CODE: 01
Operation and Information Management &
Quantitative Technics
2008-2009 Fall
Prepared By:
Saban Eren, Ph.D., Professor
CODE: 02
Objectives
The course introduces students to the variety
of methods to enable learning about
quantitative techniques in business
management where insight and problemsolving can be aided by the effective use of
quantitative analytical techniques, including
statistics.
CODE: 02
Course Outline
1.
Introduction to quantitative methods
2.
Presenting data
3.
Central tendency
4.
Dispersion of the data
5.
Probability
6.
Distribution
7.
Distribution
8.
Sampling theory
9.
Confidence intervals
10. Hypothesis theory
11. Hypothesis theory
12. Regression analysis
13. Time series
CODE: 02
References
1. Berenson M.L., Levine D.K. & Krehbiel T.C.,
“Basic Business Statistics”, 11/e, Prentice Hall,
2009.
2. Burton G., Carrol G. & Wall S., “Quantitative
Methods for Business & Economics”, Prentice
Hall, 2/e, 2002.
3. Easton & Coll, Statistics Glossary,
http://www.stats.gla.ac.uk/steps/glossary/inde
x.html
4. Kay D., “CliffsAP Statistics”, Wiley Publishing,
2005.
5. Kazmier L.J., “Business Statistics”, 4/E,
Schaum’s Outline Series McGRAW-HILL, 2004.
6. McClave J.T., Benson P.G. & Sincich T., “A
First Course in Business Statistics”, 8/e,
Prentice Hall, 2000.
7. Render B., Stair R.M. & Hanna M.E.,
“Quantitative Analysis for Management”, 8/e,
Prentice Hall, 2003.
8. Tanis E.A., “Statistics I:Descriptive Statistics
and Probability”, HJB, 1987.
9. Triola M.F., “Elementary Statistics”, 9/e,
Addison Wesley, 2005.
10. Wates D., “Quantitative Methods for Business”,
4/e, Prentice Hall, 2008.
11. Wikipedia, http://en.wikipedia.org/
CODE: 01
Operation and Information Management &
Quantitative Technics
Introduction to quantitative methods
2008-2009 Fall
Session 1
Prepared By:
Saban Eren, Ph.D., Professor
CODE: 02
Learing Objectives
In this session, you will learn about
Presenting Data and Graphical Displays.
After reading this session, you should be able
to:
1. What is quantitative analysis?
2. How statistics is used in business
3. Types of statistics
4. The vocabulary of statistics
CODE: 05
Quantitative Analysis
Input
Process
Output
Raw
Data
Quantitative
Analysis
Meaningful
Information
Render B. et al, “Quantitative Analysis for Management”, 8th ed., Prentice Hall, 2003, pp.2.
IPO (Input-Process-Output) is one of the most
fundamental design patterns.
Quantitative analysis is a scientific approach to managerial decision making
whereby raw data are processed and manipulated resulting in meaningful
information.
Quantitative analysis provides data-driven analytical services for a range of
business challenges, specializing in statistical models for site selection decisions.
Examples:

When to order additional new meterial?

What is the total annual cost?

