Useful Tools in MIS
Rev: June, 2012
Euiho (David) Suh, Ph.D.
POSTECH Strategic Management of Information and Technology Laboratory
(POSMIT: http://posmit.postech.ac.kr)
Dept. of Industrial & Management Engineering
POSTECH
Contents
1
SWOT
2
BCG Matrix & GE/Mckinsey Matrix
3
Value Chain
4
P5CFM
5
BSC
6
Knowledge Map
7
Decision Tree
8
What-if Analysis
9
Delphi Method
10 ERD
11
DFD
12 Statistical Hypothesis Testing
13 Regression Analysis
14 AHP
SWOT Analysis (1/2)
■ What is SWOT analysis?
– Development of the idea of matching the organization’s internal factors with external
environmental circumstances
Environment
Criteria
Strengths
Characteristic
A firm’s resources and capabilities that can be used for developing a competitive advantage
Internal
Weaknesses
Opportunities
External
Threat
The absence of certain strengths
A combinations of events or circumstances that arise, which, if acted upon at a certain time, will
result in profit, gain, or victory
An event, as defined by its impact on your company and the probability of its occurrence, that
will result in harm to your company
■ How to use SWOT analysis?
– TOWS Matrix
• To develop strategies that take into account the SWOT profile, a matrix of these factors can be constructed
• The SWOT matrix, can be changed into what is known as the TOWS Matrix
SWOT Analysis
Strengths
Opportunities
Weaknesses
Threats
TOWS Analysis
Strengths
Weaknesses
Opportunities
S-O strategies
Pursue opportunities that fit well
the company's strengths
W-O strategies
Overcome weaknesses to pursue
opportunities
Threats
S-T strategies
Identify ways that the firm can use
its strengths to reduce its
vulnerability to external threats
W-T strategies
Make a defensive plan to prevent
the firm's weaknesses from making
it susceptible to external threats
3
SWOT Analysis (2/2)
■ Example
– SWOT Analysis for POSTECH






Supporting of the foundation
Brilliant students
Staffs of superior ability
High quality facilities for research
POVIS system
Hard-studying campus environment
 Increasing the number of students
evading science and engineering
department
 Competitive universities’ advance.
 Increasing competition in receiving
large-scale project.
Threats
 Globalization and knowledge
society
 Increasing expectation of high
quality human resource
 Increasing attention to specialized
graduate school (ex. Steel graduate
course)
Opportunities
Strengths





Hard to attract students and faculty
Lack of Globalization
Poor External Advertisement
Small scale of Alumni Association
Lack of Leadership
Weaknesses
S-O strategies
W-O strategies
 Caring system for better human source
 Advertise POSTECH through external
cooperation
 Produce high quality human resource
through a select few education.
S-T strategies
W-T strategies
 Advertise POSTECH by showing
POSTECH has better research outcomes
than other competitive universities
 Foundation of a branch school abroad
 Increasing the number of foreign
exchange students
 Provide privilege to top notch students
4
BCG Matrix & GE/Mckinsey Matrix (1/3)
■ What is BCG Matrix?
– Well-known portfolio management tool used in product life cycle theory
■ How to use BCG Matrix?
– Plot business units or products into the matrix by assessing their relative market share and
market growth values
Criteria
Characteristic
Stars
 High growth, high market share
 Use large amounts of cash and are leaders in the business so they
should also generate large amounts of cash.
Cash
Cows
 Low growth, high market share
 Profits and cash generation should be high, and because of the Low
growth, investments needed should be low. Keep profits high
Dogs
 Low growth, low market share
 Avoid and minimize the number of dogs in a company.
Question
Marks
 High growth, low market share
 Have the worst cash characteristics of all, because high demands and
low returns due to low market share
※ Limitations of BCG Matrix
–
–
–
The link between market share and profitability is questionable since increasing market share can be very
expensive
The approach may overemphasize high growth, since it ignores the potential of declining markets
The model considers market growth rate to be a given. In practice the firm may be able to grow the market
5
BCG Matrix & GE/Mckinsey Matrix (2/3)
■ What is GE/Mckinsey Matrix?
