1. Yandell - Econ 216 Chap 1-1 Chapter 1 Introduction and Data Collection

2. Yandell - Econ 216 Chap 1-2 Chapter Goals After completing this chapter, you should be able to: Describe key data collection and sampling methods Know key definitions:  Population vs. Sample  Primary vs. Secondary data types  Categorical vs. Numerical data  Time Series vs. Cross-Sectional data  Discrete vs. Continuous data Explain the difference between descriptive and inferential statistics Describe the difference between inductive and deductive reasoning

3. Yandell - Econ 216 Chap 1-3 What is Statistics? What Is Statistics? Statistics involves collecting, organizing, summarizing, and analyzing data and using the results for decision making

4. Yandell - Econ 216 Chap 1-4 Why study statistics? To become a better decision maker Statistics is a tool used to gather, present, analyze, and interpret data for decision making

5. Yandell - Econ 216 Chap 1-5 Why Study Statistics? Business decision making: managers in all business fields must make choices based on statistical evidence Examples: marketing surveys, accounting audits, economics forecasts, manufacturing quality control, finance trends (continued)

6. Yandell - Econ 216 Chap 1-6 Personal decision making: everyone can benefit from the ability to make informed decisions Examples: smoking and health recalls and car safety marital status or sex or age vs. car insurance rates passing offense and points scored etc.... Many everyday events and decisions involve statistical reasoning and analysis of data Recent news report: night lights and nearsightedness see file nightlight_news.htm Click here to see news article Why Study Statistics? (continued)

7. Yandell - Econ 216 Chap 1-7 Key Aspects of Decision Making Objectives These represent the goals of the decision maker - they define the problem and determine the incentives of the decision maker Alternatives Without alternatives, no choices are necessary. These define the alternative means of satisfying the objective Constraints Constraints determine the boundaries of the feasible (or allowable) set of actions by the decision maker

8. Yandell - Econ 216 Chap 1-8 Two broad categories of constraints: Environmental: factors that cannot be controlled by the decision maker Physical (resources, budget) Technical (technology) Legal (government barriers or restrictions) Behavioral (customs or traditions) Informational: results of alternative actions are uncertain Risk Uncertainty

9. Yandell - Econ 216 Chap 1-9 Completing the Decision Making Process Data collection and analysis Information is needed to support the decision making process Decision An action is chosen based on objectives, alternatives, constraints, and data

10. Yandell - Econ 216 Chap 1-10 Decision Process Flowchart Scope of this course:

11. Yandell - Econ 216 Chap 1-11 Statistical Reasoning Statistics is more than mathematical manipulation, it is a way of thinking about data Statistical logic: We use Inductive Logic rather than Deductive Logic

12. Yandell - Econ 216 Chap 1-12 Deductive reasoning (from general to specific) Example All children are short (Major premise = general statement) Tom is a child (Minor premise = specific example) Therefore...

13. Yandell - Econ 216 Chap 1-13 Example All children are short (Major premise = general statement) Tom is a child (Minor premise = specific example) Therefore Tom is short (conclusion) Deductive reasoning (from general to specific) (continued)

14. Yandell - Econ 216 Chap 1-14 Using Venn diagrams: “Children” represents an entire group (the “population”) about which a characteristic is known “Tom” is one of the group (a “sample observation” or “member” of the population) and so Tom has the group characteristic Children Tom

15. Yandell - Econ 216 Chap 1-15 Inductive reasoning (from specific cases to the general case) Example I have seen many children All of them were short Therefore...

16. Yandell - Econ 216 Chap 1-16 Example I have seen many children All of them were short Therefore, the average height of children is likely to be small Inductive reasoning (from specific cases to the general case) (continued)

17. Yandell - Econ 216 Chap 1-17 Using Venn diagrams An observed characteristic of a sample of children leads us to infer a characteristic of the entire population (even though they all have not been observed) All Children Children I have seen

18. Yandell - Econ 216 Chap 1-18 Descriptive statistics Collecting, presenting, and describing data Inferential statistics Drawing conclusions and/or making decisions concerning a population based only on sample data Tools of Business Statistics

19. Yandell - Econ 216 Chap 1-19 Descriptive Statistics Collect data e.g. Survey, Observation, Experiments Present data e.g. Charts and graphs Characterize data e.g. Sample mean =

20. Yandell - Econ 216 Chap 1-20 Data Sources Primary Data Collection Secondary Data Compilation Observation Experimentation Survey Print or Electronic

21. Yandell - Econ 216 Chap 1-21 A Population is the set of all items or individuals of interest Examples: All likely voters in the next election All parts produced today All sales receipts for November A Sample is a subset of the population Examples:1000 voters selected at random for interview A few parts selected for destructive testing Every 100 th receipt selected for audit Populations and Samples

22. Yandell - Econ 216 Chap 1-22 Population vs. Sample a b c d ef gh i jk l m n o p q rs t u v w x y z PopulationSample b c g i n o r u y

23. Yandell - Econ 216 Chap 1-23 Making statements about a population by examining sample results Sample statistics Population parameters (known) Inference (unknown, but can be estimated from sample evidence) Inferential Statistics

24. Yandell - Econ 216 Chap 1-24 Inferential Statistics Estimation e.g.: Estimate the population mean weight using the sample mean weight Hypothesis Testing e.g.: Use sample evidence to test the claim that the population mean weight is 120 pounds Drawing conclusions and/or making decisions concerning a population based on sample results.

25. Yandell - Econ 216 Chap 1-25 Description vs. Inference 1. Description a. graphical techniques for summarizing and presenting information Examples: graphs, charts, histograms, other visual displays – text chapter 2 b. numerical summary values Examples: mean, median, standard deviation, quartiles – text chapter 3

26. Yandell - Econ 216 Chap 1-26 2. Inference What can be said from sample data? At what levels of confidence? With what sources and amount of error? Description vs. Inference

27. Yandell - Econ 216 Chap 1-27 Semester Road Map A useful summary chart (next page) It might be helpful to think of the course material in 3 main blocks

28. Yandell - Econ 216 Chap 1-28 Semester Road Map Introduction (Chapter 1) Basic Probability & Discrete Probability Distributions (Chapters 4-5) The Simple Linear Regression Model and Correlation (Chapter 13) Collecting, Organizing, and Visualizing Data (Chapter 2) Decision Making (Online Chapter 19) Multiple Regression and Modeling (Chapters 14-15) Time Series Forecasting (Chapter 16) Descriptive Statistics (Chapter 3) The Normal Distribution and Sampling Distributions (Chapters 6-7) Confidence Interval Estimation (Chapter 8) Hypothesis Testing (Chapters 9-10) Presenting and Describing Information Drawing Conclusions about Populations Based on Sample Information Obtaining Reliable Forecasts of Variables of Interest Block 1 Block 2 Block 3

29. Yandell - Econ 216 Chap 1-29 Chapter Summary Reviewed key data collection methods Introduced key definitions:  Population vs. Sample  Primary vs. Secondary data types  Attribute vs. Variable data  Time Series vs. Cross-Sectional data Examined descriptive vs. inferential statistics Described different sampling techniques Reviewed types of data