Transcript Economic Reasoning Using Statistics

ECONOMIC REASONING USING
STATISTICS
Econ 138
Dr. Adrienne Ohler
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

THE REST OF THIS CLASS
Attendance Policy
 Cellphone Policy
 Homeworks (10 out of 12)
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Due Sundays by 11:59pm
Quizzes (5 out of 6)
 Exams
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March 7th
 April 25th
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Cumulative Optional Final
 Data Project
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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
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STATISTICS
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Statistics (the discipline) is a way of reasoning, a
collection of tools and methods, designed to help us
understand the world.
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Will the sun rise tomorrow?
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Will I have fun at the party on Friday?
THE LANGUAGE OF STATISTICS
For of literacy
 4 cows in a field
 7 cows by the road
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4 cows in a field on the left
 3 cows in a field on the right
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At a party
Average age is 18
 Average age is 22
 Average age is 75
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WHAT IS STATISTICS REALLY ABOUT?
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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.
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
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DATA PROJECT
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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 12th in class
 Step 3: QUESTIONS – Due Tuesday April
2nd in class
 Step 4: FINAL DATA PROJECT – Due by
Friday May 3rd 5PM
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COLLECT DATA
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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
EXAMPLE QUESTION
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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?
CHAPTER 2 - WHAT ARE DATA?
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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…
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THE “W’S”
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To provide context we need the W’s
 Who
 What (and in what units)
 When
 Where
 Why (if possible)
 and How
of the data.
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Note: the answers to “who” and “what” are
essential.
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WHO
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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.
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Sometimes people just refer to data values as
observations and are not clear about the Who.
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But we need to know the Who of the data so we can
learn what the data say.
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IDENTIFY THE WHO IN THE FOLLOWING
DATASET?
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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.
WHO ARE THEY STUDYING?
1.
The cause of death for 22,563 men in the study
2.
The fitness level of the 22,563 men in the study
3.
The age of each of the 22,563 men in the study
4.
The 22,563 men in the study
WHAT AND WHY
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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.
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Categorical examples: sex, race, ethnicity
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WHAT AND WHY (CONT.)
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A quantitative variable is a measured variable
(with units) that answers questions about the
quantity of what is being measured.
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Quantitative examples: income ($), height (inches),
weight (pounds)
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WHAT AND WHY (CONT.)
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Example: In a fitness evaluation, one question asked
to evaluate the statement “I consider myself
physically fit” on the following scale:
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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.
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With an ordinal variable, look at the Why of the study to
decide whether to treat it as categorical or quantitative.
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IDENTIFYING IDENTIFIERS
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Identifier variables are categorical variables with
exactly one individual in each category.
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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.
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The Why will help you decide how to treat identifier
variables.
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COUNTS COUNT
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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.
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2011
2012
Percent
(2011)
Percent
(2012)
Male - Undergrad
8,320
8,265
44.9
45.4
Female
Undergraduate
10,215
9,942
55.1
54.6
Male – Graduate
926
831
36.4
36.2
Female Graduate
1,619
1,464
63.6
63.8
ARE FIT PEOPLE LESS LIKELY TO DIE OF
CANCER? -------------WHO IS THE POPULATION OF INTEREST?
1.
2.
3.
4.
All people
All men who exercise
All men who die of cancer
All men
25%
1
25%
25%
2
3
25%
4
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.
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WHERE, WHEN, AND HOW
GPA of Econ 101 classes.
 Class 1 – 2.56
 Class 2 – 3.34
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Where – Washington State university
 When – during the fall and spring semesters
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WHERE, WHEN, AND HOW (CONT.)
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How the data are collected can make the
difference between insight and nonsense.
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Example: results from voluntary Internet surveys are
often useless
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Example: Data collection of ‘Who will win Republican
Primary?’
Survey ISU students on campus
 Run a Facebook survey
 Rasmussen Reports national telephone survey
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DATA PROJECT
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Objective: Ask a question and try to answer
it using statistics.
Step 1: DATA COLLECTION - Due
Thursday January 31st in class.
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Ask yourself the who and what questions when
collecting data.
ECONOMIC REASONING USING STATISTICS
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What is economics?
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Wealth
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The study of scarcity, incentives, and choices.
The branch of knowledge concerned with the production,
consumption, and transfer of wealth. (google)
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.
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NEXT TIME…
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Chapter 3 – Displaying Categorical Data
Slide
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