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STATISTICS FOR ECONOMICS CLASS-XI PART-A Questions Keywords and Flowcharts, Strictly as including MCQs, Important diagrams a...

STATISTICS FOR ECONOMICS CLASS-XI PART-A Questions Keywords and Flowcharts, Strictly as including MCQs, Important diagrams and per the Latest Very short, Short terms for easy tables for easy CBSE Pattern. answer type understanding of understanding and subjective the course questions EDITION: 2024-25 Published By: Physicswallah Private Limited Physics Wallah Publication ISBN: 978-93-6034-860-1 MRP: ???/- Mobile App: Physics Wallah (Available on Play Store) Website: www.pw.live Youtube Channel: Physics Wallah - Alakh Pandey Commerce Wallah by PW (@CommerceWallahPW) CA Wallah by PW (@CAWallahbyPW) CA Intermediate by PW (@CAintermediatebyPW) Email: [email protected] RIGHTS All rights will be reserved by Publisher. No part of this book may be used or reproduced in any manner without the written permission from author or publisher. 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This book and the individual contribution contained in it are protected under copyright by the publisher. (This Module shall only be used for Educational Purpose)  Preface A highly skilled professional team of Commerce Wallah works vigorously to ensure that the students receive the best content for their school & board exams. While there are tons of commerce study resources out there, Commerce Wallah by PW stands out by offering constantly improved, top-notch materials. Commerce Wallah team continuously works to provide supreme quality study material for the Commerce students. From the beginning, the content team comprising Subject Matter Experts, Content Creators, Reviewers, DTP operators, Proofreaders, and others are involved in shaping the material to their best knowledge and experience to produce powerful content for the students. Commerce Wallah Faculties have adopted a novel style of presenting the content in easy-to-understand language and have provided the content team with expert guidance and supervision throughout the creation and curation of this book. PW Commerce Wallah strongly believes in conceptual and fun-based learning. Commerce Wallah provides highly exam-oriented content to bring quality and clarity to the students. This book adopts a multi-faceted approach to mastering and understanding the concepts by having a rich diversity of questions asked in the school and board examination and equipping the students with the knowledge for this highly crucial exam. The main objective of this book is to provide an edge to your preparation with short & crisp. BOOK FEATURES This book, especially designed for commerce students contains:  Syllabus coverage as per CBSE.  Keywords and Important terms provided for better conceptual understanding  Flowcharts, diagrams and tables in every chapter to grasp complex topics and boost understanding.  Conquer exams with MCQs, Very short and Short Answer questions for practice with detailed solutions to understand the logic behind each answer.  Contents PART-A STATISTICS FOR ECONOMICS 1. Introduction to Statistics... 3-15 2. Collection of Data... 16-29 3. Organisation of Data... 30-42 4. Presentation of Data... 43-74 5. Measures of Central Tendency – Arithmetic Mean... 75-112 6. Measures of Central Tendency – Median and Mode... 113-167 7. Correlation... 168-193 8. Index Number... 194-228  PART A STATISTICS FOR ECONOMICS  CHAPTER 1 Introduction to Statistics TOPICS TO BE COVERED ‰ Some Important Definitions ‰ Three Distinct Components of Economics ‰ Two Branches of Economics ‰ Economic Problem ‰ Subject matter in Statistics ‰ Limitations of Statistics ‰ Functions of Statistics ‰ Importance of Statistics ‰ Distrust of Statistics SOME IMPORTANT DEFINITIONS ‰ Economics  Economics is the study of economic issues problems arising out of the fact that resources are scarce in relation to our needs/wants and the scarce resources have alternative uses. ‰ Consumer  A consumer is one who consumes goods and services for the satisfaction of his vants. ‰ Consumption  Consumption is the process of consuming goods and services for the direct satisfaction of our wants. ‰ Producer  A producer is one who produces and/or sells goods and services for the generation of income. ‰ Production  Production is the process of converting raw material into finished goods. ‰ Saving  It is that part of income which is not consumed. ‰ Investment  It is expenditure by the producer on the purchase of such assets which helps to generate income. ‰ Economic Problems  It is problem of choice arising out of the fact that resources are scarce but wants are unlimited and the resources have alternative uses. ‰ Scarcity  It is a situation when demand for a commodity is more than its supply. THREE DISTINCT COMPONENTS OF ECONOMICS ‰ Consumption:  Consumption is the process of consuming goods and services for maximum level of satisfaction. ‰ Productioon:  Production is the process of converting raw material into useful things. ‰ Distribution:  It means distribution of factor income among factors of production (Land, Labour, Capital, Entrepreneur). TWO BRANCHES OF ECONOMICS Microeconomics refers to the study of the economy as an individual unit. This branch of economics focuses on studying the economy at an individual unit level. It examines the behavior of individuals, households, and firms, analyzing how they make decisions regarding resource allocation, consumption, and production. Macroeconomics refers to the study of the economy as a whole. In contrast, macroeconomics takes a broader perspective, studying the economy as a whole. It analyzes aggregate phenomena such as overall economic output, unemployment rates, inflation, and government policies affecting the entire economy. FOUR DEFINITIONS OF ECONOMICS 1. Adam Smith Wealth Definition: Economics is a science of the study of wealth only. According to Adam Smith, economics is the science that exclusively studies wealth. In this context, wealth refers to the accumulation of material resources and assets within an economy. 2. Alfred Marshall Welfare Definition: Economics is the study of man in the ordinary business of life. Alfred Marshall defined economics as the study of humanity engaged in ordinary life activities. His welfare definition emphasizes the well-being and satisfaction derived from various economic activities. 4 Statistics for Economics Part-A PW  3. Lionel Robbins Scarcity Definition: It is making choices in the presence of scarcity. Lionel Robbins defines economics as the science of making choices in the face of scarcity. It highlights the fundamental problem of limited resources and the necessity of choosing among alternative uses for these resources. 4. Paul A. Samuelson Growth Definition: Economics is related with the allocation and use of scarce resource so that economic growth can be increased and social welfare can be promoted. According to Paul A. Samuelson, economics is concerned with the allocation and utilization of scarce resources, aiming to enhance economic growth and promote social welfare. This definition emphasizes the broader societal goals of economic activities. ECONOMIC ACTIVITIES AND NON-ECONOMIC ACTIVITIES Economic Activities Non-Economic Activities 1. Economic activities are those activities which 1. Non- Economic activities are those activities are related with earning money. which are not related to money. 2. Example: teachers providing education services 2.  Example: Religious activities and social and doctors attending to patients, as both activities, which are pursued for reasons other involve monetary transactions. than financial gain. ECONOMIC AND NON-ECONOMIC GOODS Economic Goods Non-Economic Goods Economic goods are those which are scarce and Non-Economic goods are those which are available command a price. in plenty and do not command any price. They are For example: Books, pen, milk, sugar, Bike, etc. also known as free goods. For example: Air, rain, etc. ECONOMIC PROBLEM The economic problem refers to the challenge of making choices due to three key reasons: 1. Human wants are unlimited. 2. Resources are limited 3. Resources have alternative uses Scarcity: It refers to a situation where demand for resources is more than availability of resources even at a zero price level. It underscores the inherent limitations in resources relative to unlimited human wants. Introduction to Statistics 5 MEANING OF STATISTICS Statistics Plural Sense Singular Sense STATISTICS IN PLURAL SENSE In plural sense, statistics refers to information or collection of data in terms of numbers. Features of Statistics in Plural Sense 1. Aggregate of facts:  A single number can not be a statistics because no conclusion can be made from it.  Aggregate number of facts are called statistics, as it can be compared and conclusion can also be made from them.  