1. Economic Reasoning Using Statistics
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

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
Office Hours: 1-3 M, 1-3 W, and by Appointment
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):
Bureau of Economic Analysis (BEA):
Environmental Protection Agency (EPA):
U.S. Census Bureau:

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 Will the sun rise tomorrow?
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?
Experience
Theories

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 Information from the Real World
Relevant and Meaningful Numbers
Calculate a Statistic that describes the Real World
Examine probability of events in the Real World

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. (
28% of American adults have left the faith in which they were raised in favor of another religion - or no religion at all. (
How did they collect the data?
How did they calculate it?
Is their interpretation of the statistic correct?

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 subjects or participants.
Animals, plants, and inanimate subjects are called experimental units.
states

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
Answer 4

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.
Carbon emissions, year, epa region, president in office

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.
cause of death, fitness level
Not age because you don’t need to know a person’s age to answer the question.

27. Are Fit People Less Likely to Die of Cancer
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
Answer 4. 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.
2009
2010
Percent
(2009)
(2010)
Male - Undergrad
8,106
8,111
44.2
44.4
Female Undergraduate
10,238
10,143
55.8
55.6
Male – Graduate
864
888
34.4
35.4
Female Graduate
1,648
1,620
65.6
64.6

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).
2009
2010
Percent
(2009)
(2010)
Male - Undergrad
8,106
8,111
44.2
44.4
Female Undergraduate
10,238
10,143
55.8
55.6
Male – Graduate
864
888
34.4
35.4
Female Graduate
1,648
1,620
65.6
64.6
To count in Excel try the Emission data using the formula =countif(range, criteria)

31. When and Where give us some nice information about the context.
Where, When, and How
When and 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

33. Where, When, and How GPA of Econ 101 classes. Class 1 – 2.56
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 table clearly 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