Statistical Economics Class XI PDF

Class XI STATISTICAL ECONOMICS Introduction and Scope of Statistics PRASHANT KIRAD  PRASHANT KIRAD Ch:1 Introduction and Scope of Statistics Why Statistics is Included in Economics? Statistics is an essential part of ec...

Class XI STATISTICAL ECONOMICS Introduction and Scope of Statistics PRASHANT KIRAD  PRASHANT KIRAD Ch:1 Introduction and Scope of Statistics Why Statistics is Included in Economics? Statistics is an essential part of economics because it provides the tools and techniques to quantify and analyze economic activities and behaviors. The inclusion of statistics in economics can be understood through various key aspects: What is Statistics? Statistics is the science of collecting, analyzing, and interpreting quantitative data. It involves the quantification of facts and findings, providing numerical information to support decision- making. 2 th & 1 Definition of Economics: 1 t h By Alfred Marshall: Economics d 1 is "the study of mankind in the i ra K ordinary business of life." t h a n By Lionel Robbins: Economics is "a science that studies human ra s behavior as a relationship between ends and scarce means which Palternative have uses." Exam mai aayega! Statistics as Numerical Data: (EMA) Definition: In its plural sense, statistics refers to information presented in numerical form, such as poverty statistics, employment statistics, population statistics, etc. Examples: Examples of statistics include data sets like unemployment rates, average income levels, and population growth rates.  PRASHANT KIRAD Numerical Information vs. Statistics: Not all numerical information qualifies as statistics. For instance, individual numerical facts without aggregation or analysis do not constitute statistics. Distinguishing Data from Statistics: Example of Non-Statistics: "Ayesha has $500 left in her wallet at the end of the month." This is simply a piece of numerical information and not a statistic since it does not represent an aggregate or an average. Example of Statistics: "The average amount left in an individual's wallet at the end of the month is $500." This is a 2 th statistic as it provides an aggregate measure that can be analyzed. & 1 1 th Definitions of Statistics in Plural d 1 Sense: By Bowley: ira t K numerical statements of facts. h a n Definition: Statistics are Context: s These facts pertain to any department of inquiry. Pra The facts are placed in relation to each other to Relationship: provide meaningful insights. By Kendall and Yule: Definition: Statistics are quantitative data. Characteristics: These data are significantly influenced by multiple causes. Complexity: The data reflect the complexity of various factors impacting the subject of inquiry. JOSH METER?  PRASHANT KIRAD Features of Statistics in Plural Sense: 1. Aggregate of Facts: Description: Single or random numbers do not constitute statistics, as no meaningful conclusion can be drawn from them. Significance: Statistics are the aggregates of facts, providing a basis for drawing conclusions and making analyses. 2. Numerically Expressed: Requirement: Statistics must be expressed in terms of numbers. Exclusion: Qualitative descriptions such as small, big, poor, or rich do not qualify as statistics. 3. Multiplicity of Causes: 2th 1 Influence: Statistics are affected by multiple factors. & 1th Complexity: The presence of multiple influencing factors 1 ensures that the data remain meaningful even if one factor is d removed. ira t K 4. Reasonably Accurate: an Precision: Statistics should be reasonably accurate to be h s Pra reliable. Trustworthiness: Accuracy is essential for making informed decisions based on statistical data. 5. Mutually Related and Comparable: Interrelation: Statistical data must be related and comparable. Utility: This relationship and comparability enhance the utility of the data for analysis and interpretation. 6. Pre-determined Objective: Purpose: Statistics are collected with a specific, predetermined purpose. Objective: Any information collected without an objective is merely numerical data and not considered statistics.  PRASHANT KIRAD Statistics as a Singular Noun:Science of Statistical Methods Definition: Statistics, in its singular sense, refers to the science of statistical methods. It encompasses the techniques and methodologies used for handling quantitative data. 1. Collection: Description: Systematic gathering of quantitative data through various methods such as surveys, experiments, and observations. 2. Organization: Description: Structuring and arranging data into categories for better understanding and analysis. 3. Presentation: 2th 1 Description: Displaying data in a clear and understandable & format. 1th 1 Methods: Using graphs, diagrams, and tables to visually d ir represent data.a 4. Analysis: t K han Description: Examining data to identify patterns, relationships, s Pra and trends. Techniques: Calculating averages, percentages, and performing statistical tests. 5. Interpretation: Description: Drawing conclusions and making inferences from the analyzed data. Objective: Providing insights and supporting decision-making processes.  