What is the safety stock lavel?
CODE: 06
The Quantitative Analysis Approach
Defining the Problem
Developing a Model
Acquiring Input Data
Developing a Solution
Testing the Solution
Analyzing the Results
Implementing the Results
Ref: Render B. et al, “Quantitative Analysis for Management”, 8th ed., Prentice Hall, 2003, pp.3.
CODE: 04
Quantitative & Qualitative Factors
The data; may be quantitative, with values
expressed numerically may be qualitative,
with characteristics such as consumer
preferences being tabulated.
Quantitative factors might be different
investment alternatives, interest rates,
inventory levels, demand, or labor cost.
Qualitative factors such as the weather,
state and federal legislation, and technology
breakthroughs should also be considered.
Render B. et al, “Quantitative Analysis for Management”, 8th ed., Prentice Hall,
2003, pp.2
CODE: 09
Statistics
 A branch of mathematics taking and
transforming numbers into useful information
for decision makers.
 Statistics is the art of learning from data.
 Methods for processing & analyzing numbers.
 Methods for helping reduce the uncertainty
inherent in decision making.
 Statistics refers to the body of techniques
used for collecting, organizing, analyzing, and
interpreting data.
 A statistic is a quantity that is calculated from
a sample of data. It is used to give
information about unknown values in the
corresponding population.
CODE: 04
Why Learn Statistics?
So you are able to make better sense of the
ubiquitous use of numbers:
• Business memos
• Business research
• Technical reports
• Technical journals
• Newspaper articles
• Magazine articles
A Wojtek Kozak illustration.
Ref: Berenson M.L. Et al., “Basic Business
Statistics”, 11/E, Prentice Hall, 2009.
CODE: 04
Why Study Statistics?
• Present and describe business data and
information properly
• Draw conclusions about large groups of
individuals or items, using information
collected from subsets of the individuals or
items
• Make reliable forecasts about a business
activity
• Improve business processes
Ref: Berenson M.L. Et al., “Basic Business Statistics”, 11/E, Prentice
Hall, 2009.
CODE: 05
Application Areas
 Economics
Forecasting
Demographics
 Engineering
Construction
Materials
 Sports
Individual &
Team
Performance
 Business
Consumer
Preferences
Financial Trends
Quality
McClave J.T. et al., “A First Course in Business Statistics”, 8/e, Prentice Hall,2000.
Statistical analysis of quantitative data is important throughout the pure and
social sciences.
For example, during this module we will consider examples from Biology,
Medicine, Agriculture, Economics, Business and Meteorology.
CODE: 09
Business Statistics
Business statistics is the science of good
decision making in the face of uncertainty and is
used in many disciplines such as financial
analysis, econometrics, auditing, production and
operations including services improvement, and
marketing research.
Statistics is used in business to help make
better decisions by understanding the sources of
variation and by uncovering patterns and
relationships in business data.
Ref:
Kazmier L.J., “Business Statistics”, 4/E, Schaum’s Outline Series McGRAW-HILL, 2004.
Wikipedia, http://en.wikipedia.org/wiki/Business_statistics.
CODE: 05
Types of Statistics
Statistics
Descriptive
Collecting, summarizing,
and describing data
Inferential
Drawing conclusions
and/or making
decisions concerning
a population based
only on sample data
Ref: Berenson M.L. Et al., “Basic Business Statistics”, 11/E, Prentice Hall, 2009.
CODE: 04
Descriptive Statistics
Descriptive statistic can be defined as
collection, presentation, and characterization
of a set of data in order to describe properly
the various features of thatset of data.
• Collect data
90
80
70
e.g., Survey
60
50
40
30
• Present data
20
10
0
e.g., Tables and graphs
• Characterize data
e.g., Sample mean =
1st Qtr
X
i
n
Ref: Berenson M.L. Et al., “Basic Business Statistics”, 11/E, Prentice Hall,
2009
.
East
West
North
2nd Qtr
3rd Qtr
4th Qtr
CODE: 04
Inferential Statistics?
Inferential statistics can be defined as
estimation of a characteristics of a population
or the making of a decision concerning a
population based only on sample results.
• Estimation
e.g., Estimate the population mean
weight using the sample mean
weight
• Hypothesis testing
e.g., Test the claim that the
population mean weight is 120
pounds
Ref: Berenson M.L. Et al., “Basic Business Statistics”, 11/E, Prentice Hall,
2009.
CODE: 09
Data
• Data are the different values associated with a
variable.
• Data are observations (measurement,
genders, survey responses) that have been
collected.
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Types of Data
Data
Categorical
Numerical
Examples:



Marital Status
Political Party
Eye Color
(Defined categories)
Discrete
Examples:


Number of Children
Defects per hour
(Counted items)
Berenson M.L. Et al., “Basic Business Statistics”, 11/E, Prentice Hall, 2009.
Continuous
Examples:


