Economic Reasoning Using Statistics

1. Economic Reasoning Using Statistics Econ 138 Dr. Adrienne Ohler

2. How you will learn. • Textbook: Stats: Data and Models 2nd Ed., by Richard D. DeVeaux, Paul E. Velleman, and David E. Bock • Homework: MyStatLab brought to by www.coursecompass.com

3. The rest of this class • Attendance Policy • Cellphone Policy • Homeworks (10 out of 12) • Due Sundays by 11:59pm • Quizzes (5 out of 6) • Exams • March 7th • April 25th • Cumulative Optional Final • Data Project

4. Help for this Class • READ THE BOOK • Come to class prepared and awake • READ THE BOOK • Do your homework, repeatedly • READ THE BOOK • Office Hours: T, H 11-12am and by Appointment • READ THE BOOK • Get a tutor at the Visor Center

5. Statistics • Statistics (the discipline) is a way of reasoning, a collection of tools and methods, designed to help us understand the world. • Will the sun rise tomorrow? • Will I have fun at the party on Friday?

6. The language of Statistics • For of literacy • 4 cows in a field • 7 cows by the road • 4 cows in a field on the left • 3 cows in a field on the right • At a party • Average age is 18 • Average age is 22 • Average age is 75

7. What is Statistics Really About? • Statistics helps us to understand the real, imperfect world in which we live and it helps us to get closer to the unveiled truth. • A statistic is a number that represents a characteristic of a population. (i.e. average, standard deviation, maximum, minimum, range) • Statistics is about variation.

8. In this class • Observe the real world • Create a hypothesis • Collect data • Understand and classify our data • Graph our data • Standardize our data • Apply probability rules to our data • Test our hypothesis • Interpret our results

9. Data Project • Objective: Ask a question and try to answer it using statistics. • Step 1: DATA COLLECTION - Due Thursday January 31st in class. • Step 2: DESCRIPTION OF DATA – Due Tuesday February 12thin class • Step 3: QUESTIONS – Due Tuesday April 2nd in class • Step 4: FINAL DATA PROJECT – Due by Friday May 3rd 5PM

10. Collect Data • Bureau of Labor Statistics (BLS): http://bls.gov/ • Energy Information Administration (EIA): http://www.eia.gov/ • Bureau of Economic Analysis (BEA): http://www.bea.gov/ • Environmental Protection Agency (EPA): http://epa.gov/ • U.S. Census Bureau: http://www.census.gov/ • Google Data http://www.google.com/publicdata/directory

11. Example Question • Is there a difference in carbon emission for the Midwest and the Northwest U.S.? • Is there a difference in carbon emissions for years when a Republican president is in office vs. a Democrat? • Are carbon emissions in the Midwest at ‘safe’ levels?

12. Chapter 2 - What Are Data? • Information • Data can be numbers, record names, or other labels. • Not all data represented by numbers are numerical data (e.g., 1=male, 2=female). • Data are useless without their context…

13. The “W’s” • To provide context we need the W’s • Who • What (and in what units) • When • Where • Why (if possible) • and How of the data. • Note: the answers to “who” and “what” are essential.

14. Who • The Who of the data tells us the individual cases about which (or whom) we have collected data. • Individuals who answer a survey are called respondents. • People on whom we experiment are called subjectsor participants. • Animals, plants, and inanimate subjects are called experimental units. • Sometimes people just refer to data values as observations and are not clear about the Who. • But we need to know the Who of the data so we can learn what the data say.

15. Identify the Who in the following dataset? • Are physically fit people less likely to die of cancer? • Suppose an article in a sports medicine journal reported results of a study that followed 22,563 men aged 30 to 87 for 5 years. • The physically fit men had a 57% lower risk of death from cancer than the least fit group.

16. Who are they studying? • The cause of death for 22,563 men in the study • The fitness level of the 22,563 men in the study • The age of each of the 22,563 men in the study • The 22,563 men in the study

17. What and Why • Variables are characteristics recorded about each individual. • The variables should have a name that identify What has been measured. • A categorical (or qualitative) variable names categories and answers questions about how cases fall into those categories. • Categorical examples: sex, race, ethnicity

18. What and Why (cont.) • A quantitative variable is a measured variable (with units) that answers questions about the quantity of what is being measured. • Quantitative examples: income ($), height (inches), weight (pounds)

19. What and Why (cont.) • Example: In a fitness evaluation, one question asked to evaluate the statement “I consider myself physically fit” on the following scale: • 1 = Disagree Strongly; • 2 = Disagree; • 3 = Neutral; • 4 = Agree; • 5 = Agree Strongly. • We sense an order to these ratings, but there are no natural units for the variable fitness. • Variables fitness are often called ordinal variables. • With an ordinal variable, look at the Why of the study to decide whether to treat it as categorical or quantitative.

20. Identifying Identifiers • Identifier variables are categorical variables with exactly one individual in each category. • Examples: Social Security Number, ISBN, FedEx Tracking Number • Don’t be tempted to analyze identifier variables. • Be careful not to consider all variables with one case per category, like year, as identifier variables. • The Why will help you decide how to treat identifier variables.

21. Counts Count • When we count the cases in each category of a categorical variable, the counts are not the data, but something we summarize about the data. • The category labels are the What, and • the individuals counted are the Who.

22. Are Fit People Less Likely to Die of Cancer? --------------Who is the population of interest? • All people • All men who exercise • All men who die of cancer • All men

23. Where, When, and How • Whenand Where give us some nice information about the context. • Example: Values recorded at a large public university may mean something different than similar values recorded at a small private college.

24. Where, When, and How • GPA of Econ 101 classes. • Class 1 – 2.56 • Class 2 – 3.34 • Where – Washington State university • When – during the fall and spring semesters

25. Where, When, and How (cont.) • How the data are collected can make the difference between insight and nonsense. • Example: results from voluntary Internet surveys are often useless • Example: Data collection of ‘Who will win Republican Primary?’ • Survey ISU students on campus • Run a Facebook survey • Rasmussen Reports national telephone survey

26. Data Project • Objective: Ask a question and try to answer it using statistics. • Step 1: DATA COLLECTION - Due Thursday January 31st in class. • Ask yourself the whoand what questions when collecting data.

27. Economic reasoning using statistics • What is economics? • The study of scarcity, incentives, and choices. • The branch of knowledge concerned with the production, consumption, and transfer of wealth. (google) • Wealth • The health, happiness, and fortunes of a person or group. (google) • What is/are statistics? • Statistics (the discipline) is a way of reasoning, a collection of tools and methods, designed to help us understand the world. • Statistics (plural) are particular calculations made from data. • Data are values with a context.

28. Next Time… • Chapter 3 – Displaying Categorical Data