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
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Attendance Policy
Cellphone Policy
Homeworks (10 out of 12)
Quizzes (5 out of 6)
Exams (March 3 and April 21)
Cummulative Optional Final
Data Project
Help for this Class
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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
How much sleep did you get last
night?
1.
2.
3.
4.
5.
6.
<6
6
7
8
9
>9
39%
22%
11%
11%
11%
6%
Slide 1- 5
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Why is this economic reasoning?
• Economics is the study of scarcity, incentives,
and decision making.
• Your time is scarce.
• You want to get the best grade in this class
possible.
• You also want to do well in other classes.
• Decision: What is the best use your time?
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.
Class Objective
Information from
the Real World
Relevant and
Meaningful
Numbers
Calculate a Statistic
that tell us about
the Real World
Examine probability
of events in the
Real World
Slide 1- 8
Chapter 1 – The path to a statistic
Information,
Information,
Information
Numbers (Data)
Statistic
Slide 1- 9
Chapter 1 – The path to a statistic
Information,
Information,
Information
Students, GPA, Hair Color, Eye
color, weight, major, study habits,
health, number of children,
hobbies, hope & dreams, current
facebook status
Numbers (Data)
4.00, 3.23, 3.24, 2.15, 0, 1.25, 2.45, 3.15, 2.15, 2.45, 1.41, 3.54,
4.00, 3.23, 3.24, 2.15, 0, 1.25, 2.45, 3.15, 2.15, 2.45, 1.41,
3.54,4.00, 3.23, 3.24, 2.15, 0, 1.25, 2.45, 3.15, 2.15, 2.45, 1.41,
3.54,4.00, 3.23, 3.24, 2.15, 0, 1.25, 2.45, 3.15, 2.15, 2.45, 1.41,
3.54
Statistic
Slide 1- 11
3.45
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.
Slide 1- 12
Questioning a Statistic
• How did they collect the data?
• How did they calculate it?
• Is their interpretation of the statistic correct?
Statistics
A. 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)
B. 66% of the total adult population in this country
is currently overweight or obese.
– (http://win.niddk.nih.gov/statistics/)
C. 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)
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.
Slide 1- 15
In this class
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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
Chapter 2 - What Are Data?
• 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…
Slide 2- 17
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.
Slide 2- 18
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.
Slide 2- 20
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.
Slide 2- 21
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
• 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.
Slide 2- 24
What and Why (cont.)
• Some variables have units that tell how each
value has been measured and tell the scale of
the measurement.
Slide 2- 25
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
• 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)
Slide 2- 26
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:
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1 = Disagree Strongly;
2 = Disagree;
3 = Neutral;
4 = Agree;
5 = Agree Strongly.
• Question: Is fitness categorical or quantitative?
Slide 2- 27
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.
Slide 2- 28
Who is the population of interest?
1. All people
2. All men who exercise
3. All men who die of
cancer
4. All men
25%
1
25%
25%
2
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25%
4
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.
Slide 2- 33
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).
– The What is teens,
Who is months,
the units are
teenage customers.
Slide 2- 34
the
and
number of
Oregon Pts.
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72 (New Mexico)
48 (Tennessee)
69 (Portland State)
42 (Arizona State) - Conference
52 (Stanford) - Conference
43 (Wash. State) - Conference
60 (UCLA) - Conference
53 (USC) - Conference
53 (Wash.) - Conference
15 (Cal) - Conference
48 (Arizona) - Conference
37 (Oregon State) - Conference
19 (Auburn) –BCS Championship Game
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.
Slide 2- 36
Where, When, and How
• We need the Who, What, and Why to analyze
data. But, the more we know, the more we
understand.
• 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.
Slide 2- 37
Where, When, and How
• GPA of Econ 101 classes.
• Class 1 – 2.56
• Class 2 – 3.34
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
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 student evaluations
• Course Evaluations
• Ask students during office hours
• Rate My Professors
Slide 2- 40
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.
Slide 2- 41
Next time…
• Chapter 3 – Describing
and displaying
categorical data