For example: If there are 600 students in a school. Then this data can not be a statistics as no conclusion can be made from this data. But if we say, there are 200 students from Science, 300 students from Commerce and 200 students from Arts. Now, this data is statistics as from this data, we can do comparision and conclusion can also be drawn. 2. Numerically Expressed:  The data should be in the form of numbers.  Qualitative data like small or big, rich or poor, honest, intelligent, healthy or unhealthy, educated or uneducated etc. are not statistics.  For example: Ram is tall and Sham is short. This is not statistics. But if we say Ram height is 6 ft. but Sham height is 5 ft. Then this data is statistics because it is in the form of numbers. 3. Multiplicity of Causes:  Statistics are not affected by single factor, but affected by multiple factors.  For example: 20% rise in the price due to increase in demand, decrease in supply, increase in cost of production, rise in taxes etc. 4. Reasonable Accuracy:  The data should be reasonable accurate so that accurate conclusion can be drawn from that. 5. Mutually related and comparable:  The numerical data should be related to each other so that it can be comparable and conclusion can be drawn.  For example: Ram is 20 years old, Sham is 6 ft tall and Rohan has 50 kg of weight. This data is in numerical form but still is not statistics because these data are not related to each other. So, it can’t be comparable and no conclusion can be drawn. 6. Pre-determined objectives:  The data which has been collected should have a pre-determined objective. 6 Statistics for Economics Part-A PW   The person who is collecting that data should know why he is collecting that data and what is the objective behind collecting a data.  The data which is collected without any definite objective is just a numerical data, not statistics. 7. Enumerated or Estimated:  Statistics may be collected in a estimated way.  If the area of investigation is large, then the procedure of estimation may be helpful.  For example: 3 lakhs people attend the rally in Delhi. This statistics is based on estimation. 8. Collected in a Systematic Manner:  Data should be collected in a systematic manner.  Planning must be done before collecting a data so that proper conclusion can be drawn.  For example: Marks of students are collected without any reference to class, subject, roll no, name etc. will lead to no conclusion. Conclusion: “All numerical data can’t be called statistics but all Statistics are called numerical data.” STATISTICS IN SINGULAR SENSE Singular statistics means collection, organisation, presentation, analysis and interpretation of data. STAGES OF STATISTICS 1. Collection of Data: The initial stage involves gathering relevant data from various sources, ensuring it is comprehensive and representative of the phenomenon under study. 2. Organization of Data: Once collected, data is organized systematically. This includes sorting, classifying, and structuring the information to facilitate analysis. 3. Presentation of Data: Data is presented visually or in tabular form, making it easier to comprehend. This stage enhances the clarity of the information for further analysis. 4. Analysis of Data: Statistical analysis is performed to identify patterns, trends, and relationships within the data. This step involves applying statistical methods to draw meaningful insights. 5. Interpretation of Data: The final stage involves interpreting the results of the analysis. Conclusions are drawn, and implications are explained, providing a deeper understanding of the phenomenon studied. Scope of Statistics Nature of Subject Matter Limitations of Statistics of Statistics Statistics Descriptive Statistics Inferential Statistics Introduction to Statistics 7 NATURE OF STATISTICS According to Prof. Tippet, Statistics is both as well as an art. As a science, Statistics studies numerical data in a scientific or systematic manner. As a art, Statistics relates to real life problems. It means by using statistical data, we are able to analyse and understand real life problems much better (unemployment, population, inflation, poverty etc.) SUBJECT MATTER IN STATISTICS Subject Matter of Statistics Descriptive Statistics Inferential Statistics 1. Descriptive statistics It refers to those methods which are used for the collection, presentation as well as analysis of data. It includes techniques like measures of central tendency (mean, median, and mode) and measures of dispersion (range, variance, standard deviation) to provide a comprehensive overview of the data. 2. Inferential statistics Inferential statistics deals with methods by which conclusions are drawn beyond the immediate data at hand. It involves making inferences or predictions about a population based on a sample, using techniques such as hypothesis testing and confidence intervals. LIMITATIONS OF STATISTICS According to Newshome, “Statistics must be regarded as an instrument of research of great value but barring serve limitations which are not possible to overcome.” 1. Study of Numerical Facts only:  Statistics studied only Numerical data.  It does not study Qualitative data like rich, poor, honest, intelligent, short, tall, educated, uneducated, healthy, unhealthy, friendship etc. 2. Study of Aggregates only:  Statistics studies only aggregate data.  It does not study single data as no conclusion can be drawn from that.  For example: If there are 600 students in a school. Then this data can not be a statistics as no conclusion can be made from this data. But if we say, there are 200 students from Science, 300 students from Commerce and 100 students from Arts. Now, this data is statistics as from this data, we can do comparison and conclusion can also be drawn. 3. Homogeneity of Data, an essential Requirement:  The data should be homogeneous or uniform in nature so that it can comparable and conclusion can be drawn from it. 8 Statistics for Economics Part-A PW   Data of different qualities or features can not be comparable.  For example: The production of Rice can not be compared with the production of cloth because Rice are measured in tonnes but cloth is measured in metres. 4. Result are true only on Average:  Most results are true only on average basis  For example: Prices of goods increases by 10%. But it does not mean that prices of all goods and services have uniformly increases by 10%. It only means that on average there has been rise of 10% in the price of goods and services. 5. Without Reference, Results may prove to be wrong:  In order to understand the conclusion, it is necessary that circumstances and conditions should also be studied.  For example: Income of Two Firms A and B for the years 2021-2023 Year Income (`) Firm A Firm B 2021 1000 3000 2022 2000 2000 2023 3000 1000 6000 6000 X= = 2000 X= = 2000 3 3 In the above Table, average income of both A and B Firm is equal during the years 2021 to 2023. But a close examination of the series reveals that while the income of Firm A is increasing over years, but the income of Firm B is decreasing. Thus, it is necessary that in order to understand the conclusion, circumstances and conditions should also be studied. 6. Can be used only by Experts: The statistical data can be used only by the experts who have a specialise knowledge of statistical methods. Data in the hand of unqualified people leads to disastrous result. In words of Yule and Kendall, “Statistical methods are most dangerous tools in the hands of inexperts”. 7. Prone to Misuse: The data which is collected can be manipulated according to one’s own interest. In such a way data are misused. “Statistical are like a clay by which you can make a good or a devil, as you please”. FUNCTIONS OF STATISTICS 1. Simplifies complex data: Statistics simplifies complex data by summarizing it into key metrics and patterns, making it easier to understand and interpret. Introduction to Statistics 9  2. Presents facts in numerical form: By presenting information numerically, statistics provides a clear and concise representation of facts, aiding in effective communication and analysis. 3. Provides a technique of comparison: Statistics offers a method for comparing different sets of data, enabling the identification of similarities, differences, and trends. 4. Studies relationship: Statistical analysis explores relationships between variables, helping to identify correlations or causal links between different factors. 5. Helps in formulating policies: Statistics provide evidence-based insights that aid in the formulation of policies. Decision-makers can use statistical data to develop effective strategies and plans. 6. Helps in forecasting: Utilizing historical data and trends, statistics enables forecasting of future outcomes, supporting businesses and policymakers in making informed predictions. IMPORTANCE OF STATISTICS Statistics is widely used in many fields: 1. Importance to the Government: Statistics play a vital role in government administration by providing data for informed decision- making. It contributes to the efficient functioning of various departments and aids in achieving welfare objectives by collecting relevant and accurate information. 