– Model to perform a business portfolio analysis on the Strategic Business units of a corporation
Competitive Strength
Low
Medium
Low
Medium
Invest aggressively
Invest selectively
Harvest or divest
High
Market
Attractiveness
High
– GE/Mckinsey matrix attempt to improve upon the BCG Matrix
• Market (Industry) attractiveness replaces market growth as the dimension of industry attractiveness
• Competitive strength replaces market share as the dimension by which the competitive position of each
SBU is assessed
• GE/McKinsey Matrix works with a 3 x 3 grid, while the BCG Matrix has only 2 x 2.
This also allows for more sophistication
※ Limitations of GE/Mckinsey Matrix
– Core competencies are not represented
– Interactions between Strategic Business Units are not considered
6
BCG Matrix & GE/Mckinsey Matrix (3/3)
■ Example
– BCG Matrix of LG Electronics
Division
Market Growth Rate
Mobile Communications (MC)
e.g. Smartphone
Relative Market Share
 Very high
 Samsung: 25.4%
 LG: 3.7%
 0.15
Home Entertainment (HE)
e.g. LED/3D TV
 High
 Samsung: 22.1%
 LG: 14.1%
 0.64
Home Appliance (HA)
e.g. Washing machine
 Low
 LG: 10.9%
 Samsung: 7.1%
 1.54
Air Conditioning (AC)
e.g. Air conditioner
 Low
 LG: 21.6%
 Samsung: 19.2%
 1.26
Market Growth
High
Relative Market Share =
LGE’s market share
Rival’s market share
MC
HE
HA
AC
Low
Bubble size: LGE’s relative sales account
10
1
Relative Market Share
7
0.1
Value Chain (1/3)
■ What is Value Chain?
– A tool for systematically examining the activities of a firm and how they interact with one
another and affect each other’s cost and performance
– A tool to gain a competitive advantage by performing these activities better or at a lower cost
than competitors
– A tool to represent the main activities in the business and their relationships in terms of how
they add value so as to satisfy the customer and obtain resources from suppliers
Firm Infrastructure
Those that are involved in the
creation, sale and transfer of
products (including after-sales
service)
Human Resource Management
Support
activities
Technology Development
Procurement
Inbound
Logistics
Those that merely support the
primary activities
Primary
Activities
8
Operations
Outbound
Logistics
Marketing
Service
Value Chain (2/3)
■ How to use Value Chain?
– Internal Analysis for the
firm
Support
activities
– Analysis all the
activities according
to the description
below
Primary
Activities
Category
Operations
Outbound
Logistics
Marketing
Service
Description
Concerned with receiving storing, distributing inputs
Comprise the transformation of the inputs into the final product form
Outbound logistics
Involve the collecting, storing, and distributing the product to the buyers
Marketing and sales
How buyers can be convinced to purchase the product
Service
Procurement
Support activities
Operations
Inbound
Logistics
Activities
Inbound logistics
Primary activities
Firm Infrastructure
Human Resource Management
Technology Development
Procurement
Involves how to maintain the value of the product after it is purchased
Concerned with the tasks of purchasing inputs
Technology development
These activities are intended to improve the product and the process
Human resource management
Involve in recruiting, hiring, training, development and compensation
Firm infrastructure
The activities which are not specific to any activity area
9
Value Chain (3/3)
■ Example
– Value chain of a generic airport company
10
P5CFM (1/3)
■ What is P5CFM (Porter’s Five-Force Model)?
– A tool to know about difference forces that impact on a company’s ability to compete
– A tool to diagnose the principal competitive pressures in a market
– A tool to assess how strong and important each force is
Threat of Potential
Entrants
Bargaining Power
of Suppliers
Degree of
Existing Rivalry
Threat of
Substitutes
11
Bargaining Power
of Buyers
P5CFM (2/3)
Threat of
Potential
Entrants
■ How to use P5CFM?
– External Analysis for the firm
– Analysis all the
activities according
to the description
below
Bargaining
Power of
Suppliers
Degree of
Existing Rivalry
Bargaining
Power of
Buyers
Threat of
Substitutes
Force
Degree of existing rivalry
Threat of potential entrants
Bargaining power of suppliers
Bargaining power of buyers
Threat of substitutes
Description
The major determinant of the competitiveness of the industry for most industries
Many new entrants will decrease profitability for all firms in the industry
When suppliers of raw materials, components, labor, and services have a strong power, they may
refuse to work with the firm, or charge excessively high prices for unique resources
The ability of customers to put the firm under pressure, which also affects the customer's sensitivity
to price changes
The existence of products outside of the realm of the common product boundaries increases the
propensity of customers to switch to alternatives
12
P5CFM (3/3)
■ Example
– A lubricants industry analysis
13
BSC (1/2)
■ What is BSC (Balanced ScoreCard)?
