Managerial Decision MakingFacilitator: René Cintrón

MBA / 510

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

Exposition Argument

InductionDeduction

Types of Discourse

Inner-city household interviewing is especially difficult and expensive

Inner-city household interviewing is especially difficult and expensive

This survey involves substantial inner-city

household interviewing

This survey involves substantial inner-city

household interviewing

The interviewing in this survey will be especially difficult and expensive

The interviewing in this survey will be especially difficult and expensive

Thought process: Deductive Reasoning

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 observationDirect observation

Clearly defined variablesClearly defined variables

Clearly defined methodsClearly defined methods

Empirically testableEmpirically testable

Elimination of alternativesElimination of alternatives

Statistical justificationStatistical justification

Self-correcting processSelf-correcting process

Tools of Data Analysis

• 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

Types of Statistics

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.

Descriptive StatisticsDescriptive 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.

Types of Statistics

A PopulationPopulation is a CollectionCollection of all possible individuals, objects, or measurements of interest.

A SampleSample is a portion, or part, of the population of interest

Inferential StatisticsInferential Statistics:: A decision, estimate, prediction, or generalization about a population, based on a sample.

Types of Statistics(examples of inferential statistics)

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 programs by hiring Nielsen and other organizations to sample the preferences of TV viewers.

Example 3: The accounting department of a large firm will select a sample of the invoices to check for accuracy for all the invoices of the company.

# 1

Types of Variables

G ender E yeC olor

For a Qualitative VariableQualitative Variable

the characteristic being studied is nonnumeric.

T ype of car

State of B irth

For a Qualitative VariableQualitative Variable

the characteristic being studied is nonnumeric.

Previous Ownership

Frequency

Relative Frequency

None 85 0.17

Windows 60 0.12

Macintosh 355 0.71

Total 500 1.00

Pie Chart

Frequency Table

Bar Chart

Number of children in a family

In a Quantitative VariableQuantitative Variable information is reported numerically.

Balance in your checking account

Minutes remaining in class

Types of Variables

• Histograms • Stem and Leaf• Box plots• XY Scatter Charts (2

variables)• Line Graphs

0

2

4

6

8

10

12

14

10 15 20 25 30 35

Hours spent studying

Fre

quen

cy

In a Quantitative VariableQuantitative Variable information is reported numerically.

U.S. median age by gender

25

30

35

40

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

Med

ian

Age

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

A Frequency DistributionFrequency Distribution is a grouping of data into mutually exclusive

categories showing the number of observations in each class.

Constructing a Frequency Distribution

Determining the question to be addressed

Constructing a frequency distribution involves:

Collecting raw data

Organizing data (frequency distribution)

Presenting data (graph)

Drawing conclusions

Software Commands

Graphic Presentations of Data

• Line charts ◊• Bar charts ◊• Pie charts ◊• Dot plots ◊• Skewness ◊

There are four levels of data

Nominal Nominal OrdinalOrdinalIntervalInterval

RatioRatio

Levels of Measurement

Nominal levelNominal level Data that is classified into categories and cannot be arranged in any particular order.

G ender

E yeC olor

Nominal data

Mutually exclusiveMutually exclusive

An individual, object, or measurement is included in only one category.

Nominal level variables must be:

ExhaustiveExhaustive Each individual, object, or measurement must appear in one of the categories.

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.

Ordinal levelOrdinal level: involves data arranged in some order, but the differences between data values cannot be determined or are meaningless.

1

2

3

4

Temperature on the Fahrenheit scale.

Interval levelInterval 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.

M onthly incomeof surgeons

M iles trav eled by salesrepresentativ e in a month

Ratio level:Ratio level: the interval level with an inherent zero starting point. Differences and ratios are meaningful for this level of measurement.

Validity

• Content Validity

• Concurrent Validity

• Construct Validity

Reliability

• Stability• Test-retest

Equivalence• Parallel forms

• Internal Consistency• Split-half• KR20• Cronbach’s alpha

Sampling Methods

• Reasons to sample • Simple random sampling • Systematic random sampling • Stratified random sampling• Cluster sampling

Why Sample the Population?

The destructive nature of certain tests.

The physical impossibility of checking all items in the population.

The cost of studying all the items in a

population.The adequacy of sample results in most cases.

The time-consuming aspect of contacting the whole population.

Probability Sampling Methods

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.

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.

Stratified Random Sampling: A population is first divided into subgroups, called strata, and a sample is selected from each stratum.

Methods of Probability Sampling

Cluster Sampling

Cluster Sampling: A population is first divided into primary units then samples are selected from the primary units.

Methods of Probability Sampling

The sampling error is the difference between a sample statistic and its corresponding population parameter.

In nonprobability sample inclusion in the sample is based on the judgment of the person selecting the sample.

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• 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)• 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)