2. Importance in Economics: (a) Making Economic Laws: Statistics is instrumental in formulating economic laws, such as the law of demand and concepts like elasticity. It provides empirical evidence and quantitative support for these fundamental principles. (b) Understanding and solving economic problems: In economics, statistics serves as a powerful tool for understanding and addressing economic challenges. It provides insights into trends, patterns, and relationships that contribute to problem-solving. (c) Studying market structure: Statistics is crucial for analyzing market structures. It helps economists examine market behavior, competition, and dynamics, providing valuable information for market studies. (d) Finding mathematical relations between variables: Through statistical methods, economists can identify mathematical relationships between different economic variables. This aids in constructing models that represent the complexities of economic systems. 3. Importance in Economic Planning: Statistics provides a valued interpretation of relevant facts and figures essential for economic planning. It is indispensable for forecasting, which is a cornerstone of effective planning. Statistics equips planners with tools for estimation and prediction. 10 Statistics for Economics Part-A PW  4. Importance in Business: Business statistics helps a business to deal with uncertainties by forecasting seasonal, cyclic and general economic fluctuations. Helps in sound decision making by providing accurate estimates about cost demand, prices, sales etc. Helps in business on the basis of sound predictions and assumptions. DISTRUST OF STATISTICS Distrust of statistics means having no trust or confidence in statistical data. The common beliefs about statistics are: 1. Statistics can prove anything: There is a common perception that statistics can be manipulated to support any claim, highlighting the importance of careful interpretation. 2. There are three types of lies- lies, dam lies and statistics: This belief suggests that statistics, when misused, can be as deceptive as outright lies. 3. Statistics is the rainbow of lies: The metaphor implies that statistics can create an illusion, much like a rainbow, emphasizing the need for caution in interpreting statistical data. 4. Statistics are like clay by which you can make a god or devil, as you please. Statistics can also be presented in such a way which can confuse a reader. Statistical data can be manipulated in order to get the predetermined conclusions. Therefore, it is important to understand that statistics is a tool, which if misused can cause a disaster. Statistics neither approves nor disapproves anything. Hence, we must take utmost care and precaution while interpreting statistical data in all manifestations. HOW TO REMOVE DISTRUST? Following are some solutions to remove of the distrust of Statistics: (i) Always consider Statistical Limitations: While using Statistics data, always consider the limitations of Statistics. (ii) Impartial: The data should be collected without any biasness so that the reliable conclusion can be drawn. (iii) Used by Experts: Statistical data should be used only by the experts so that misused of data can be minimised. EXERCISE MULTIPLE CHOICE QUESTIONS 2. _____________________ is someone who receives compensation for providing services to 1. A person who purchases things and services another person. in order to fulfill his desires is ____________. (a) Provider of services (a) Manufacturer (b) Producer (b) Customer (c) Service Holder (c) Provider of Services (d) Customer (d) Holder of Services Introduction to Statistics 11 3. A person who generates goods and services 10. The fundamental cause of the current is ___________. economic issues is: (a) Service holder (a) Unlimited Wants (b) Service provider (b) Scarcity (c) Producer (c) Alternative applications (d) Consumer (d) None of the above 4. Work that is done for a living is referred to 11. “Statistics” is used in which sense: as ________. (a) Singular (a) Economic operations (b) Plural (b) Non-Economic operations (c) Both (a) and (b) (c) Either (a) or (b) (d) None of these (d) Both of the options 12. The term “statistics” in singular form means: 5. Out of the following, which ones are economic (a) Data from Statistics activities? (b) Methods of Statistics (a) Distribution (c) Inductive Statistics (b) Production (d) Descriptive Statistics (c) Consumption 13. Statistics is the science of analysing: (d) All of the Above mentioned (a) Data of any form 6. The following are affected by resource (b) Quantitative data scarcity: (c) Qualitative data (a) People (b) Organisations (c) Countries (d) All of the above (d) Both (a) and (b) 7. The concept of welfare economics provided 14. The plural noun “statistics” suggests: by: (a) Inductive Statistics; (a) Adam Smith (b) Lionel Robbins (b) Descriptive Statistics; (c) Alfred Marshall (d) Prof. Samuelson (c) Statistical Data 8. What definition did Adam Smith provide? (d) Statistical Methods (a) Definition of Welfare 15. The statistics is concerned with: (b) Definition of Wealth (a) The aggregate of Organised facts (c) Definition of Scarcity (b) Aggregate of Disorganised facts (d) Definition of Growth-Oriented (c) Sum of pointless information 9. Among the following, which one is not (d) Compilation of unconnected facts economic activity? 16. The following factors contribute to distrust (a) A housewife preparing meals for her in statistics: household. (a) Misuse of statistics (b) A physician ministering to patients at his clinic (b) Inadequate statistical methods (c) Factory workers (c) Limited statistical scope (d) A chef preparing meals in a restaurant (d) Limitations of statistics 12 Statistics for Economics Part-A PW 17. Numerical data is used to define statistics in (a) Data collection the following ways: (b) Aggregate of facts (a) Singular sense (b) Plural sense (c) Data analysis (c) Either (a) or (b) (d) Both (a) and (b) (d) Data interpretation 18. Who uses statistics tools and methods? 20. Which of the following does not constitute a (a) Government officials statistical limitation? (a) One can misuse statistics. (b) Entrepreneurs (b) Statistics does not examine qualitative (c) Financial analysts phenomena. (d) All of the above (c) The laws of statistics are not exact 19. Which of the following, taken in the singular (d) Statistics deals with the totality of the sense, is not a feature of statistics? facts SUBJECTIVE TYPE QUESTIONS 1. Identify one constraint of statistics. 6. What is the role of a producer? 2. What is the definition of economic activity? 7. Create a list of things to do at a bus terminal or a market. What is the number of economic 3. What are activities that are not related to the activities among them? economy? 8. “You have infinite desires and finite resources 4. Describe one purpose of statistics. to fulfill them.” Elaborate on this statement 5. Explain the meaning of consumer. by providing two examples. Answers 1. (b) 2. (a) 3. (c) 4. (a) 5. (d) 6. (d) 7. (c) 8. (b) 9. (a) 10. (b) 11. (c) 12. (b) 13. (b) 14. (c) 15. (a) 16. (a) 17. (b) 18. (d) 19. (b) 20. (d) Solutions 1. Statistics exclusively deals with quantitative 5. A consumer is someone who purchases data. goods and services to fulfill their desires. 2. An economic activity refers to an activity that 6. A producer is someone who creates or sells relies on limited resources to fulfill human goods and services in order to make money. desires. 7. Different activities in a bus station or a 3. The activities that do not involve making market area include: money or have no economic connection. (i) Stores offering products and services. 