ch01.ppt

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© 2003 South-Western /Thomson LearningTM
Slides Prepared by
JOHN S. LOUCKS
St. Edward’s University

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Chapter 1
Data and Statistics
 Applications in Business and Economics
 Data
 Data Sources
 Descriptive Statistics
 Statistical Inference
 Statistical Analysis
Using Microsoft Excel

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Applications in
Business and Economics
 Accounting
Public accounting firms use statistical sampling
procedures when conducting audits for their clients.
 Finance
Financial advisors 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.

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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.
Applications in
Business and Economics

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Data
 Elements, Variables, and Observations
 Scales of Measurement
 Qualitative and Quantitative Data
 Cross-Sectional and Time Series Data

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

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Elements, Variables, and Observations
 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.

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Data, Data Sets, Elements,
Variables, and Observations
Elements
Variables
Data Set Datum
Stock Annual Earn/
Company Exchange Sales($M) Sh.($)
Dataram AMEX 73.10 0.86
EnergySouth OTC 74.00 1.67
Keystone NYSE 365.70 0.86
LandCare NYSE 111.40 0.33
Psychemedics AMEX 17.60 0.13

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Scales of Measurement
 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.

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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.

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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).

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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.

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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).

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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.

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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.

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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.

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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.

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Qualitative and Quantitative Data
 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.

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Qualitative Data
 Qualitative data are labels or names used to identify
an attribute of each element.
 Qualitative data 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.

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Quantitative Data
 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 are meaningful only
with quantitative data.

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Cross-Sectional and Time Series Data
 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

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Data Sources
 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.

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Data Sources
 Existing Sources
•Government agencies are another important
source of data.
•Data are also available from a variety of industry
associations and special-interest organizations.

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Data Sources
 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.

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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.
•A survey is perhaps the most common type of
observational study.
Data Sources

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Data Acquisition Considerations
 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.

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

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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
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.

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

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 Graphical Summary (Histogram)
Parts
Cost ($)
2
4
6
8
10
12
14
16
18
Frequency
50 60 70 80 90 100 110

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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).

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Statistical Inference
 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).

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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.
3. The sample data
provide a sample
average cost of
$79 per tune-up.
4. The value of the
sample average is used
to make an estimate of
the population average.

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Statistical Analysis Using Microsoft Excel
 Statistical analysis typically involves working with
large amounts of data.
 Computer software is typically used to conduct the
analysis.
 Frequently the data that is to be analyzed resides in a
spreadsheet.
 Modern spreadsheet packages are capable of data
management, analysis, and presentation.
 MS Excel is the most widely available spreadsheet
software in business organizations.

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Statistical Analysis Using Microsoft Excel
 In using Excel for statistical analysis, 3 tasks might be
needed.
•Enter Data
•Enter Functions and Formulas
•Apply Tools

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 Data Set
Note: Rows 10-51 are not shown.
A B C D
1 Customer Invoice #
Parts
Cost ($)
Labor
Cost ($)
2 Sam Abrams 20994 91 185
3 Mary Gagnon 21003 71 205
4 Ted Dunn 21010 104 192
5 ABC Appliances 21094 85 178
6 Harry Morgan 21116 62 242
7 Sara Morehead 21155 78 148
8 Vista Travel, Inc. 21172 69 165
9 John Williams 21198 74 190

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 Formula Worksheet
C D E F G
1
Parts
Cost ($)
Labor
Cost ($)
2 91 185 Average Parts Cost =AVERAGE(C2:C51)
3 71 205
4 104 192
5 85 178
6 62 242
7 78 148
8 69 165
9 74 190
Note: Columns A-B and rows 10-51 are not shown.

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 Value Worksheet
Note: Columns A-B and rows 10-51 are not shown.
C D E F G
1
Parts
Cost ($)
Labor
Cost ($)
2 91 185 Average Parts Cost 79
3 71 205
4 104 192
5 85 178
6 62 242
7 78 148
8 69 165
9 74 190

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End of Chapter 1

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