MBA / 510
Managerial Decision Making
Facilitator: René Cintrón
Syllabus & Expectations
• Syllabus ◊
• Expectations
• On-Time
• Participation
• Grades
• Communication
• Professionalism / Etiquette
• APA Style ◊
• Bloom’s Taxonomy ◊
Decision Making
• What is decision making?
• Why do we make decisions?
• Who makes decisions?
• When do we make decisions?
• How do we make decisions?
• Personal / Professional / Other •
Week 1 - Objectives
• Distinguish among what is knowable,
unknowable, and researchable
• Distinguish between secondary and primary
research
• Identify tools of data analysis
• Describe the different levels of measurement
• Explain the concepts of validity and reliability
of data
• Distinguish among sampling methods.
Research Defined
• Why Study Business Research?
• What is Research?
• What is Good Research?
• Value of Acquiring Research Skills
• Manager-Researcher Relationship
• Understanding theory: components and
connections
Understanding theory:
components and connections
• Concepts
• Constructs
• Definitions
• Variables
• Propositions
• Hypotheses
Types of Research
• Primary
• Interviews
• Questionnaires
• Observations
• Secondary
• Literature / Publications
• Other Media
• Non-human sources
Sources of Knowledge
• Empiricists attempt to describe,
explain, and make predictions through
observation
• Rationalists believe all knowledge can
be deduced from known laws or basic
truths of nature
• Authorities serve as important sources
of knowledge, but should be judged on
integrity and willingness to present a
balanced case
Thought process:
Sound Reasoning
Types of Discourse
Exposition
Deduction
Argument
Induction
Thought process:
Deductive Reasoning
Inner-city household
interviewing is especially
difficult and expensive
This survey involves
substantial inner-city
household interviewing
The interviewing in this
survey will be especially
difficult and expensive
Thought process:
Inductive Reasoning
Why didn’t sales increase during our
promotional event?
• Regional retailers did not have sufficient
stock to fill customer requests during
the promotional period
• A strike by employees prevented stock
from arriving in time for promotion to
be effective
• A hurricane closed retail outlets in the
region for 10 days during the promotion
The Scientific Method
Direct observation
Clearly defined variables
Clearly defined methods
Empirically testable
Elimination of alternatives
Statistical justification
Self-correcting process
Tools of Data Analysis
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•
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Descriptive and inferential statistics
Statistics, graphics, and ethics
Constructing a frequency distribution
Software example
Graphic presentation of a frequency
distribution
• Other graphic presentations of data
Descriptive Statistics: Methods of
organizing, summarizing, and presenting data in an
informative way.
EXAMPLE 1: A
Gallup poll found that
49% of the people in a
survey knew the name
of the first book of the
Bible. The statistic 49
describes the number
out of every 100
persons who knew the
answer.
EXAMPLE 2: According
to Consumer Reports,
General Electric washing
machine owners reported
9 problems per 100
machines during 2001.
The statistic 9 describes
the number of problems
out of every 100 machines.
Types of Statistics
Inferential Statistics: A decision,
estimate, prediction, or generalization about a
population, based on a sample.
A Population
is a Collection
of all possible
individuals,
objects, or
measurements of
interest.
A Sample is a
portion, or part,
of the population
of interest
Types of Statistics
#1
Example 2: Wine
tasters sip a few drops
of wine to make a
decision with respect
to all the wine waiting
to be released for sale.
Example 1: TV
networks constantly
monitor the
popularity of their
Example 3: The accounting
programs by hiring department of a large firm will
Nielsen and other
select a sample of the invoices to
organizations to
check for accuracy for all the
sample the
invoices of the company.
preferences of TV
viewers.
Types of Statistics
(examples of inferential statistics)
For a Qualitative
Variable
the characteristic being studied is nonnumeric.
Gender
Type of car
Eye
Color
State of
Birth
Types of
Variables
For a Qualitative
Variable
the characteristic being studied is nonnumeric.
Frequency Table
Bar Chart
Pie Chart
Previous
Freq Relative
Ownership uency Frequency
None
85
0.17
Windows
60
0.12
Macintosh
355
0.71
Total
500
1.00
In a Quantitative Variable
information is reported numerically.
Balance in your checking account
Minutes remaining in class
Number of children in a family
Types of
Variables
In a Quantitative Variable
information is reported numerically.
