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 Mondays at 5pm • Quizzes (5 out of 6) • Exams • March 8th • April 26th • Cumulative Optional Final • Data Project

4. Help for this Class • READ THE BOOK • Come to class prepared and awake • READ THE BOOK • Office Hours: 1-3 M, 1-3 W, and by Appointment • READ THE BOOK • Get a tutor at the Visor Center

5. Data Project • Objective: Ask a question and try to answer it using statistics. • Step 1: DATA COLLECTION - Due Tuesday January 31st in class. • Step 2: DESCRIPTION OF DATA – Due Tuesday February 7th in class • Step 3: QUESTIONS – Due Tuesday April 4th in class • Step 4: FINAL DATA PROJECT – Due by Friday May 4th 5PM

6. 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?

7. 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/

8. 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.

9. 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?

10. What is Statistics Really About? • A statistic is a number that represents a characteristic of a population. (i.e. average, standard deviation, maximum, minimum, range) • Statistics is about variation. • All measurements are imperfect, since there is variation that we cannot see. • Statistics helps us to understand the real, imperfect world in which we live and it helps us to get closer to the unveiled truth.

11. Class Objective

12. Class Objective • The course objectives are to learn the basic ideas and tools behind statistics and probability theory, develop an understanding of statistical thinking, apply the basic statistical techniques, and accurately interpret results.

13. Questioning a Statistic • ½ of all American children will witness the breakup of a parent’s marriage. Of these, close to 1/2 will also see the breakup of a parent’s second marriage. • (Furstenberg et al, American Sociological Review �1983) • 66% of the total adult population in this country is currently overweight or obese. • (http://win.niddk.nih.gov/statistics/) • 28% of American adults have left the faith in which they were raised in favor of another religion - or no religion at all. • (http://religions.pewforum.org/reports)

14. 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

15. 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 (but funny) without their context…

16. 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.

17. 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.

18. Who (cont.) • 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.

19. 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.

20. 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

21. What and Why • Variables are characteristics recorded about each individual. • The variables should have a name that identify What has been measured. • To understand variables, you must Think about what you want to know.

22. What and Why (cont.) • A categorical (or qualitative) variable names categories and answers questions about how cases fall into those categories. • Categorical examples: sex, race, ethnicity

23. 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)

24. 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. • Question: Is fitness categorical or quantitative?

25. What and Why (cont.) • 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.

26. Identify the What 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.

27. 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

28. 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.

29. 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.

30. Counts Count (cont.) • When we focus on the amount of something, we use counts differently. For example, Amazon might track the growth in the number of teenage customers each month to forecast CD sales (the Why).

31. 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.

32. Where, When, and How • GPA of Econ 101 classes. • Class 1 – 2.56 • Class 2 – 3.34

33. 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

34. 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

35. Data Tables • The following data tableclearly shows the context of the data presented: • Notice that this data table tells us the What (column titles) and Who (row titles) for these data.

36. Why statistics is challenging? • Word problems… • Rules of statistics don’t change • Data is information • If you are struggling with a problem, always ask the W questions about the data collected. • Who • What • When • Where • Why

37. Next time… • Chapter 3 – Describing and displaying categorical data