– A strategic performance management tool that is used extensively in business and
organizations worldwide
• To align business activities to the vision and strategy of the organization
• To improve internal and external communications
• To monitor organization performance against strategic goals
– Set of measures that gives top managers a fast but comprehensive view of the business
■ How to use BSC?
– Build up goals and measure in terms of the four perspectives
Perspective
Description
Financial
• Encourages the identification of a few relevant
high-level financial measures
• "How do we look to shareholders?"
Customer
• Encourages the identification of measures that
answer the question "How do customers see us?"
Internal Business
• Encourages the identification of measures that
answer the question "What must we excel at?"
Learning and Growth
• Encourages the identification of measures that
answer the question "How can we continue to
improve and create value?"
14
BSC (2/2)
■ Example
– ECI ’s Balanced Business Scorecard
Financial Perspective
Internal Business
Perspective
Customer Perspective
Learning and Growth
Perspective
15
Knowledge Map (1/3)
■ What is Knowledge Map?
– A diagrammatic representation of corporate knowledge, having nodes as knowledge and links
as the relationships between knowledge, and knowledge specification or profile
– Two components
• Diagram: graphical representation of knowledge
 Node: rectangular object denoting knowledge captured from business processes
 Linkage: arrow between nodes implying relationships among knowledge
• Specification: descriptive representation of knowledge
– Advantages
•
•
•
•
Formalization of all knowledge inventories within an organization
Perception of relationships between knowledge
Efficient navigation of knowledge inventory
Promotion of socialization/externalization of knowledge by connecting domain experts with knowledge
explorers
– The figure left depicts a conceptual model
of knowledge map
16
Knowledge Map (2/3)
■ How to use Knowledge Map?
– Procedures of building the knowledge map
To provide a uniform, text-based
intermediate representation of the
knowledge types specific to a
development effort that is
comprehensible by either humans or
machines
Defining organizational knowledge
Process map analysis
Knowledge extraction
Knowledge profiling
Knowledge linking
Knowledge map validation
17
Knowledge Map (2/3)
■ How to use Knowledge Map?
– Procedures of building the knowledge map
Defining organizational knowledge
 Business process is analyzed using a
process map technique
Process map analysis
 Composed of process, flow, event,
and external object
Knowledge extraction
– An example of a process map of issuing
a membership card
Knowledge profiling
Knowledge linking
Knowledge map validation
18
Knowledge Map (2/3)
■ How to use Knowledge Map?
– Procedures of building the knowledge map
Defining organizational knowledge
 Three types of the extracted
knowledge:
Process map analysis
 Prerequisite knowledge before
process execution
 Used knowledge during execution
 Produced knowledge after execution
Knowledge extraction
 Techniques available:
 Interviewing
Knowledge profiling
 Document analysis
 System analysis
 Knowledge workshop
Knowledge linking
 Brainstorming, etc.
Knowledge map validation
19
Knowledge Map (2/3)
■ How to use Knowledge Map?
– Procedures of building the knowledge map
Defining organizational knowledge
Process map analysis
Knowledge extraction
Supports connecting people with
information and connecting people with
people by providing
Knowledge profiling
 Informational attributes
 keywords, description, importance
 People-finder attributes
Knowledge linking
 an expert or author
Knowledge map validation
20
Knowledge Map (2/3)
■ How to use Knowledge Map?
– Procedures of building the knowledge map
– An example of knowledge link
Defining organizational knowledge
Process map analysis
Knowledge extraction
Knowledge profiling
 Knowledge link is represented as an
arrow in a knowledge map
Knowledge linking
 Link denotes pre- and postrelationship between knowledge
Knowledge map validation
21
Knowledge Map (2/3)
■ How to use Knowledge Map?