14
12
10
8
6
4
2
0
10
15
20
25
30
35
Hours spent studying
U.S. median age by gender
40
35
30
25
19
92
19
93
19
94
19
95
19
96
19
97
19
98
19
99
20
00
20
01
20
02
Frequency
Histograms
Stem and Leaf
Box plots
XY Scatter Charts (2
variables)
• Line Graphs
Median Age
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•
•
•
Males
Females
Line Graph Exercise
• The expenditures on
research and
development for the
Hennen Manufacturing
Company are given
• Construct a simple line
graph
• Analyze the results of
the graph
• Estimate 2004’s
expenses
Line Graph
Constructing a Frequency
Distribution
A Frequency Distribution is a
grouping of data into mutually exclusive
categories showing the number of
observations in each class.
Constructing a frequency distribution involves:
Drawing conclusions
Presenting data (graph)
Organizing data (frequency distribution)
Collecting raw data
Determining the question to be addressed
Software Commands
Graphic Presentations of Data
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•
•
•
•
Line charts ◊
Bar charts ◊
Pie charts ◊
Dot plots ◊
Skewness ◊
Levels of Measurement
There are four levels of
data
Nominal
Ordinal
Interval
Ratio
Nominal data
Gender
Nominal level
Data that is
classified into
categories and
cannot be arranged
in any particular
order.
Eye
Color
Nominal level variables must be:
Mutually exclusive
An individual, object, or
measurement is included in only
one category.
Exhaustive
Each individual, object, or
measurement must appear in one
of the categories.
Ordinal level: involves data arranged in some
order, but the differences between data values
cannot be determined or are meaningless.
During a taste test
of 4 soft drinks,
Coca Cola was
ranked number 1,
Dr. Pepper number
2, Pepsi number 3,
and Root Beer
number 4.
4
2
1
3
Interval level
Similar to the ordinal level, with the additional
property that meaningful amounts of differences
between data values can be determined. There is no
natural zero point.
Temperature on the
Fahrenheit scale.
Ratio level: the interval level with an inherent
zero starting point. Differences and ratios are
meaningful for this level of measurement.
Miles traveled by sales
representative in a month
Monthly income
of surgeons
Validity
• Content Validity
• Concurrent Validity
• Construct Validity
Reliability
• Stability
• Test-retest
Equivalence
• Parallel forms
• Internal Consistency
• Split-half
• KR20
• Cronbach’s alpha
Sampling Methods
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•
•
•
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Reasons to sample
Simple random sampling
Systematic random sampling
Stratified random sampling
Cluster sampling
Why Sample the Population?
The physical
impossibility of
checking all items in
the population.
The cost of studying
all the items in a
population.
The destructive
nature of
certain tests.
The time-consuming
aspect of contacting
the whole population.
The adequacy of
sample results
in most cases.
Probability Sampling Methods
Simple Random
Sample A sample formulated
so that each item or person in
the population has the same
chance of being included.
A probability sample is a
sample selected such
that each item or person
in the population being
studied has a known
likelihood of being
included in the sample.
Systematic Random Sampling The items
or individuals of the population are
arranged in some order. A random
starting point is selected and then every
kth member of the population is selected
for the sample.
Methods of Probability
Sampling
Stratified Random
Sampling: A
population is first
divided into
subgroups, called
strata, and a sample
is selected from each
stratum.
Cluster Sampling
Cluster Sampling: A population is first divided
into primary units then samples are selected from
the primary units.
Methods of Probability Sampling
In nonprobability
sample inclusion in
the sample is based
on the judgment of
the person selecting
the sample.
The sampling error is
the difference between
a sample statistic and
its corresponding
population parameter.
The sampling distribution of the sample mean is
a probability distribution consisting of all
possible sample means of a given sample size
selected from a population.
Next Week
• Analyze data using descriptive statistics
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Population mean (Lind Chapter 3)
Sample mean (Lind Chapter 3)
Weighted mean (Lind Chapter 3)
Median (Lind Chapter 3)
Mode (Lind Chapter 3)
Variance and standard deviation (Lind Chapter
3)
• Empirical rule (Lind Chapter 3)
Next Week
• Apply basic probability concepts to
facilitate business decision making
• What is a probability? (Lind Chapter 5)
• Approaches to assigning probabilities (Lind
Chapter 5)
• Some rules for computing probabilities (Lind
Chapter 5)
• Rules of multiplication
• Contingency tables (Lind Chapter 5)
Next Week
• Distinguish between discrete and
continuous probability distributions
• Discrete probability distributions (Lind Chapter
6)
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•
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What is a probability distribution?
Random variables
Discrete random variable
Mean, variance, and standard deviation of a
probability distribution
• Continuous probability distributions (Lind
Chapter 7)
• Continuous random variable
Next Week
• Apply the normal distribution to facilitate
business decision making
• Family of normal probability distributions (Lind
Chapter 7)
• Standard normal distribution (Lind Chapter 7)
• Empirical rule (Lind Chapter 7)
• Finding areas under the normal curve (Lind
Chapter 7)