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```					Business Statistics (BUSA 3101)
Dr. Lari H. Arjomand
lariarjomand@clayton.edu

Text Book
Economics
Lind, Marchal, and Wathen
14th Ed. 2010

Slide 1

   For Lecture Notes, Slides, Quizzes,
Projects, Syllabus, Office Hours, Exams &
Bulletin Board & Much More referee to
my website at the following URL:

Slide 2
Chapter 1
What is Statistics
I need
   Applications in Business and Economics    help!
   Data
   Data Sources
   Descriptive Statistics
   Statistical Inference
   Computers and
Statistical Analysis

Slide 3
Application Areas of Statistics

Accounting                     Management
•   Auditing                  •   Describe employees
•   Costing                   •   Quality improvement

Finance                       Marketing
•   Financial trends          •   Consumer preferences
•   Forecasting               •   Marketing mix effects

Slide 4
Applications in
   Accounting
Public accounting firms use statistical
sampling procedures when conducting
audits for their clients.

   Economics
Economists use statistical information
in making forecasts about the future of
the economy or some aspect of it.

Slide 5
Applications in
   Marketing
Electronic point-of-sale scanners at
retail checkout counters are used to
collect data for a variety of marketing
research applications.

   Production
A variety of statistical quality
control charts are used to monitor
the output of a production process.

Slide 6
Applications in
 Finance
Financial advisors use price-earnings ratios and
dividend yields to guide their investment
recommendations.

Slide 7
Why Collect Data?

   Obtain input to a research study
   Measure performance
   Assist in formulating decision alternatives
   Satisfy curiosity
• Knowledge for the sake of knowledge

Slide 8
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.

Slide 9
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.

Slide 10
Data, Data Sets,
Elements, Variables, and Observations
Variables
Element
Names              Stock   Annual    Earn/
Company      Exchange Sales(\$M) Share(\$)

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

Data Set

Slide 11
Scales of Measurement

Scales of measurement include:
Nominal        Interval
Ordinal         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 12
Scales of Measurement

   Nominal
Data are labels or names used to identify an
attribute of the element.

A nonnumeric label or numeric code may be used.

Slide 13
Scales of Measurement

   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 14
Scales of Measurement

   Ordinal
The data have the properties of nominal data and
the order or rank of the data is meaningful.

A nonnumeric label or numeric code may be used.

Slide 15
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.
Alternatively, a numeric code could be used for
the class standing variable (e.g. 1 denotes
Freshman, 2 denotes Sophomore, and so on).

Slide 16
Scales of Measurement

   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 17
Scales of Measurement

   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 18
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.

Slide 19
Scales of Measurement

   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 20
Types of Data

Data

Numerical             Categorical
(Quantitative)            (Qualitative)

Discrete           Continuous

Slide 21
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.

Slide 22
Qualitative Data

Labels or names used to identify an attribute of each
element

Often 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

Slide 23
Quantitative Data

Quantitative data indicate how many or how much:

discrete, if measuring how many

continuous, if measuring how much

Quantitative data are always numeric.

Ordinary arithmetic operations are meaningful for
quantitative data.

Slide 24
Scales of Measurement

Data

Qualitative                Quantitative

Numerical            Non-numerical       Numerical

Nominal   Ordinal     Nominal   Ordinal   Interval   Ratio

Slide 25
Cross-Sectional 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 2003 in each of the counties
of Ohio

Slide 26
Time Series Data

Time series data are collected over several time
periods.

Example: data detailing the number of building
permits issued in Lucas County, Ohio in each of
the last 36 months

Slide 27
Data Sources
Data
Sources

Primary                   Secondary

Experiment   Survey      Observation    Published
(& On-Line)

Slide 28
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
Internet – more and more firms

Slide 29
Data Sources (Continued)

   Statistical Studies

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 (non-experimental) studies no
attempt is made to control or influence the
variables of interest.
a survey is a
good example

Slide 30
Data Acquisition Considerations

Time Requirement
•   Searching for information can be time consuming.
•   Information may 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

Slide 31
What Is Statistics?
   Collecting data
• e.g., Survey
   Presenting data                              Why?
• e.g., Charts & tables        Data
   Characterizing data            Analysis
• e.g., Average

Decision-
Making

Slide 32
Statistical Methods

Statistical
Methods

Descriptive                    Inferential
Statistics                     Statistics

Slide 33
Descriptive Statistics

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

Descriptive Statistics:
These are statistical
methods used to
describe data that
have been collected.

Slide 34
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 on the next
slide.

Slide 35
Example: Hudson Auto Repair

   Sample of Parts Cost for 50 Tune-ups

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

Slide 36
Tabular Summary:
Frequency and Percent Frequency

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

Slide 37
Graphical Summary: Histogram

Tune-up Parts Cost
18
16
14
12
Frequency

10
8
6
4
2
Parts
50-59 60-69 70-79 80-89 90-99 100-110 Cost (\$)

Slide 38
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 39
Inferential Statistics
   Involves
• Estimation
• Hypothesis                            Population?
testing
   Purpose
population characteristics

Inferential Statistics: These are
statistical methods used to find out
on a sample.
Slide 40
Statistical Inference

Population   - the set of all elements of interest in a
particular study
Sample - a subset of the population

Statistical inference - the process of using data obtained
from a sample to make estimates
characteristics of a population
Census - collecting data for a population

Sample survey - collecting data for a sample

Slide 41
Process of Statistical Inference

1. Population
consists of all        2. A sample of 50
tune-ups. Average         engine tune-ups
cost of parts is          is examined.
unknown.

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

Slide 42
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
 Modern spreadsheet packages are capable of data
management, analysis, and presentation.
 MS Excel is the most widely available spreadsheet

Slide 43
Statistical Analysis Using Microsoft Excel
   3 tasks might be needed:                              A
Parts
• Enter Data                                     1   Cost
• Enter Functions and Formulas                   2      91
3      71
• Apply Tools                                    4     104
5      85
6      62
7      78
8      69

D                E
Mean   =AVERAGE(A2:A71)
Median   =MEDIAN(A2:A71)
Mode   =MODE(A2:A71)
Range   =MAX(A2:A71)-MIN(A2:A71)

Slide 44
Statistical Analysis Using Microsoft Excel

   Excel Worksheet (showing data)
A               B          C            D
Parts       Labor
1         Customer         Invoice #    Cost (\$)    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 W illiams              21198          74         190

Note: Rows 10-51 are not shown.

Slide 45
Statistical Analysis Using Microsoft Excel

   Excel Formula Worksheet
C         D     E           F                   G
Parts    Labor
1   Cost (\$) 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.

Slide 46
Statistical Analysis Using Microsoft Excel

   Excel Value Worksheet
C         D     E           F                   G
Parts    Labor
1   Cost (\$) 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
Note: Columns A-B and rows 10-51 are not shown.

Slide 47
End of Chapter 1

Slide 48
Slide 48

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