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2. Chapter 1
Data and Statistics I need
help!
Applications in Economics
Data
Data Sources
Descriptive Statistics
Statistical Inference
Computers and
Statistical Analysis

3. Applications in Economics
Statistics: a methodology to use data to
learn the “truth.” i.e., Uncover the true
data mechanism
Probability: Branch of mathematics that
models of the truth
In economics, we estimate and test economic models
and their predictions
Use empirical models for prediction,
forecasting, and policy analysis.

4. Applications in Business
Statistical quality
control charts are used to monitor
the output of a production process.
 Production
Electronic point-of-sale scanners at
retail checkout counters are used to
collect data for a variety of marketing
research applications.
 Marketing

5. Applications in Finance
Financial advisors use statistical models
to guide their investment advice.
 Finance

6. Annual Earn/
Company Sales($M) Share($)
Data, Data Sets,
Elements, Variables, and Observations
Dataram 73.10 0.86
EnergySouth 74.00 1.67
Keystone 365.70 0.86
LandCare 111.40 0.33
Psychemedics 17.60 0.13
Variables
Data Set
Element
Names
Dataram
EnergySouth
Keystone
LandCare
Psychemedics

7. Data and Data Sets
 Data are the facts and figures collected,
summarized, analyzed, and interpreted.
 The data collected in a particular study are referred
to as the data set.

8.  The elements are the entities on which data are
collected.
 A variable is a characteristic of interest for the elements.
 The set of measurements collected for a particular
element is called an observation.
 The total number of data values in a data set is the
number of elements multiplied by the number of
variables.
Elements, Variables, and Observations

9. Scales of Measurement
Qualitative Quantitative
Numerical Numerical
Nonnumerical
Data
Nominal Ordinal Nominal Ordinal Interval Ratio

10. Scales of Measurement
The scale indicates the data summarization and
statistical analyses that are most appropriate.
The scale determines the amount of information
contained in the data.
Scales of measurement include:
Nominal
Ordinal
Interval
Ratio

11. Scales of Measurement
 Nominal
A nonnumeric label or numeric code may be used.
Data are labels or names used to identify an
attribute of the element.

12. Example:
Students of a university are classified by the
dorm that they live in using a nonnumeric label
such as Farley, Keenan, Zahm, Breen-Phillips,
and so on.
A numeric code can be used for
the school variable (e.g. 1: Farley, 2: Keenan,
3: Zahm, and so on).
Scales of Measurement
 Nominal

13. Scales of Measurement
 Ordinal
A nonnumeric label or numeric code may be used.
The data have the properties of nominal data and
the order or rank of the data is meaningful.

14. Scales of Measurement
 Ordinal
Example:
Students of a university are classified by their
class standing using a nonnumeric label such as
Freshman, Sophomore, Junior, or Senior.
A numeric code can be used for
the class standing variable (e.g. 1 denotes
Freshman, 2 denotes Sophomore, and so on).

15. Scales of Measurement
 Interval
Interval data are always numeric.
The data have the properties of ordinal data, and
the interval between observations is expressed in
terms of a fixed unit of measure.

16. Scales of Measurement
 Interval
Example: Average Starting Salary Offer 2003
Economics/Finance: $40,084
History: $32,108
Psychology: $27,454
Econ & Finance majors earn $7,976 more than
History majors and $12,630 more than
Psychology majors.
Source: National Association of Colleges and Employers

17. Scales of Measurement
 Ratio
The data have all the properties of interval data
and the ratio of two values is meaningful.
Variables such as distance, height, weight, and time
use the ratio scale.
This scale must contain a zero value that indicates
that nothing exists for the variable at the zero point.

18. Scales of Measurement
 Ratio
Example:
Econ & Finance majors salaries are 1.24 times
History major salaries and are 1.46 times
Psychology major salaries

19. Data can be qualitative or quantitative.
The appropriate statistical analysis depends
on whether the data for the variable are qualitative
or quantitative.
There are more options for statistical
analysis when the data are quantitative.
Qualitative and Quantitative Data

20. Qualitative Data
Labels or names used to identify an attribute of each
element. E.g., Black or white, male or female.
Referred to as categorical data
Use either the nominal or ordinal scale of
measurement
Can be either numeric or nonnumeric
Appropriate statistical analyses are rather limited

21. Quantitative Data
Quantitative data indicate how many or how much:
Discrete, if measuring how many. E.g., number
of 6-packs consumed at tail-gate party
Continuous, if measuring how much. E.g., pounds
of hamburger consumed at tail-gate party
Quantitative data are always numeric.
Ordinary arithmetic operations are meaningful for
quantitative data.

22. Cross-Sectional Data
Cross-sectional data observations across individuals
at the same point in time.
Example: the growth rate from 1960 to 2004 of
each country in the world (about 182 of them).
Example: wages for head of household in
Indiana

23. Time Series Data
Time series data are collected over several time
periods.
Example: the sequence of U.S. GDP growth each
Year from 1960 to 2005
Example: the sequence of Professor Mark’s wage
each year from 1983 to 2005.

24. Data Sources
 Existing Sources
Within a firm – almost any department
Business database services – Dow Jones & Co.
Government agencies - U.S. Department of Labor
Industry associations – Travel Industry Association
of America
Special-interest organizations – Graduate Management
Admission Council
Collect your own

25.  Statistical Studies
Data Sources
In experimental studies variables of interest
are identified. Then additional factors are
varied to obtain data that tells us how
those factors influence the variables.
In observational (nonexperimental) studies we
cannot control or influence the
variables of interest.
a survey is a
good example

26. Descriptive Statistics
 Descriptive statistics are the tabular, graphical,
and numerical methods used to summarize data.

27. Example: Hudson Auto Repair
The manager of Hudson Auto
would like to understand the cost
of parts used in the engine
tune-ups performed in the
shop. She examines 50
customer invoices for tune-ups. The costs of parts,
rounded to the nearest dollar, are listed on the next
slide.

28. 91 78 93 57 75 52 99 80 97 62
71 69 72 89 66 75 79 75 72 76
104 74 62 68 97 105 77 65 80 109
85 97 88 68 83 68 71 69 67 74
62 82 98 101 79 105 79 69 62 73
Example: Hudson Auto Repair
 Sample of Parts Cost for 50 Tune-ups

29. Tabular Summary:
Frequency and Percent Frequency
50-59
60-69
70-79
80-89
90-99
100-109
2
13
16
7
7
5
50
4
26
32
14
14
10
100
(2/50)100
Parts
Cost ($)
Parts
Frequency
Percent
Frequency

30. Graphical Summary: Histogram
2
4
6
8
10
12
14
16
18
Parts
Cost ($)
Frequency
50-59 60-69 70-79 80-89 90-99 100-110
Tune-up Parts Cost

31. Numerical Descriptive Statistics
 Hudson’s average cost of parts, based on the 50
tune-ups studied, is $79 (found by summing the
50 cost values and then dividing by 50).
 The most common numerical descriptive statistic
is the average (or sample mean).

32. Statistical Inference
Population
Sample
Statistical inference
Census
Sample survey
- the set of all elements of interest in a
particular study
- a subset of the population
- the process of using data obtained
from a sample to make estimates
and test hypotheses about the
characteristics of a population
- collecting data for a population
- collecting data for a sample

33. Process of Statistical Inference
1. Population
consists of all
tune-ups. Average
cost of parts is
unknown.
2. A sample of 50
engine tune-ups
is examined.
3. The sample data
provide a sample
average parts cost
of $79 per tune-up.
4. The sample average
is used to estimate the
population average.