Chapter 1
Data and Statistics
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Applications in Business and Economics
Data
Data Sources
Descriptive Statistics
Statistical Inference
Slide 1
Applications in
Business and Economics
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Accounting
Public accounting firms use statistical sampling
procedures when conducting audits for their clients.
Finance
Financial analysts use a variety of statistical
information, including price-earnings ratios and
dividend yields, to guide their investment
recommendations.
Marketing
Electronic point-of-sale scanners at retail checkout
counters are being used to collect data for a variety of
marketing research applications.
Slide 2
Applications in
Business and Economics
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Production
A variety of statistical quality control charts are used
to monitor the output of a production process.
Economics
Economists use statistical information in making
forecasts about the future of the economy or some
aspect of it.
Slide 3
Data
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Elements, Variables, and Observations
Scales of Measurement
Qualitative and Quantitative Data
Cross-Sectional and Time Series Data
Slide 4
Data and Data Sets
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Data are the facts and figures that are collected,
summarized, analyzed, and interpreted. E.g.,
• IBM’s sales revenue is $100 bn.; stock price $80.
The data collected in a particular study are referred to
as the data set. E.g.,
• The sales revenue and stock price data for a
number of firms including IBM, Dell, Apple, etc.
Slide 5
Elements, Variables, and Observations
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The elements are the entities on which data are
collected. E.g.,
• IBM, Dell, Apple, etc. in the previous setting.
A variable is a characteristic of interest for the
elements. E.g.,
• Sales revenue, stock price (of a company)
The set of measurements collected for a particular
element is called an observation.
• Sales revenue, stock price for 2003
The total number of data values in a data set is the
number of elements multiplied by the number of
variables.
Slide 6
Data, Data Sets,
Elements, Variables, and Observations
Variables
Company
Dataram
EnergySouth
Keystone
LandCare
Psychemedics
Elements
Stock
Exchange
Annual Earn/
Sales($M) Sh.($)
AMEX
OTC
NYSE
NYSE
AMEX
Data Set
73.10
74.00
365.70
111.40
17.60
0.86
1.67
0.86
0.33
0.13
Datum
Slide 7
Scales of Measurement
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Scales of measurement include:
• Nominal
• Ordinal
• Interval
• Ratio
The scale determines the amount of information
contained in the data.
The scale indicates the data summarization and
statistical analyses that are most appropriate.
Slide 8
Scales of Measurement
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Nominal
• Data are labels or names used to identify an
attribute of the element.
• A nonnumeric label or a numeric code may be
used.
Slide 9
Scales of Measurement
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Nominal
• Example:
Students of a university are classified by the
school in which they are enrolled using a
nonnumeric label such as Business,
Humanities, Education, and so on.
Alternatively, a numeric code could be used for
the school variable (e.g. 1 denotes Business, 2
denotes Humanities, 3 denotes Education, and
so on).
Slide 10
Scales of Measurement
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Ordinal
• The data have the properties of nominal data and
the order or rank of the data is meaningful.
• A nonnumeric label or a numeric code may be
used.
Slide 11
Scales of Measurement
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Ordinal
• Example:
Students of a university are classified by their
class standing using a nonnumeric label such as
Freshman, Sophomore, Junior, or Senior.
Alternatively, a numeric code could be used for
the class standing variable (e.g. 1 denotes
Freshman, 2 denotes Sophomore, and so on).
Slide 12
Scales of Measurement
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Interval
• The data have the properties of ordinal data and
the interval between observations is expressed in
terms of a fixed unit of measure.
• Interval data are always numeric.
Slide 13
Scales of Measurement
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Interval
• Example:
Melissa has an SAT score of 1205, while Kevin
has an SAT score of 1090. Melissa scored 115
points more than Kevin.
Slide 14
Scales of Measurement
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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.
Slide 15
Scales of Measurement
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Ratio
• Example:
Melissa’s college record shows 36 credit hours
earned, while Kevin’s record shows 72 credit
hours earned. Kevin has twice as many credit
hours earned as Melissa.
Slide 16
Qualitative and Quantitative Data
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Data can be further classified as being qualitative or
quantitative.