4. Statistics delivers data in a compact form. (ii) Street vendors selling veggies and fruits. Introduction to Statistics 13  (iii) Buses of different destinations waiting Instances: for their passengers. (i) Imagine you have 20,000 and you wish (iv) Ticket seller at the bus station. to buy a Laptop and a Smartphone. With (v) Individuals sharing ideas, perspectives, barely 20,000 in hand, you can’t afford and thoughts with one another while both. You have the option to purchase awaiting their bus. either a Laptop or a Smartphone. Economic activities refer to the actions that (ii) Assume the Government aims to boost are carried out to make a living among all the sugar output in order to meet growing different activities. Regarding the specific human demands. Now, this can only be situation, (i), (ii), and (iv) can be classified as achieved by decreasing the production economic activities. However, (iii) and (v) are of other items since every economy has non-economic activities because they are driven by emotional motivations and are not finite resources. focused on generating money. These examples clearly show a basic economic 8. Human desires are infinite, meaning they can situation: ‘Because our resources are finite, never be completely fulfilled. Once a desire we always have to choose between different is fulfilled, a different desire arises. The goods’. desires of individuals are boundless and continue to increase, but cannot be fulfilled due to finite means. CHAPTER SUMMARY & GLOSSARY ‰ Wealth Orientation: Adam Smith views ‰ Microeconomics: Focuses on individual economics as the study of factors determining economic units and variables. a country’s wealth and growth. ‰ Macroeconomics: Examines the functioning ‰ Material Welfare Focus: Marshall sees of the entire economy as a whole. economics as studying human actions related to acquiring and using material well-being. DEFINITION OF STATISTICS: ‰ Scarcity Perspective: Robbins defines ‰ Statistics encompasses both plural and economics as analyzing human behavior in singular senses. managing scarce resources with alternative ‰ Plural: Quantitative data collected uses. systematically. ‰ Growth Emphasis: Samuelson defines ‰ Singular: Science of statistical methods. economics as the science concerned with society’s resource allocation for present and CHARACTERISTICS OF STATISTICS future consumption. (PLURAL SENSE) ‰ Importance of Economics: Consumers ‰ Aggregation of facts benefit from understanding market dynamics. ‰ Numerical expression or estimation Producers rely on economic principles for ‰ Influenced by multiple causes decision-making. Workers’ welfare is influenced by economic conditions. Price ‰ Required accuracy standards determination is guided by economic forces. ‰ Purpose-driven collection Distribution problems can be addressed ‰ Interrelatedness through economic analysis. ‰ Systematic collection 14 Statistics for Economics Part-A PW CHARACTERISTICS OF STATISTICS ‰ Not applicable to individuals. (SINGULAR SENSE): ‰ Statistical results don’t always represent ‰ Data collection group values accurately. ‰ Data classification ‰ Subject to bias and exploitation. ‰ Data tabulation ‰ Short-term statistical laws may not hold true. ‰ Data presentation DISTRUST OF STATISTICS: ‰ Data analysis and interpretation Misuse can lead to misinterpretation. Statistics themselves are not inherently flawed; it’s their FUNCTIONS OF STATISTICS: misuse that’s problematic. ‰ Simplifying complex facts ‰ Economy: How people make a living. ‰ Comparing data ‰ Consumer: Someone who buys stuff to fulfill ‰ Establishing relationships their needs and wants. ‰ Enlarging knowledge ‰ Consumption: Using things up to satisfy ‰ Formulating policies wants. ‰ Measuring effects ‰ Producer: People who make or sell stuff to ‰ Hypothesis testing earn money. ‰ Forecasting ‰ Production: Making stuff that people find useful. NATURE OF STATISTICS: ‰ Saving: Money left over after spending. ‰ Combination of science and art ‰ Investment: Spending money on things that ‰ Art: Methodologies make more money. ‰ Science: Application of scientific methods ‰ Economic Activity: Doing things to earn money. IMPORTANCE OF STATISTICS IN ‰ Non-Economic Activity: Activities not about ECONOMICS: making money. ‰ Vital for production, consumption, exchange, ‰ Microeconomics: Study of how individuals distribution, and economic planning. and small groups handle money. LIMITATIONS OF STATISTICS: ‰ Macroeconomics: Study of how whole economies work. ‰ Results may lack uniformity. ‰ Statistics: Using numbers to understand and ‰ Not suitable for qualitative studies. make decisions about money and the ‰ Relies heavily on averages, which can lead to economy. inaccuracies.  Introduction to Statistics 15 CHAPTER 2 Collection of Data TOPICS TO BE COVERED ‰ Collection of data is the first important aspect of statistical survey. ‰ Data ‰ Two Sources of data ‰ Primary data Vs Secondary data ‰ Methods/Sources of Collection of Primary Data ‰ Sources of Collection of Secondary Data ‰ Census method Vs Sampling Method ‰ Methods of Sampling COLLECTION OF DATA IS THE FIRST IMPORTANT ASPECT OF STATISTICAL SURVEY ‰ Population or universe. In statistics, population or universe simply the totality of items to be studied for an investigation. The population or universe refers to the entire group of individuals or items that are the subject of the study. It could be people, objects, events, etc. Example: If you were conducting a survey about the average income of families in a city, the population would be all the families in that city. ‰ Sample - A group of items taken from the population for investigation and representative of all the items. A sample is a subset of the population/universe that is selected for the actual study. It’s important that the sample is representative of the entire population to ensure the findings can be generalized. Example: If you want to study the average height of people in a country, you might select a sample of individuals from different regions and demographics. ‰ Census survey - In this method each and every item of population is included in the investigation. This means the entire population is studied. Example: Conducting a census survey of all households in a town to gather information about their educational levels. ‰ Sample survey - In this method, few samples are taken from the entire population and on the basis of those samples, conclusions are drawn. Example: Instead of surveying every employee in a large company, you might randomly select and survey a representative sample of employees. DATA Information which can be expressed in numbers. It can include numbers, measurements, or other quantifiable observations. ‰ Statistical enquiry = Statistical survey A statistical enquiry or survey involves collecting, analyzing, interpreting, presenting, and organizing data to make informed decisions or draw conclusions about a population. ‰ Survey A survey is a method of collecting information or data from a sample of individuals or items within a population. Surveys can be conducted through various means, such as questionnaires, interviews, or observations. ‰ Enumerator An enumerator is the person responsible for collecting information from respondents during a survey. Enumerators play a crucial role in ensuring that data is gathered accurately and efficiently. ‰ Respondent A respondent is the individual or entity from whom information is collected during a survey. Respondents provide answers to survey questions and contribute the data needed for analysis. ‰ Investigator An investigator is the person who conducts a statistical inquiry or survey. This role involves planning and designing the survey, selecting the sample, training enumerators, and overseeing the overall data collection process. ‰ Pilot Survey A pilot survey, also known as a trial survey, is a small-scale, preliminary study conducted to test the feasibility, reliability, and validity of survey instruments and procedures before implementing the full-scale survey. It helps identify potential issues and allows for adjustments to be made before the main survey. TWO SOURCES OF DATA ‰ Internal Sources 1. Internal sources of data refer to information that is collected from within the organization itself. This data is typically generated and maintained as part of the organization’s routine operations and activities. 2. For example, an organization’s internal sources of data may include reports, records, databases, financial statements, and other documents that are produced and kept within the company. For instance, an annual report detailing profit and loss, total sales, loans, wages, and other financial metrics would be considered an internal source of data. Collection of Data 17 ‰ External Sources 1. External sources of data involve gathering information from outside the organization. This data is obtained from entities or agencies that are not part of the organization itself. 2. For example, if a tour and travel company wants to gather information about tourism in Kashmir and obtains data from the Kashmir Transport Corporation, this would be considered an external source of data. Other examples of external sources include government publications, industry reports, surveys conducted by third-party organizations, and data obtained from suppliers or customers. PRIMARY DATA VS SECONDARY DATA ‰ Primary Data 1. Primary data is original and collected first hand by the researcher for a specific purpose or study. 2. Collecting primary data typically takes more time because it involves designing data collection methods, reaching out to respondents, and gathering information directly. 3. It is generally more costly to collect primary data as it involves resources such as manpower, materials, and time. 4. Primary data is considered more accurate because it is specifically collected for the particular research question or objective. The researcher has control over the data collection process, ensuring relevance and precision. ‰ Secondary Data 1. Secondary data is not original; it has been collected by someone else for a purpose other than the researcher’s current study. 