– Procedures of building the knowledge map
Defining organizational knowledge
Process map analysis
Knowledge extraction
Knowledge profiling
Knowledge linking
 A structured walkthrough is
conducted with domain experts,
business managers, and knowledge
map producer
Knowledge map validation
22
Knowledge Map (3/3)
■ Example
– P steel company
The rolling mill reduces a hot slab into a
coil of specified thickness
Defining organizational knowledge
To specify the knowledge requirement,
analyze input sources, and develop basic
taxonomy
Process map analysis
 Five categories of segment knowledge
 Mechanical
Knowledge extraction
 Electrical
 Instrumental
 Information system
Knowledge profiling
 Control
Knowledge linking
Knowledge map validation
23
Knowledge Map (3/3)
■ Example
– P steel company
Defining organizational knowledge
Process map analysis
Knowledge extraction
Knowledge profiling
Knowledge linking
Knowledge map validation
24
Knowledge Map (3/3)
■ Example
– P steel company
Defining organizational knowledge
Process map analysis
Knowledge extraction
Knowledge profiling
Knowledge linking
Knowledge map validation
25
Knowledge Map (3/3)
■ Example
– P steel company
Defining organizational knowledge
Process map analysis
Knowledge extraction
Knowledge profiling
Knowledge linking
Knowledge map validation
26
Knowledge Map (3/3)
■ Example
– P steel company
Defining organizational knowledge
Process map analysis
Knowledge extraction
Knowledge profiling
Knowledge linking
Knowledge map validation
27
Knowledge Map (3/3)
■ Example
– P steel company
Defining organizational knowledge
Process map analysis
Knowledge extraction
Knowledge profiling
Knowledge linking
 A structured walkthrough is
conducted with domain experts
Knowledge map validation
28
Decision Tree (1/3)
■ What is Decision Tree?
– A decision support tool that uses a tree-like graph or model of decisions and their possible
consequences, including chance event outcomes, resource costs, and utility
• To display an algorithm, to help identify a strategy most likely to reach a goal
• To be used in operations research, specifically in decision analysis
– Advantages
• Are simple to understand and interpret
 People are able to understand decision tree models after a brief explanation
• Have value even with little hard data
 Important insights can be generated based on experts describing a situation (its alternatives, probabilities, and costs) and
their preferences for outcomes
• Use a white box model
 If a given result is provided by a model, the explanation for the result is easily replicated by simple math
• Can be combined with other decision techniques
 may use Net Present Value calculations, PERT 3-point estimations and a linear distribution of expected outcomes
– Disadvantages
• For data including categorical variables with different number of levels, information gain in decision trees
are biased in favor of those attributes with more levels
29
Decision Tree (2/3)
■ How to use Decision Tree?
– 3 types of nodes
• Decision nodes
 Represented by squares
• Chance nodes
 Represented by circles
• End nodes
 Represented by triangles
– Process
•
•
•
•
•
•
•
Interview decision makers and construct a preliminary tree
Present tree and show how various concerns are captured
Solicit a list of new concerns
Revise tree
Estimating Probabilities from Data, Experts & Literature
Estimating Costs
Analysis of Trees
 Folding back
» Replace a node with its expectation
 Continue until the decision node
30
Decision Tree (3/3)
■ Example
– Screen golf or field golf?
• Chance of rain tomorrow: 60%
Satisfaction
Level
Payoff
4
4×0.4 = 1.6
Sunny (0.4)
1.6 + 4.8 = 6.2
Screen golf
Rainy (0.6)
8
8×0.6 = 4.8
10
10×0.4 = 4.0
Start
Filed golf
Sunny (0.4)
4.0 + 1.2 = 5.2
2
Rainy (0.6)
31
2×0.6 = 1.2
What-if Analysis (1/2)
■ What is What-if Analysis?
– Observing how changes to selected variables affect other variables
– Examples of what if analysis
•
•
•
•
•
What
What
What
What
What
if
if
if
if
if
air traffic was shut down due to another volcano? What would this do to our supply chain?
we offered our client a new discount model? Would they buy more products in the future?
we were able to reduce our expenses by 5%? How much flexibility would we gain?
every employee reduced their business travel by just one trip per year?
we changed our fixed phone plans to variable ones? Would we be able to save cost?
– Sensitivity analysis: A special case of what-if analysis. Typically, the value of only one variable
is changed repeatedly, and the resulting changes on other variables are observed
■ How to use What-if Analysis
Identify the relationship
Understanding the relationship between the variables selected to be changed and
the variables which will be affected by the selected variables’ change
Analyze effect of change
Changing the selective variables at various levels and (mathematically or
strategically) predicting the effect of the change to the variables we are observing
Make strategy
against the change
With the results from the previous step, design strategies to make the negative
effect minimum and the positive effect maximum when the expected change occurs
32
What-if Analysis (2/2)
■ Simple example of What-if Analysis
– Lead time: 3 days / Demand per day: 10 items / Safety Stock: 30 items (3 days × 10)
Re-order Quantity: 100 / Initial Stock: 100
Thus, we need to re-order on 7th, 17th, 27th…day (re-order interval: 10 days).
