Lecture-1 Introduction to statistics.ppt
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This document outlines the syllabus for a Business Statistics course taken during the third semester of a Bachelor of Commerce program. The syllabus covers six units: descriptive statistics, probability and probability distributions, correlation and regression analysis, index numbers, time series analysis, and sampling concepts. It also provides an overview of the introductory chapter topics for the course, which include an introduction to why managers need statistics, the growth of modern statistics, key definitions, the differences between descriptive and inferential statistics, why data is needed, and types and sources of data.
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Lecture-1 Introduction to statistics.ppt
- 1. Bachelor of Commerce (Hons) Semester - III (July-December)
- 3. Syllabus Unit 1: Statistical Data and Descriptive Statistics Unit 2: Probability and Probability Distributions Unit 3: Simple Correlation and Regression Analysis Unit 4: Index Numbers Unit 5: Time Series Analysis Unit 6: Sampling Concepts, Sampling Distributions and Estimation
- 4. Basic Business Statistics Introduction and Data Collection
- 5. Chapter Topics Why a manager needs to know about statistics The growth and development of modern statistics Key definitions Descriptive versus inferential statistics Why data are needed Types of data and their sources
- 6. Why a Manager Needs to Know about Statistics To know how to properly present information To know how to draw conclusions about populations based on sample information To know how to improve processes To know how to obtain reliable forecasts
- 7. The Growth and Development of Modern Statistics Needs of government to collect data on its citizens The development of the mathematics of probability theory The advent of the computer
- 8. Key Definitions A population (universe) is the collection of things under consideration A sample is a portion of the population selected for analysis A parameter is a summary measure computed to describe a characteristic of the population A statistic is a summary measure computed to describe a characteristic of the sample
- 9. Population and Sample Population Sample Use parameters to summarize features Use statistics to summarize features Inference on the population from the sample
- 10. Statistical Methods Descriptive statistics Collecting and describing data Inferential statistics Drawing conclusions and/or making decisions concerning a population based only on sample data
- 11. Descriptive Statistics Collect data e.g. Survey Present data e.g. Tables and graphs Characterize data e.g. Sample mean = i X n
- 12. Inferential Statistics Estimation e.g.: Estimate the population mean weight using the sample mean weight Hypothesis testing e.g.: Test the claim that the population mean weight is 120 pounds Drawing conclusions and/or making decisions concerning a population based on sample results.
- 13. Why We Need Data To provide input to survey To provide input to study To measure performance of service or production process To evaluate conformance to standards To assist in formulating alternative courses of action To satisfy curiosity
- 14. Data Sources Primary Data Collection Secondary Data Compilation Observation Experimentation Survey Print or Electronic
- 15. Discrete Data result when the number of possible values is either a finite number or a ‘countable’ number of possible values. 0, 1, 2, 3, . . . Example: The number of eggs that hens lay per day. Definitions
- 16. Continuous (Numerical) data result from infinitely many possible values that correspond to some continuous scale that covers a range of values without gaps, interruptions, or jumps. Definitions 2 3 Example: The amount of height of a students increases e.g. 0.00032 meter per day.
- 17. Univariate, Bivariate and Multivariate Data Univariate Data: When the data set involves only one variable. Example: Marks of candidates in an entire examination. Bivariate Data: The data set which involve information on two characteristics for each subjects. Example: Unit price and quantity demanded of a commodity. Multivariate Data: When three or more variables are involved , the data are termed as multivariate. Example: Salary, number of years of experience, performance and gender of the employees in an Institution.
- 18. Business Statistics Business Steps: Planning Setting Goal & Standard Developing Strategies Implementing various policies. Data gives Trends and Patterns Data Statistics Meaningful Information
- 19. Application of Statistics Production Accounting Finance Marketing Insurance Banking Economics
- 20. Thank You for your attention! Good Luck!