2. Collection of secondary data is less time-consuming since the data already exists and doesn’t require the researcher to go through the entire data collection process. 3. Secondary data is comparatively cheaper as it doesn’t involve the direct costs associated with data collection. However, acquiring some secondary data may still have associated costs. 4. Secondary data may be less accurate compared to primary data because it was not collected with the specific research question or study objectives in mind. The researcher has less control over the methods used to collect the data originally. METHODS/SOURCES OF COLLECTION OF PRIMARY DATA ‰ Direct Personal Interview: Data is personally collected by the interviewer who directly interacts with the respondent. This method involves face-to-face communication between the interviewer and the participant, allowing for real-time exchange of information. The interviewer poses questions and records the respondent’s answers. This approach is commonly used in various research fields, surveys, and qualitative studies. 18 Statistics for Economics Part-A PW  Advantages 1. Highest response rate: Direct personal interviews often yield a high response rate because the interviewer can actively engage with the respondent, answer questions, and address concerns, leading to increased cooperation. 2. Allow all types of questions: This method allows for a variety of question types, including open-ended and closed-ended questions. It provides flexibility in the types of information collected. 3. Allow clearing doubts regarding questions: Interviewers can clarify any confusion or doubts respondents may have about the questions. This can contribute to the accuracy and reliability of the data collected. Disadvantages 1. Most expensive: Direct personal interviews can be resource-intensive and expensive. Costs may include hiring and training interviewers, travel expenses, and the time required for face- to-face interactions. 2. Informants can be influenced: The presence of an interviewer may influence the responses of the informants. Respondents may feel social pressure to provide answers that are perceived as more socially acceptable or favorable. 3. Takes more time: Conducting direct personal interviews is a time-consuming process. It involves scheduling appointments, traveling to the interview location, and spending time on the actual interview. This can be a limitation, especially when a large sample size is needed. ‰ Indirect Oral investigation: Data is collected from third parties who have information about the subject of enquiry. Indirect oral investigation, also known as third-party reporting or indirect questioning, is a method of data collection where information about the subject of inquiry is gathered from individuals or sources other than the primary individuals involved. This approach involves talking to people who have knowledge or observations related to the subject but are not the direct subjects of the research. Key characteristics of indirect oral investigation include: 1. Data Collection from Third Parties: Information is obtained from individuals who are not the direct subjects of the study but have knowledge or insights into the subject. 2. Interviewing Informants: Researchers may conduct interviews with informants who can provide relevant information based on their observations, experiences, or interactions with the primary individuals under investigation. 3. Ensuring Anonymity: Informants may be kept anonymous to encourage honest and unbiased reporting, especially when dealing with sensitive topics. 4. Verifying Information: Researchers may cross-reference information obtained through indirect oral investigation with other sources to enhance reliability and validity. 5. Generally used in Sensitive or Confidential situations: This method is often employed when direct questioning of the primary individuals is not feasible or might lead to biased or unreliable responses, such as in cases of sensitive topics or legal investigations. Collection of Data 19  Questionnaire: A list of questions by the researcher with space for answers to be given by the respondent. Qualities of a good questionnaire 1. A covering letter with objectives and scope of survey 2. Minimum number of questions. 3. Avoid Personal questions. 4. Questions should be clear and simple. 5. Questions should be logically arranged. ‰ Mailed Questionnaire: Data is collected through questionnaire mailed to the informant. Questionnaires are sent to respondents through postal mail or other mail delivery services. Respondents are expected to complete the questionnaire on their own without direct interaction with the researcher. Advantages 1. Least expensive: Mailed questionnaires are cost-effective compared to other methods, as they eliminate the need for face-to-face interactions or phone calls. This makes them a budget- friendly option for large-scale surveys. 2. Only method to reach remote areas: Mailed questionnaires are particularly useful for reaching respondents in remote or geographically dispersed areas. This method allows for broad coverage without the need for direct contact. 3. Informants can’t be influenced: Respondents may feel less pressured or influenced when completing a questionnaire in the privacy of their own space, potentially leading to more honest and thoughtful responses. Disadvantages 1. Long response time: Mailed questionnaires often have a longer response time compared to other methods. This delay can impact the timeliness of data collection and analysis. 2. Cannot be used by illiterates: Mailed questionnaires may not be suitable for individuals who are illiterate or have difficulty understanding written instructions. This can introduce a bias in the sample. 3. Doubts cannot be cleared regarding questions: Since there is no direct interaction between the researcher and the respondent, any doubts or confusion regarding the questions cannot be immediately clarified. This may lead to incomplete or inaccurate responses. ‰ Questionnaire filled by enumerators: When a questionnaire is filled by enumerators, it involves the use of trained individuals who administer the survey and record the responses on behalf of the respondents. This method is commonly employed in various types of research, especially when direct interaction between an interviewer and a respondent is preferred. Here are some key points related to this data collection approach. 1. Role of Enumerators: Enumerators are individuals who are trained to conduct surveys and administer questionnaires. They play a crucial role in collecting accurate and consistent data. 20 Statistics for Economics Part-A PW  2. Face-to-Face Interaction: Enumerators engage in face-to-face interactions with respondents, asking questions from the questionnaire and recording the responses on behalf of the respondents. ‰ Information from Correspondents: When information is collected from correspondents, it involves gathering data from individuals or agents who are appointed or designated in the specific areas of investigation. These correspondents act as representatives or sources of information in their assigned regions or fields. 1. Role of Correspondents: Correspondents are individuals appointed or designated to represent and collect information within a specific geographic area, community, or domain. 2. Local Expertise: Correspondents are often chosen for their local knowledge, expertise, or familiarity with the subject matter. They serve as on-the-ground sources of information. 3. Application: In journalism, correspondents are reporters stationed in specific regions to cover local news and events. Companies may use correspondents to gather information about local markets, consumer preferences, and competition in different areas. ‰ Telephonic interview: Telephonic interview is a method of data collection where an interview is conducted over the telephone between an interviewer and a respondent. This approach involves asking questions and recording responses, similar to face-to-face interviews, but it takes advantage of the convenience of telephone communication. Advantages 1. Relatively low cost: Telephonic interviews are generally more cost-effective compared to face-to-face interviews since they eliminate the need for travel and on-site resources. 2. Relatively high response rate: Telephone interviews often have a higher response rate compared to mailed questionnaires or other self-administered methods. The direct interaction may encourage participation. 3. Less influence on Informants: The absence of a physical presence can reduce the potential for social desirability bias or other forms of influence, as respondents may feel more comfortable expressing their opinions over the phone. Disadvantages 1. Limited use: Telephonic interviews may not be suitable for all types of research or for populations with limited access to phones. Some topics may require a more in-depth, face- to-face approach. 