The statistical analysis that is appropriate depends on
whether the data for the variable are qualitative or
quantitative.
In general, there are more alternatives for statistical
analysis when the data are quantitative.
Slide 17
Qualitative Data
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Qualitative data are labels or names used to identify
an attribute of each element.
• use either the nominal or ordinal scale of
measurement.
Qualitative data can be either numeric or
nonnumeric.
The statistical analysis for qualitative data are rather
limited.
Slide 18
Quantitative Data
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Quantitative data indicate either how many or how
much.
• Quantitative data that measure how many are
discrete.
• Quantitative data that measure how much are
continuous because there is no separation between
the possible values for the data.
Quantitative data are always numeric.
Ordinary arithmetic operations (e.g., +, -) are
meaningful only with quantitative data.
Slide 19
Cross-Sectional and Time Series Data
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Cross-sectional data are collected at the same or
approximately the same point in time.
• Example: data detailing the number of building
permits issued in June 2000 in each of the counties
of Texas
Time series data are collected over several time
periods.
• Example: data detailing the number of building
permits issued in Travis County, Texas in each of
the last 36 months
Slide 20
Data Sources
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Existing Sources
• Data needed for a particular application might
already exist within a firm. Detailed information
is often kept on customers, suppliers, and
employees for example.
• Substantial amounts of business and economic
data are available from organizations that
specialize in collecting and maintaining data.
Slide 21
Data Sources
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Existing Sources
• Government agencies are another important
source of data.
• Data are also available from a variety of industry
associations and special-interest organizations.
Slide 22
Data Sources
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Internet
• The Internet has become an important source of
data.
• Most government agencies, like the Bureau of the
Census (www.census.gov), make their data
available through a web site.
• More and more companies are creating web sites
and providing public access to them.
• A number of companies now specialize in making
information available over the Internet.
Slide 23
Data Sources
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Statistical Studies
• Statistical studies can be classified as either
experimental or observational.
• In experimental studies the variables of interest
are first identified. Then one or more factors are
controlled so that data can be obtained about how
the factors influence the variables.
• In observational (nonexperimental) studies no
attempt is made to control or influence the
variables of interest; an example is a survey.
Slide 24
Data Acquisition Considerations
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Time Requirement
• Searching for information can be time consuming.
• Information might no longer be useful by the time
it is available.
Cost of Acquisition
• Organizations often charge for information even
when it is not their primary business activity.
Data Errors
• Using any data that happens to be available or
that were acquired with little care can lead to poor
and misleading information.
Slide 25
Descriptive Statistics
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Descriptive statistics are the tabular, graphical, and
numerical methods used to summarize data.
Slide 26
Example: Hudson Auto Repair
The manager of Hudson Auto would like to have
a better understanding of 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 below.
91
71
104
85
62
78
69
74
97
82
93
72
62
88
98
57
89
68
68
101
75
66
97
83
79
52
75
105
68
105
99
79
77
71
79
80
75
65
69
69
97
72
80
67
62
62
76
109
74
73
Slide 27
Example: Hudson Auto Repair
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Tabular Summary (Frequencies and Percent
Frequencies)
Parts
Cost ($)
50-59
60-69
70-79
80-89
90-99
100-109
Frequency
2
13
16
7
7
5
Total 50
Percent
Frequency
4
26
32
14
14
10
100
Slide 28
Example: Hudson Auto Repair
Graphical Summary (Histogram)
18
16
14
Frequency
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12
10
8
6
4
2
50
60
70
80
90
100
110
Parts
Cost ($)
Slide 29
Example: Hudson Auto Repair
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Numerical Descriptive Statistics
• The most common numerical descriptive statistic
is the average (or mean).
• 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).
Slide 30
Statistical Inference
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Statistical inference is the process of using data
obtained from a small group of elements (the sample)
to make estimates and test hypotheses about the
characteristics of a larger group of elements (the
population).
Slide 31
Example: Hudson Auto Repair
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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.
4. The value of the
sample average is used
to make an estimate of
the population average.
3. The sample data
provide a sample
average cost of
$79 per tune-up.
Slide 32
End of Chapter 1
Slide 33