon can be drawn even without constructing a histogram.
c. Frequency Curve - A frequency curve is a curve which is plotted by joining the mid points of all tops of histogram by free hand smoothed curves and not by straight lines.
A cumulative frequency curve or ogive may be constructed in two ways
Less than Method In this method, beginning from upper limit of the 1st values we go on adding the frequencies corresponding to every next upper limit of the series.

More than Method In this method, we take cumulative total of the frequencies beginning with lower limit of the 1st class interval.



d. Ogive curve - Ogive or cumulative curve is the curve which is constructed by plotting cumulative frequency data on the group paper, in the form of a smooth curve.
3. Arithmetic Line Graph or Time series graph - In this graph, time(hour,day, date, week, month, year) is plotted along X-axis and the corresponding value of variable along Y-axis.

Unit : 2
Collection, Organization and Presentation of data