– What if demand per day increased to 20 items? What would this do to our inventory control?
 The safety stock would be 60 items (3 days x 20) and re-order quantity would be 200 items
or, re-order interval would be shortened to 5 days with re-order quantity as 100 items
33
Delphi Method (1/2)
■ What is Delphi Method?
– Communication technique based on a structured process for collecting and synthesizing
knowledge from a group of experts by means of a series of questionnaires accompanied by
controlled opinion feedback
– Key characteristics:
• Structuring of information flow
• Regular feedback
• Anonymity of the participants
■ How to use Delphi Method
Define the problem
Give everyone the problem
Collate the response
Give everyone the collation
Identifying the problem(s) in various forms from a questionnaire to a broad and
open question
Recruiting experts to the Delphi group, sending the problem(s) to everyone in the
group and asking them to respond
Taking the responses that experts send back and collating these into a single
anonymous list or sets of lists
Sending the collation back out to everyone with request to score each item on a
given scale (typically 1 to 5) and may allow them to add further items or comments
Repeat as necessary
Repeating the rounding until a certain stopping condition meets
(Number of iterations, a specific level of agreement)
Act on the findings
Analyzing the findings and putting plans in place to deal with future risks and
opportunities in the project
34
Delphi Method (2/2)
■ Example
– Choosing the next strategy for the company with 5 experts (Stopping Condition: 3 rounds)
• 1st round (Questionnaire & Scoring result)
Score (1 is low, 7 is high)
Strategy + Comments
E1
E2
E3
E4
E5
Organizing a task force team
2
6
4
5
7
Promoting a new supervisor for the project
5
3
4
6
2
Proceeding the project as now
3
2
1
4
3
Organize a new department for the project
6
2
1
3
1
• 2nd round (Questionnaire & Scoring result)
Score (1 is low, 7 is high)
Strategy + Comments
Organizing a task force team
Promoting a new supervisor for the project
+ It can be hard to find a right person for the job
Proceeding the project as now
+ It is inefficient
E1
E2
E3
E4
E5
4
6
5
6
7
3
3
4
2
2
3
2
2
3
3
2
1
1
2
1
Organize a new department for the project
+ Reorganizing will be needed after the project is over
+ It is premature to make a new department
+ The future of project is a little gray
35
Delphi Method (2/2)
■ Example
– Choosing the next strategy for the company with 5 experts (Stopping Condition: 3 rounds)
• 3rd round (Questionnaire & Scoring result)
Score (1 is low, 7 is high)
Strategy + Comments
Organizing a task force team
+ It would be a good chance to make proper people involved to
the project regardless of their departments
Promoting a new supervisor for the project
+ It can be hard to find a right person for the job
+ It can be a threat to the current supervisor and make him upset
E1
E2
E3
E4
E5
6
6
5
7
7
2
3
2
2
1
3
2
2
1
3
1
1
1
2
1
Proceeding the project as now
+ It is inefficient
+ The project is too big to be proceeded with the current
temporary process
Organize a new department for the project
+ Reorganizing will be needed after the project is over
+ It is premature to make a new department
+ The future of project is a little gray
 The company choose “Organizing a task force team” strategy
36
ERD (1/5)
■ What is ERD (Entity Relationship Diagram)?
– A detailed, logical representation of the entities, associations and data elements for an organiz
ation or business
■ How to use ERD?
– Data entities
• An entity is a business object that represents a group, or category of data
 Person, place, object, event or concept about which data is to be maintained
– Attributes
• An attribute is a sub-group of information within an entity
 Named property or characteristic of an entity
– Relationship models
• Association between the instances of one or more entity types






Mandatory Relationships
Optional Relationships
Many-to-Many Relationships
One-to-Many Relationships
One-to-One Relationships
Recursive Relationships
Entity 1
Entity 2
 Attribute 1-1
 Attribute 2-1
 Attribute 1-2
 Attribute 2-2
 Attribute 1-3
 Attribute 2-3
37
ERD (2/5)
■ How to use ERD (continued)?
