Applications of statistics
Introductory Statistics discusses the definition and history of statistics. Statistics deals with quantitative or numerical data and is the scientific method of collecting, organizing, analyzing, and making decisions with quantitative data. Historically, Indian texts from the Mauryan period and Mughal period contained early forms of statistical analysis of topics like agriculture. The typical process of a statistical study involves defining objectives, identifying the population and characteristics, planning data collection, collecting and organizing data, performing statistical analysis, and drawing conclusions. Statistics is useful for simplifying complex data, quantifying uncertainty, discovering patterns to enable forecasting, and testing assumptions. Statistical techniques have various applications in fields like marketing, economics, finance, operations, human resources, information technology,
Applications of statistics
- 1. Introductory Statistics Data: Qualitative and Quantitative Statistics: Deals with quantitative data. (or Numerical data.) Definition: It is the scientific method of collection, classification, presentation , analysis and decision making of the quantitative data
- 2. History • In India, Kautilya’s ‘Arthashastra’ contains Statistics of Agriculture, metals, animal husbandry, medicine etc. during the period of Chandragupta Maurya. • Similarly Ain-e-Akbari gives account of statistics relating to population, production, land revenue during Akbar’s Rule.
- 3. Phases of statistical study A typical problem requiring use of statistical study involves the following steps: • Defining objective (s) • Specification of population and its characteristic(s) • Planning for collection of data. • Collection of data. • Compilation/Presentation of data. • Statistical Analysis • Drawing conclusions, testing assumptions or making predictions
- 4. Scope of statistics • Simplifying complex data. • Conversion of data into information and make it more useful in decision making. • Quantifies and measures uncertainty and variability and so helps in measuring risk. • Discovers past and emerging patterns in a data. It helps in forecasting. • Helps in estimation and validating assumptions
- 5. Applications of statistics in various areas Marketing: Philip Kotler and Gary Armstrong state: “ identifying customer needs and wants, determines which target markets the organizations can serve best and designs appropriate products, services and programs to serve these markets.” Statistical methods helps in forecasting sales, market share and demand for various types of industrial products
- 6. Applications of statistics Economics: formulation of economic policies, econometrics Finance: helps in value at risk, stock market-derivative Insurance: based on concept of probability Operations: inventory, SQC, six sigma method
- 7. Applications of statistics HR: performance evaluations, Feedback of training program IT: Optimization of server time, Testing software Data mining: specialized branch of combination of IT & statistics It is used in all fields of business
- 8. Some statistical techniques and their applications Technique field specific applications Binomial Distribution quality assurance sampling inspection Cluster analysis marketing planning target marketing Correlation Regression financial risk marketing cross market analysis Index Numbers Economics Wholesale and consumer price index
- 9. Some statistical techniques and their applications Normal distribution equity research finance marketing production engg project management risk management performance appraisal six sigma PERT/CPM Sampling market research consumer survey
- 10. Some statistical techniques and their applications Testing of Hypothesis Agriculture, paramedical, Pharmaceutical testing a fertilizer, testing a drug, testing of drug, clinical trial Decision theory Finance Investment and portfolio selection
- 11. Some statistical techniques and their applications Forecasting HRD Insurance Marketing manpower planning Determining premium Demand forecasting Discriminant Analysis Finance, Marketing Credit risk analysis, Customer profile
- 12. Limitations of statistics It does play very important role as discussed but it has to be implemented by well defined objectives, scientific collection of data, appropriate assumptions and analysis. Scarcity of any of the above leads to wrong conclusion