PRASHANT KIRAD Statistical tools: Standard techniques or methods used in each stage of statistical study.Example:Graphs,Tables,Pie charts are some of the statistical tools used for presentation of data. Scope of Statistics: 1. Nature of Statistics: Science: Involves systematic methods and techniques for collecting, analyzing, and interpreting data. Art: Requires skill and judgment to apply statistical methods effectively to real-world problems. th 2. Subject Matter of Statistics: 2 manner. & 1 Numerical Data: Studies data in a scientific and systematic 1th Application: Helps relate data to real-life problems for analysis d 1 and decision-making. ira K Types of Statistical Methods: t an a. Descriptive Statistics: h s Definition: Methods used for the collection, presentation, and Pra analysis of data. Scope: Includes data from each and every element of the given population. Purpose: Summarizes and describes the features of a dataset, providing a clear view of the data. b. Inferential Statistics: Definition: Methods used to draw conclusions about a population based on a sample. Scope: Involves making inferences or predictions about the entire population from a representative sample. Purpose: Helps generalize findings from a sample to the larger population.  PRASHANT KIRAD Note on Universe or Population in Stats: Definition: Refers to the aggregate of all items or units related to any subject of study. Purpose: Understanding the complete set of data points for comprehensive analysis. Limitations of Statistics: (EMA) 1. Study of Aggregate Data: Limitation: Statistics typically focuses on aggregates or groups rather than individual cases. Implication: Insights are drawn from overall patterns and may th overlook specific details or anomalies in individual data points. 2 2. Study of Numerical Data: & 1 1th Limitation: Statistics requires data to be in numerical form for analysis. d 1 ira Implication: Qualitative data that cannot be quantified is not K addressed by statistical methods. t an 3. No Study of Heterogeneous Data: h s Pra Limitation: Statistics may struggle with data that is highly heterogeneous or varied. Implication: Variability in data types or sources can complicate analysis and interpretation. 4. Results are True Only as Averages: Limitation: Statistical results often represent averages or general trends. Implication: Such results may not fully capture extremes or specific cases within the data. 5. Results Must Be Contextualized: Limitation: Statistical results cannot be interpreted in isolation without reference to context. Implication: Proper understanding requires consideration of the context in which the data was collected and analyzed.  PRASHANT KIRAD 6. Requires Expertise: Limitation: Effective use of statistical methods often requires specialized knowledge and skills. Implication: Misapplication of statistical techniques by non- experts can lead to incorrect conclusions. 7. Potential for Misuse: Limitation: Statistics can be misused or manipulated to support biased interpretations. Implication: Data can be presented in ways that mislead or distort the true findings, as statistical results are not always clear-cut. (EMA) Importance and Functions of Statistics: 1. Quantitative Expression of Economic Problems:1 2 t h Provides numerical data on issues like h & poverty and unemployment for clear analysis. 1 t d 1 Comparison: ir a 2. Inter-Sectoral and Inter-Temporal t K Description: Allows for comparisons between different sectors a n and across different h years. r a Temporal sComparison: Compares data from different years. P Comparison: Compares data from various sectors Sectoral within the same period. 3. Establishing Cause and Effect Relationships: Identifies how changes in one factor affect another. 4. Policy Formation: Aids in creating data-driven policies for better decision-making. 5. Forecasting: Predicts future trends based on past data. 6. Establishing Economic Models: Develops models like demand functions and consumption functions to understand economic behavior.  PRASHANT KIRAD Top 5 Questions Q1.Define statistics. Ans: Statistics can be defined as the collection, presentation, classification, analysis, and interpretation of quantitative data. Q2.What are the stages of statistical study? Ans: The stages of a statistical study are: Collection of data Organisation of data Presentation of data Analysis of data Interpretation of data Q3.Define statistics as a plural noun. 2 th Ans: In the plural sense, statistics is defined as& 1 the information in terms of numerical data or numbers such1 th as employment d 1expenditure, population statistics, statistics concerning public statistics, etc. ira t K Q4.What are the two a n components of the subject matter in statistics? sh P r a Ans: The two components of the subject matter in statistics are: Descriptive statistics Inferential statistics Q5.What is descriptive statistics? Ans: Descriptive statistics refers to those methods which are used for the collection, presentation as well as analysis of data. These methods relate to such estimations as a measurement of central tendencies, measurement of dispersion, measurement of correlation, etc.