Weight
Voltage
(Measured
characteristics)
CODE: 05
Discrete Data
Examples:
• 3 - the number of kittens in a litter
• 2 – the number of patients in a doctors surgery
• 6 - the number of flaws in one metre of cloth
• (M, F) - gender (male, female)
• (O, A, B, AB) - blood group
Easton & Coll, Statistics Glossary, http://www.stats.gla.ac.uk/steps/glossary.
A set of data is said to be discrete if the values / observations belonging to it are
distinct and separate, i.e. they can be counted (1,2,3,....).
CODE: 05
Categorical Data
Examples:
• Shoes in a cupboard can be sorted according to
colour: the characteristic 'colour' can have nonoverlapping categories 'black', 'brown', 'red' and
'other'.
• People have the characteristic of 'gender' with
categories 'male' and 'female'.
Easton & Coll, Statistics Glossary, http://www.stats.gla.ac.uk/steps/glossary.
A set of data is said to be categorical if the values or observations belonging to it
can be sorted according to category. Each value is chosen from a set of nonoverlapping categories.
CODE: 05
Nominal Data
Examples:
• In a data set males could be coded as 0, females
as 1.
• marital status of an individual could be coded as Y
if married, N if single.
Easton & Coll, Statistics Glossary, http://www.stats.gla.ac.uk/steps/glossary.
• A set of data is said to be nominal if the values / observations belonging to it
can be assigned a code in the form of a number where the numbers are simply
labels.
• You can count but not order or measure nominal data.
CODE: 05
Ordinal Data
Examples:
• suppose a group of people were asked to taste
varieties of biscuit and classify each biscuit on a
rating scale of 1 to 5, representing strongly
dislike, dislike, neutral, like, strongly like. A rating
of 5 indicates more enjoyment than a rating of 4.
Easton & Coll, Statistics Glossary, http://www.stats.gla.ac.uk/steps/glossary.
• A set of data is said to be ordinal if the values / observations belonging to it can
be ranked (put in order) or have a rating scale attached.
•You can count and order, but not measure, ordinal data.
CODE: 05
Interval Scale
Examples:
• The time interval between the starts of years 1981
and 1982 is the same as that between 1983 and
1984, namely 365 days. The zero point, year 1
AD, is arbitrary; time did not begin then.
• Other examples of interval scales include the
heights of tides, and the measurement of
longitude.
Easton & Coll, Statistics Glossary, http://www.stats.gla.ac.uk/steps/glossary.
• An interval scale is a scale of measurement where the distance between any
two adjacents units of measurement (or 'intervals') is the same but the zero point
is arbitrary.
• Scores on an interval scale can be added and subtracted but can not be
meaningfully multiplied or divided.
CODE: 09
Variable
A variable is a characteristic of an item or
individual.
Variables
Categorical
(qualitative)
variables have
values that can
only be placed
into categories,
such as “yes”
and “no.”
Numerical
(quantitative)
variables have
values that
represent
quantities.
Variables are either qualitative or quantitative. Qualitative variables have
non-numeric outcomes, with no natural ordering. For example, gender,
disease status, and type of car are all qualitative variables. Quantitative
variables have numeric outcomes. For example, survival time, height, age,
number of children, and number of faults are all quantitative variables.
CODE: 06
Quantitative variables

Quantitative variables can be discrete or continuous.

Discrete random variables have outcomes which can take only a countable
number of possible values. These possible values are usually taken to be
integers, but don’t have to be.
◦