– Relationship models: Mandatory, many-to-many
Instructor
Student
Instructor
Student
 Attribute I-1
 Attribute St-1
 Attribute I-1
 Attribute St-1
 Attribute I-2
 Attribute St-2
 Attribute I-2
 Attribute St-2
 Attribute I-3
 Attribute St-3
 Attribute I-3
 Attribute St-3
– Relationship models: Optional, many-to-many
Department
Student
Department
Student
 Attribute D-1
 Attribute St-1
 Attribute D-1
 Attribute St-1
 Attribute D-2
 Attribute St-2
 Attribute D-2
 Attribute St-2
 Attribute D-3
 Attribute St-3
 Attribute D-3
 Attribute St-3
38
ERD (3/5)
■ How to use ERD (continued)?
– Relationship models: Optional/mandatory, many-to-many
Instructor
Skill
Instructor
Skill
 Attribute I-1
 Attribute Sk-1
 Attribute I-1
 Attribute Sk-1
 Attribute I-2
 Attribute Sk-2
 Attribute I-2
 Attribute Sk-2
 Attribute I-3
 Attribute Sk-3
 Attribute I-3
 Attribute Sk-3
– Relationship models: Optional/mandatory, one-to-many
Product
Vendor
Product
Vendor
 Attribute P-1
 Attribute V-1
 Attribute P-1
 Attribute V-1
 Attribute P-2
 Attribute V-2
 Attribute P-2
 Attribute V-2
 Attribute P-3
 Attribute V-3
 Attribute P-3
 Attribute V-3
39
ERD (4/5)
■ How to use ERD (continued)?
– Relationship models: Mandatory, one-to-one
Automobile
Engine
Automobile
Engine
 Attribute A-1
 Attribute En-1
 Attribute A-1
 Attribute En-1
 Attribute A-2
 Attribute En-2
 Attribute A-2
 Attribute En-2
 Attribute A-3
 Attribute En-3
 Attribute A-3
 Attribute En-3
– Relationship models: Recursive
EMPLOYEE
Employee
supervises
 Attribute Em-1
 Attribute Em-2
 Attribute Em-3
is supervised by
40
ERD (5/5)
■ Example
41
DFD (1/2)
■ What is DFD (Data Flow Diagram)?
– Graphical representation of the "flow" of data through an information system, modeling its
process aspects
– Preliminary step used to create an overview of the system which can later be elaborated
– The visualization of data processing (structured design)
■ How to use DFD?
– Draw diagrams to show…
• What kinds of data will be input to and output from the system
• Where the data will come from and go to
• Where the data will be stored
– Notations
Function
File/Database
Input/Output
42
Flow
DFD (2/2)
■ Example
– General Model Of Publisher's Present Ordering System
43
Statistical Hypothesis Testing (1/3)
■ What is Statistical Hypothesis Testing?
– Method of making decisions using experimental data
– Procedure for deciding if a null hypothesis should be accepted or rejected in favor of an
alternate hypothesis
■ How to use Statistical Hypothesis Testing?
– Hypothesis
• H0: θ∈ϴ0 vs. H1: θ∈ ϴ1 where ϴ0 and ϴ1 are partition of possible parameter values
 H0: null hypothesis, H1: alternative hypothesis
• H0: θ ≥ k vs. H1: θ < k; one-sided hypotheses
H0: θ = k vs. H1: θ ≠ k; two-sided hypotheses
• The threshold value c is called a critical value
Setting a critical value is equivalent to dividing the range of the test statistic X into
{x: x < c}: acceptance region, {x: x ≥ c}: rejection region
– Consequences of a decision
• Type I error probability: α(θ) = P(Reject H0 | H0) = Pθ(X ≥ c),
Type Ⅱ error probability: β(θ) = P(Accept H0 | H1) = Pθ(X < c),
• Traditional approach is keeping the type I error probability
under a pre-specified level
α(θ) = P(Reject H0 | H0) ≤ α, for some 0 < α < 1
44
Decision
Truth
Accept H0
Reject H1
H0
O.K.
Type I error
H1
Type II error
O.K.
