Transcript DATA AND STATISTICS

Anderson
u
Sweeney
u
Williams
CONTEMPORARY
BUSINESS
STATISTICS
WITH MICROSOFT EXCEL
u Slides Prepared by JOHN LOUCKS u
© 2001 South-Western /Thomson Learning
Slide 1
Chapter 1
Data and Statistics






Applications in Business
and Economics
Data
Data Sources
Descriptive Statistics
Statistical Inference
Statistical Analysis using
Microsoft Excel
Slide 2
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.
Slide 3
Applications
in Business and Economics


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 4
Data



Elements, Variables, and Observations
Qualitative and Quantitative Data
Cross-Sectional and Time Series Data
Slide 5
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.
Slide 6
Elements, Variables, and Observations



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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.
Slide 7
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 8
Qualitative and Quantitative Data
The statistical analysis that is appropriate depends on
whether the data for the variable are qualitative or
quantitative.
 Qualitative data are labels or names used to identify
an attribute of each element.
 Quantitative data indicate either how much or how
many.
 Quantitative data are always numeric.
 Qualitative data can be either numeric or
nonnumeric.
 Ordinary arithmetic operations are meaningful only
with quantitative data.

Slide 9
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
Slide 10
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.
• Government agencies are another important
source of data.
• Data are also available from a variety of industry
associations and special-interest organizations.
Slide 11
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.
Slide 12
Data Sources

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.
Slide 13
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.
Slide 14
Descriptive Statistics

Descriptive statistics are the tabular, graphical, and
numerical methods used to summarize data.
Slide 15
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 16
Example: Hudson Auto Repair

Tabular Summary (Frequencies and Percent
Frequencies
Parts
Cost ($)
50-59
60-69
70-79
80-89
90-99
100-109
Frequency
2
13
16
7
7
5
Total 50
Percent
Frequency
4
26
32
14
14
10
100
Slide 17
Example: Hudson Auto Repair
Graphical Summary (Histogram)
18
16
14
Frequency

12
10
8
6
4
2
50
60
70
80
90
100
110
Parts
Cost ($)
Slide 18
Example: Hudson Auto Repair

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 19
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).
Slide 20
Example: Hudson Auto Repair

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
$79 per tune-up.
Slide 21
Using Excel for Statistical Analysis
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Statistical analysis typically involves working with
large amounts of data.
Computer software is typically used to conduct the
analysis.
Frequently the data that is to be analyzed resides in a
spreadsheet.
Modern spreadsheet packages are capable of data
management, analysis, and presentation.
MS Excel is the most widely available spreadsheet
software in business organizations.
Slide 22
Using Excel for Statistical Analysis

In using Excel for statistical analysis, 3 tasks might be
needed.
• Enter Data
• Enter Functions and Formulas
• Apply Tools
Slide 23
Using Excel for Statistical Analysis

Data Set
A
1
2
3
4
5
6
7
8
9
Customer
Sam Abrams
Mary Gagnon
Ted Dunn
ABC Appliances
Harry Morgan
Sara Morehead
Vista Travel, Inc.
John Williams
B
Invoice #
20994
21003
21010
21094
21116
21155
21172
21198
C
Parts
Cost ($)
91
71
104
85
62
78
69
74
D
Labor
Cost ($)
185
205
192
178
242
148
165
190
Note: Rows 10-51 are not shown.
Slide 24
Using Excel for Statistical Analysis

Formula Worksheet
1
2
3
4
5
6
7
8
9
C
D
E
Parts
Labor
Cost ($) Cost ($)
91
185
71
205
104
192
85
178
62
242
78
148
69
165
74
190
F
G
Average Parts Cost =AVERAGE(C2:C51)
Note: Rows 10-51 and Columns A-B are not shown.
Slide 25
Using Excel for Statistical Analysis

Value Worksheet
1
2
3
4
5
6
7
8
9
C
D
E
Parts
Labor
Cost ($) Cost ($)
91
185
71
205
104
192
85
178
62
242
78
148
69
165
74
190
F
G
Average Parts Cost
79
Note: Rows 10-51 and Columns A-B are not shown.
Slide 26
End of Chapter 1
Slide 27