2. Reactions cannot be watched: Without visual cues, researchers miss non-verbal communication such as body language and facial expressions, potentially limiting the depth of understanding of the respondents’ reactions. 3. Respondents can be influenced: While telephonic interviews may reduce certain forms of influence, there is still the potential for the interviewer’s tone, language, or style to influence respondent’s answers. Collection of Data 21  SOURCES OF COLLECTION OF SECONDARY DATA ‰ Published Source Published sources refer to data that is made available to the public through various means. These sources are accessible to a wide audience and are typically produced by authoritative entities. Examples: 1. Government publications: Reports, statistics, and documents released by government agencies. For example, census data, economic reports, and demographic statistics. 2. Semi-government publications: Documents and reports published by organizations that have government affiliations but may operate independently. Examples include reports from quasi-governmental bodies or public-private partnerships. ‰ Unpublished Source Unpublished sources are data and information that is not made available to the public through standard channels. This data is often collected for internal purposes or records by specific organizations. Examples: 1. Census Data (Internal Records): While census data is often published for public use, organizations may also collect and maintain internal records from census data for their own purposes. 2. Internal Organizational Records: Any data collected and used by an organization for its own record-keeping, decision-making, or analysis. This could include sales data, employee records, or project-related information. CENSUS METHOD VS SAMPLING METHOD ‰ Census method 1. Every unit of population studied: The census method involves collecting data from every individual or unit within the entire population. No sampling is done; the goal is to include everyone. 2. Reliable and accurate results: Because data is collected from the entire population, the results are considered highly reliable and accurate. It provides a comprehensive overview of the entire group. 3. Expensive method: Conducting a census can be costly and resource-intensive. It requires resources to reach every unit in the population, making it impractical for large populations. 4. Suitable when population is of homogenous nature: The census method is particularly suitable when the population is relatively small or when it is homogenous, meaning that units within the population are similar in characteristics. ‰ Sampling Method 1. Few units of population are studied: The sampling method involves selecting a subset or sample of the population for study. Data is collected from this sample, and conclusions are drawn about the entire population based on the findings. 22 Statistics for Economics Part-A PW  2. Less reliable and accurate results: While statistical methods are used to make the sample representative of the population, there is always some degree of uncertainty. Results are considered estimates, and there is a margin of error. 3. Less expensive method: Sampling is generally less expensive than conducting a census, as data is collected from a smaller subset of the population. This makes it more feasible for larger populations. 4. Suitable when population is of heterogeneous nature: Sampling is particularly useful when the population is large, diverse, or heterogeneous. It allows researchers to gather insights without the need to study every individual. METHODS OF SAMPLING ‰ Random Sampling (Probability sampling): It is a sampling method in which all the items have equal chance of being selected. ‰ Simple or Unrestricted random Sampling: In simple random sampling, every individual or element in the population has an equal chance of being selected for the sample. This is achieved through random selection techniques such as random number generators or drawing names from a hat. 1. Complex or Restricted random Sampling (a) Stratified or mixed Sampling: In stratified sampling, the population is divided into subgroups or strata based on certain characteristics. Samples are then randomly selected from each strata, ensuring representation from each subgroup. (b) Systematic sampling: In systematic sampling, every kth individual is selected from a list after an initial random start. This method is efficient and suitable for ordered populations. (c) Multistage or cluster sampling: Multistage or cluster sampling involves a multistep process where the population is divided into clusters. Random clusters are then selected, and individuals within those clusters are sampled. ‰ Non-Random Sampling (Non-Probability sampling): It is a Sampling method in which all the items do not have an equal chance of being selected and judgment of the investigator plays an important role. 1. Judgment sampling: In judgment sampling, the researcher uses their judgment to select participants who are believed to be representative of the population. This method relies on the researcher’s expertise and may introduce biasness in the data collection process. 2. Quota sampling: Quota sampling involves dividing the population into specific subgroups based on certain characteristics. The researcher then sets quotas for each subgroup and selects individuals to meet those quotas. This method allows for control over the composition of the sample. 3. Convenience sampling: Convenience sampling involves selecting participants based on their availability and accessibility. This method is convenient but may lead to a non-representative sample, as it relies on individuals who are easy to reach. Collection of Data 23  TYPES OF STATISTICAL ERRORS ‰ Sampling Errors: Sampling errors are differences between the characteristics of a sample and the characteristics of the entire population from which the sample is drawn. These errors result from the inherent variability in randomly selecting samples. ‰ Non-Sampling Errors Non-sampling errors are errors that occur at any stage of data collection, analysis, or interpretation, excluding the variability due to random sampling. They can arise from various sources and may affect the accuracy and reliability of study results. ‰ Types of Non-sampling Errors: 1. Error of measurement due to incorrect response: This occurs when respondents provide incorrect or inaccurate information due to misunderstanding a question, memory issues, or intentional misreporting. 2. Errors of non-response of some units of the sample selected: Non-response errors occur when selected individuals or units in the sample do not participate or provide data. This can introduce bias if non-respondents differ systematically from respondents. 3. Sampling bias occurs when sample does not include some members of the target population: Sampling bias occurs when certain segments of the target population are not included in the sample, leading to an unrepresentative sample. This can result in skewed or inaccurate conclusions about the population. CENSUS OF INDIA  The Census of India is conducted to gather a complete and continuous demographic record of the population. It is one of the largest administrative and statistical exercises in the world.  The census is usually conducted every ten years, providing a comprehensive snapshot of the population’s characteristics, including demographic, social, and economic aspects. NATIONAL SAMPLE SURVEY ORGANIZATION  NSSO conducts national surveys on various socio-economic issues to collect data for policy formulation, planning, and research.  NSSO conducts regular surveys on topics such as employment, consumption, health, education, and more. These surveys provide valuable insights into the living conditions and economic activities of the population. SARVEKSHANA  Sarvekshana is a quarterly journal published by NSSO (National Sample Survey Organization). It likely serves as a platform for disseminating research findings, survey results, and analytical studies related to socio-economic issues.  The National Sample Survey office (NSSO) merged with the Central statistical office (CSO) to form the National statistical office, (NSO). On 23rd may 2019, the government of India has approved the merger of NSSO and CSO.  NSO continues the roles and responsibilities of NSSO and CSO, playing a key role in providing statistical data for evidence-based policymaking and research. 