For example, number of children and number of faults are discrete random
variables which take only integer values, but your score in a quiz where
“half” marks are awarded is a discrete quantitative random variable which
can take on non-integer values.
Continuous random variables can take any value over some continuous
scale.
◦
For example, survival time and height are continuous random variables.
Often, continuous random variables are rounded to the nearest integer,
but the are still considered to be continuous variables if there is an
underlying continuous scale. Age is a good example of this.
CODE: 09
Operational Definations
Data values are meaningless unless their
variables have operational definitions,
universally accepted meanings that are clear to
all associated with an analysis.
Ref: Berenson M.L. Et al., “Basic Business Statistics”, 11/E, Prentice Hall,
2009.
CODE: 09
Population
A population is any entire collection of people,
animals, plants or things from which we may
collect data.
It is the entire group we are interested in, which
we wish to describe or draw conclusions about.
Example
The population for a study of infant health might
be all children born in the UK in the 1980's. The
sample might be all babies born on 7th May in
any of the years.
Ref: Easton & Coll, Statistics Glossary, http://www.stats.gla.ac.uk/steps/glossary
CODE: 09
Census
A census is the collection of data from every
member of the population.
Ref: Triola M.F., “Elementary Statistics”, 9/e, Addison Wesley, 2005, p.4.
CODE: 09
Sample
A sample is a group of units selected from a
larger group (the population). By studying the
sample it is hoped to draw valid conclusions
about the larger group.
A sample is generally selected for study because
the population is too large to study in its
entirety. The sample should be representative of
the general population. This is often best
achieved by random sampling. Also, before
collecting the sample, it is important that the
researcher carefully and completely defines the
population, including a description of the
members to be included.
Example
The population for a study of infant health might
be all children born in the UK in the 1980's. The
sample might be all babies born on 7th May in
any of the years.
Ref: Easton & Coll, Statistics Glossary, http://www.stats.gla.ac.uk/steps/glossary
CODE: 04
Statistical Computer Programs
• Minitab
• SAS
• SPSS
• Microsoft Excel
Example SPSS screen shot.
CODE: 11
What is the correct word(s) to fill in the blank in the sentence?
Statistics is a branch of mathematics taking and transforming numbers into
________________ for decision makers.
a)
b)
c)
d)
e)
decision
estimation
useful information
population
qualitative factors
c) useful information
CODE: 10
Which one of the following is one of the
steps of Quantitative Analysis approach?
e) All
All choise are related to
qauantitative analysis
approach.
Lets remember:
Defining the Problem
Developing a Model
a)
b)
c)
d)
e)
Defining the problem
Developing a model
Acquiring input data
Developing a solution
All
Acquiring Input Data
Developing a Solution
Testing the Solution
Analyzing the Results
Implementing the Results
CODE: 12
Select the best match for each defination.
1) Data
A) A characteristic of an item or
individual
2) Population
B) Taking and transforming numbers
into useful information for decision
makers.
3) Sample
C) Set of observations that have been
collected
4) Variable
D) Any entire collection of people,
animals, plants or things from which
we may collect data.
5) Statistics
E) A group of units selected from a
larger group
1C, 2D, 3E, 4A, 5B
CODE: 11
Which of the following is not a statistical computer programs?
a)
b)
c)
d)
e)
Minitab
SAS
SPSS
Microsoft Word
Microsoft Excel
d) Microsoft Word
CODE: 14
Conclusion
In this session, we have:
1. Defined quantitative methods
2. Defined statictics
3. Distinguished descriptive & inferential statistics
4. Defined basic statistical terms
5. Defined levels of measurement
6. Defined types of data
References
Berenson M.L., Levine D.K. & Krehbiel T.C., “Basic Business Statistics”, 11/e, Prentice Hall,
2009.
Easton & Coll, Statistics Glossary, http://www.stats.gla.ac.uk/steps/glossary/
Kazmier L.J., “Business Statistics”, 4/E, Schaum’s Outline Series McGRAW-HILL, 2004.
McClave J.T., Benson P.G. & Sincich T., “A First Course in Business Statistics”, 8/e, Prentice
Hall, 2000.
Render B., Stair R.M. & Hanna M.E., “Quantitative Analysis for Management”, 8/e, Prentice
Hall, 2003.
Triola M.F., “Elementary Statistics”, 9/e, Addison Wesley, 2005.
Wikipedia, http://en.wikipedia.org/wiki/Business_statistics