24 Statistics for Economics Part-A PW  EXERCISE MULTIPLE CHOICE QUESTIONS 6. Which of the following methods is used when an investigator collects the required 1. Data which is collected for the first time from the source of origin is known as: information from the witness? (a) Primary data (a) Direct Personal Investigation (b) Secondary data (b) Indirect Oral Investigation (c) Published data (c) Mailing (Questionnaire) Surveys (d) None of these (d) All of these 2. Which of the following is a merit of a good 7. Which of the following methods is used for questionnaire? the estimation of population in a country? (a) Simple questions (a) Census method (b) Less number of questions (b) Sampling method (c) valid and logical questions and in a (c) Both (a) and (b) proper order (d) None of these (d) All of above 8. Under random sampling, each and every item 3. Schedules are filled by the: of the universe gets (a) Enumerator (a) equal chance of being selected (b) Investigator (b) unequal chance of being selected. (c) Respondent (c) No chance of being selected. (d) None of these (d) none of these 4. Questionnaires are filed by: 9. Under which method of sampling, population (a) Enumerator is divided into different groups on the basis (b) Investigator of their diverse characteristics: (c) Respondent (a) Purposive sampling method (d) None of these (b) Stratified sampling method 5. Which one of the following is a source of (c) Quota sampling method secondary data: (d) Both (b) and (c) (a) Government and Semi -Government Publications 10. Under which method of sampling, lottery (b) Publications of trade associations method is used: (c) International publications (a) Purposive (b) Convenience (d) All of above (c) Random (d) quota Collection of Data 25 MATCH THE FOLLOWING: 1. From the set of statements given in Column I and Column II, choose the correct pair of statements Column I Column II A. Census method (i) Time saving investigation B. Sample method (ii) each and every item gets equal chance of being selected C. Stratified sampling (iii) mixture of purposive and convenience sampling D. Systematic sampling (iv) item of population is numerically, geographically and alphabetically arranged. Alternatives: (a) A-(i) (b) B-(ii) (c) C-(iii) (d) D-(iv) 2. Identify the correct sequence of alternatives given in Column II by matching them with respective items in Column I: Column I Column II A. Secondary data (i) Data is collected by the enumerators from the respondents B. Mailing (Questionnaire) (ii) Data is collected from the witness Surveys C. Indirect Oral (iii) Collected by other individual or organization Investigation method D. Enumerator method (iv) Questionnaires are mailed to the respondents Alternatives: (a) A-(iv), B-(iii), C-(i), D-(ii) (b) A-(iii), B-(iv), C-(ii), D-(i) (c) A-(i), B-(iii), C-(iv), D-(ii) (d) A-(iv), B-(ii), C-(i), D-(iii) ASSERTION AND REASONING: 1. Assertion (A): Secondary data are less expensive Alternatives: (a) Both Assertion (A) and Reason (R) are Reason (R): Secondary data are simply true and Reason (R) is the correct collected from published or unpublished explanation of Assertion (A) sources. (b) Both Assertion (A) and Reason (R) are true and Reason (R) is not the correct 2. Assertion (A): Results based on census explanation of Assertion (A) method are accurate and highly reliable. (c) Assertion (A) is true but Reason (R) is Reason (R): This is because each and every false item of the population is studied. (d) Assertion (A) is false but Reason (R) is true 26 Statistics for Economics Part-A PW STATEMENT BASED QUESTIONS 7. What is questionnaire? What is the difference between questionnaire and schedule? Alternatives: (a) Both the statements are true 8. Write 5 qualities of a good questionnaire. (b) Both the statements are false 9. What are the main sources of secondary data? (c) Statement 1 is true and Statement 2 is false 10. What precautions are necessary to use (d) Statement 2 is true and Statement 1 is secondary data? false 11. “Census of India provide statistical 1. Statement 1 : Information collected through information on various aspects of the census method is extensive and more demographic changes in India.” Explain. meaningful because all the items of a universe 12. Explain: Census of India and NSSO are examined. Statement 2 : Sample method of investigation 13. What is census method? Write its suitability, is economical in terms of time, money and merits and demerits. efforts 14. What is sample method? Write its suitability, 2. Statement 1 : Census method requires few merits and demerits. manpower (enumerators) for doing 15. What are the difference between census investigation. method and sampling method? Statement 2 : If the selected sample is wrong 16. Explain the essentials of sampling. and does not represent the features of entire population, then study may end up with 17. What are the main methods of sampling? wrong conclusions. 18. Explain random sampling. Write its merits 3. Statement 1 : Mailing methods is economical and demerits. in terms of time, money and efforts 19. Difference between Random sampling and Statement 2 : Under Information from Local haphazard sampling. Sources or Correspondents, information is to be used by journals, magazines, radio, TV, etc. 20. Difference between stratified sampling and quota sampling. ANSWER THE TYPE OF QUESTIONS 21. Why stratified sampling is known as mixed 1. Define primary and secondary data. Give one sampling ? example of each. 22. Explain 3 merits and demerits of stratified 2. Differentiate between primary and secondary sampling data. 23. What is Purposive sampling? Write its 2 3. What are the main methods of collecting merits and demerits. primary data? 4. Explain direct personal investigation? Write 24. Distinguish between random sampling and its 3 merits and demerits. Stratified sampling. Give suitable examples. 5. Explain indirect oral investigation? Write its 25. Compare the census and sample methods of 3 merits and demerits. collecting data with reference to reliability, time involved and cost. 6. Explain enumerators method ? Write its 3 merits and demerits. 26. What are Non-sampling Errors? Explain. Collection of Data 27  Answers MULTIPLE CHOICE QUESTIONS 1. (a) 2. (d) 3. (a) 4. (c) 5. (d) 6. (b) 7. (a) 8. (a) 9. (d) 10. (c) MATCH THE FOLLOWING 1. (d) 2. (b) ASSERTION AND REASONING 1. (a) 2. (a) STATEMENT BASED QUESTIONS 1. (a) 2. (d) 3. (a) CHAPTER SUMMARY & GLOSSARY ‰ Planning Statistical Data Collection: ‰ Difference Between Schedule and  Define objectives and scope. Questionnaire:  Identify information sources.  Responsibility, medium of information,  Determine timing and type of and scope of investigation. investigation. ‰ Qualities of a Good Questionnaire:  Choose appropriate statistical tools.  Brief, clear, objective, familiar to  Consider the desired accuracy level. respondent, unbiased, logical sequence, clear instructions, tested through pilot ‰ Sources of Data: survey.  Internal and external sources. ‰ Collection of Secondary Data:  External sources further categorized into  Published (e.g., government reports, primary and secondary data. international publications) and ‰ Primary vs. Secondary Data: unpublished sources.  Primary data: firsthand, from original ‰ Reliability of Secondary Data: sources.  Assess reliability, suitability, and  Secondary data: derived from existing adequacy for the intended purpose. sources. ‰ Key Sources of Secondary Data in India: ‰ Methods for Collecting Primary Data:  Census of India: Records demographic  Direct observation, oral investigation, data since 1951. telephone interviews, local  National Sample Survey Organisation correspondents, mailed or enumerator- filled questionnaires. (NSSO): Conducts nationwide socio- economic surveys. ‰ Selection of Method:  Based on nature, objectives, financial ‰ Census vs. Sample Survey: resources, accuracy needs, and time  Census method: Examines all units in the constraints. investigation. 28 Statistics for Economics Part-A PW   Sample survey: Analyzes representative  Homogeneity. units from the universe to draw  Adequacy. conclusions.  Similar regulating conditions. ‰ Census Survey: ‰ Sampling Methods:  Collects information about every unit in  Random. the universe related to the problem.  Purposive/Deliberate. ‰ Advantages of Census Investigation:  Mixed/Stratified random.  Higher accuracy and reliability.  Systematic random.  In-depth study.  Multi-stage area random.  Applicability.  Extensive. ‰ Disadvantages of Census Investigation:  Multi-stage.  Expensive.  Quota.  Requires more time and labor.  Convenience.  Not feasible in certain situations. ‰ Statistical Data: ‰ Advantages of Sample Investigation:  Data aids in drawing informed conclusions by offering valuable  Cost-effective. information.  Faster. ‰ Primary Data:  Wider scope.  Primary data are freshly collected and  Enhanced accuracy. original.  Detailed inquiry. ‰ Secondary Data:  Administrative convenience.  Secondary data are previously gathered  Often the only feasible method. by different sources, commonly found in ‰ Disadvantages of Sample Investigation: journals, newspapers, research papers, and official documents.  Risk of drawing incorrect conclusions. ‰ Universe:  Need for a representative sample.  Universe or population refers to an  Specialized expertise required. aggregate of items to be studied for an  Difficult to confine the study to the investigation. sample. ‰ Sample:  Challenges in sample selection.  Selected representative units studied in ‰ Essentials of Sampling: detail.  Representativeness.  Findings from the sample apply to the  Independence. entire universe it represents.  Collection of Data 29  CHAPTER 3 Organisation of Data TOPICS TO BE COVERED ‰ Introduction ‰ Classification ‰ Characteristics of Good Classification ‰ Objectives of Classification ‰ Basis of Classification ‰ Variable Vs Attribute ‰ Types of Statistical Series ‰ Types of Frequency Distribution INTRODUCTION The organisation of data is a fundamental step in the data analysis process. 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 It means classification of data into different groups on the basis of their different characteristics. CHARACTERISTICS OF GOOD CLASSIFICATION 1. Comprehensiveness Classification should cover all the items of the data. In other words, it should be so comprehensive that it classifies all items in some group or class. No data or point should be left unclassified. It ensures that the entire dataset is accounted in the classification process. 2. Clarity Clarity in classification means that there should be no confusion about where a particular item belongs. Each item should fit clearly into a specific group or class, avoiding any confusion in its placement. 3. Homogeneity Homogeneity emphasizes that items within a specific group or class should be similar to each other. This ensures that the classification is meaningful, with items sharing common characteristics within the same category.  4. Suitability Classification should be done in such a manner which suits the objective of the investigator. 5. Elasticity This concept suggests that the basis of classification should be adaptable. If the purpose of classification changes, one should be able to change the criteria or attributes used for classification accordingly. OBJECTIVES OF CLASSIFICATION 1. Simplification and Briefness Classification simplifies complex data by presenting it in a concise manner. It condenses information, making it easier to analyze and understand. 2. Utility Classification makes the data more useful as it brings out similarity between the diverse characteristics of data. 3. Distinctiveness Classification, by grouping data into classes, emphasizes the distinct characteristics of different subsets. It aids in identifying unique features within the dataset. 4. Comparability Classification facilitates the comparison of data. By grouping similar items together, it becomes easier to compare different groups and draw meaningful comparisons. 5. Scientific Arrangement Classification of data is done in a scientific and systematic manner which makes the data more reliable. 6. Attractive & effective Through the classification process, data becomes not only more organized but also more visually appealing. This attractiveness contributes to the overall effectiveness of the data presentation. BASIS OF CLASSIFICATION Different basis of classification of data are shown as: Basis of Classification 1. Geographical 2. Chronological/ 4. Quantitative or 3. Qualitative or Spatial Temporal Numerical Simple Manifold Organisation of Data 31 1. Geographical / Spatial Classification: Under this method of classification, the data is classified on the basis of location/place. The place may be a village, town, state, country etc. Table: Number of Firms producing Bags (2022-2023) E.g. Place No. of Firms (Producing Bags) Haryana 20 Punjab 15 UP 10 2. Chronological/Temporal Classification: Under this method of classification, data is classified on the basis of time. E.g. Year Sales (`) 2020 60 Lakh 2021 75 Lakh 2022 90 Lakh 3. Qualitative Classification: Under this method of classification, data classified on the basis of qualities. Qualitative Classification Simple Classification Manifold Classification (i) Simple Classification: Under this classification, data is classified on the basis on one characteristic. E.g. Rich-Poor, Male-Female, Healthy-Unhealthy, Educated-Uneducated. (ii) Manifold Classification: When data are classified on the basis of more than one characteristics is called Manifold Classification or Multiple Classification. E.g. Number of Students Boys Girls Rural Urban Rural Urban 32 Statistics for Economics Part-A PW 4. Quantitative or Numerical Classification: Under this, data are classified on the basis of numbers. E.g. Table: Marks Obtained by Students of Class X Marks of Students Number of Students 0–10 2 10–20 4 20–30 6 30–40 8 VARIABLE VS ATTRIBUTE ‰ Variable: Variable are that which can be expressed in terms of numbers. For example: age, height, weight, income, marks etc. ‰ Attribute: Attribute are that which can not be expressed in terms of numbers. For example: beauty, honesty, healthy, unhealthy etc. Variable Discrete variable Continuous variable Complete Numbers like Which are in form of Fraction, 3, 4, 5, 6 point, percentage E.g. 5’1, 5’2 TYPES OF STATISTICAL SERIES Types of Statistical Series Individual Series or Simple Series Frequency Series Discrete Series Continuous Series or Frequency Array or Class Interval Series or Frequency Distribution ‰ Individual Series Individual series is a series which shows the data individually. It does not include class or frequency of the values. Organisation of Data 33  For example: A list of daily wages of 6 workers (in `) where each worker’s wage is mentioned separately. Daily wages (in `) 25 50 35 40 20 45 ‰ Frequency Series 1. Discrete Series When the values are largely repeated, then we can prepare a series which shows different values of the variable along with the number of repetitions of each value. Such a series is known as Discrete series. The example in the following table illustrates a discrete series table. Marks Frequency 10 2 20 4 30 8 40 10 50 6 60 3 2. Continuous series In continuous series, data is classified into different class intervals (such as 10-20, 20-30, 30-40) along with their corresponding frequencies. For Example: Marks obtained by students in a Class Test, where the number of students scoring in a range of marks are mentioned in front of their respective class marks range/ class interval. Marks Number of Students or Frequency 10–20 4 20–30 5 30–40 8 40–50 5 50–60 4 60–70 3 34 Statistics for Economics Part-A PW  Important terms 1. Frequency: Frequency is the number of times an item repeats in the series. 2. Class: Range of value is called class. 3. Class Limits: Extreme values of a class are limits. For E.g. 5 - 10 Lower Limit Upper Limit 4. Magnitude of Class: Difference between the upper limit and lower limit of a class. For E.g. Difference = (10 – 15) = 5 5. Mid value: Average value of upper and lowers limits. or It means adding up of the upper limit and lower limit and dividing the total by 2. Upper limit + Lower limit Midvalue = 2 l2 + l1 i.e. m= 2 where, m = mid-value, l1 = lower limit, l2 = upper limit. 6. Tally Bars: Marks Tally Bars Frequency 0–10 |||| 4 10–20 |||| 5 20–30 |||| | 6 30–40 |||| ||| 8 40–50 |||| |||| || 12 TYPES OF FREQUENCY DISTRIBUTION Five types of Frequency Distribution are as follows: Frequency Distrbution Exclusive Inclusive Open end Cumulative Mid-value Series Series Series Frequency Series Frequency Series Organisation of Data 35 1. Exclusive Series: In this series, upper limit of one class and the lower limit of another class is same. Marks Frequency 10–20 4 20–30 6 30–40 8 40–50 10 50–60 2 Total = 30 2. Inclusive Series: In this series, upper limit of one class and the lower limit of other class is not same. Always convert inclusive series into exclusive series.. Inclusive Series Exclusive Series Marks Frequency Marks Frequency 10–19 4 9.5–19.5 4 20–29 6 19.5–29.5 6 30–39 8 29.5–39.5 8 40–49 10 39.5–49.5 10 50–59 2 49.5–59.5 2 Total = 30 Total = 30 3. Open End Series: Open end series is the series in which lower limit of the first class interval and the upper limit of last class interval is missing. Always convert open end series into exclusive series. Open End Series Exclusive Series Marks Frequency Marks Frequency Below 5 1 0–5 1 5–10 3 5–10 3 10–15 4 10–15 4 15–20 6 15–20 6 20 and above 1 20–25 1 4. Cumulative Frequency Series: Cumulative Frequency Series Conversion of simple frequency Conversion of cumulative frequency series into cumulative series into simple frequency series frequency series 36 Statistics for Economics Part-A PW  (i) Conversion of simple frequency series into cummulative frequency series. Simple Frequency Series Cummulative Frequency Series Class Interval Frequency (f) 0–10 8 Class Class CF CF Interval Interval 10–20 10 Less than 10 8 More than 0 50 20–30 12 Less than 20 18 More than 10 42(50 – 8) 30–40 17 Less than 30 30 More than 20 32(42 – 10) 40–50 3 Less than 40 47 More than 30 20(32 – 12) Sf = 50 Less than 50 50 More than 40 3(20–17) More than 50 0(3 – 3) (ii) Conversion of cumulative frequency series into simple frequency series. (a) Less than cumulative frequency series into simple frequency series. Less than cumulative Simple Frequency Series frequency series Class Interval No. of students Class Interval CF F Less than 10 8 0–10 8 8 Less than 20 18 10–20 18 10 (18–8) Less than 30 30 20–30 30 12 (30 – 18) Less than 40 47 30–40 47 17 (47–30) Less than 50