Statistical Hypothesis Testing (2/3)
■ How to use Statistical Hypothesis Testing? (continued)
– Critical region
• One-sided test
• Two-sided test
α/2
α
Do not reject H0
k
Reject H0
-k
Reject H0 Do not reject H0
α/2
k
Reject H0
– p-value
• The probability of obtaining a test statistic at least as extreme as the one that was actually observed,
assuming that the null hypothesis is true
• p-value = P(observed value | H0 is true )
45
Statistical Hypothesis Testing (3/3)
■ Example
– A random sample of 100 recorded deaths in the United States during the past year showed an
average life span of 71.8 years. Assuming a population standard deviation of 8.9 years, does
this seem to indicate that the mean life span today is greater than 70 years?
Use a 0.05 level of significance.
– Solution
• H0: μ ≤ 70 years
• H1: μ > 70 years
• α = 0.05
• Critical region: z > 1.645=z0.05, where 𝑍 =
𝑋−𝜇0
𝜎/ 𝑛
• Computations: 𝑋=71.8 years, σ=8.9 years, and 𝑍 =
71.8−70
8.9/√100
= 2.02
• Decision: Reject H0 and conclude that the mean life span today is greater than 70 years
• P-value : P=P(Z>2.02) = 0.0217 < 0.05
critical region (α=0.05)
n(0, 1)
p=0.0217
0
Do not reject H0
46
1.645 2.02
Reject H0
Regression Analysis (1/4)
■ What is Regression Analysis?
– Techniques for modeling and analyzing the relationship between dependent variables and
independent variables
– Estimating the conditional expectation of the dependent variable given the independent
variables
– Used for prediction and forecasting, understanding related independent variables, and
exploring the forms of the relationships
■ How to use Regression Analysis?
– Regression models
Y = f(X, β)
• The unknown parameters, denoted as β, which may represent a scalar or a vector
• The independent variables, X
• The dependent variable, Y
• Usually formalized as E(Y|X) = f(X, β)
– Simple linear regression model
Y = β𝟎 + β𝟏 𝒙 + ℰ
• β𝟎 and β𝟏 : parameters of the model
• ℰ: error term (random variable with mean of zero)
47
Regression Analysis (2/4)
■ How to use Regression Analysis? (Cont’d)
– Simple linear regression Equation
E(y)= β𝟎 + β𝟏 𝒙
• β𝟎 : y intercept of the regression line
• β𝟏 : slope of the regression line
• E(y): expected value of y for a given x value
– Estimated simple linear regression equation
• b𝟎 : y intercept of the regression line
• b𝟏 : slope of the regression line
• ŷ: estimated value of y for a given x value
𝒚 = b𝟎 + b𝟏 𝒙
– Least squared criterion
min
𝒚𝒊 − ŷ𝒊
𝟐
• 𝒚: observed value of the dependent variable for the ith observation
• ŷ: estimated value of the dependent variable for the ith observation
(𝒙𝒊 − 𝒙)(𝒚𝒊 − 𝒚)
𝒙𝒊 − 𝒙 𝟐
– Slope for the estimated regression equation:
𝒃𝟏 =
– Y-Intercept for the estimated regression equation:
𝒃𝟎 = 𝒚 − 𝒃𝟏 𝒙
48
Regression Analysis (3/4)
■ How to use Regression Analysis? (Cont’d)
– R squared: Coefficient of determination
𝑹𝟐
𝑺𝑺𝑹
𝑺𝑺𝑬
=
=𝟏−
𝑺𝑺𝑻𝑶
𝑺𝑺𝑻𝑶
• 𝑺𝑺𝑹 =
𝒀𝒊 − 𝒀
• 𝑺𝑺𝑻𝑶 =
𝒀𝒊 − 𝒀
• 𝑺𝑺𝑬 =
𝒀𝒊 − 𝒀𝒊
𝟐
- Regression sum of squares
𝟐
- Total sum of squares
𝟐
- Error sum of squares
• 𝟎 ≤ 𝑹𝟐 ≤ 𝟏 since 𝟎 ≤ 𝑺𝑺𝑬 ≤ 𝑺𝑺𝑻𝑶
• 𝑹𝟐 : Proportionate reduction of total variation associated with the use of the predictor variable X
 When all observation fall on the fitted regression line, then 𝑺𝑺𝑬 = 𝟎 and 𝑹𝟐 = 𝟏
 When the fitted regression line is horizontal so that 𝐛𝟏 = 𝟎 and 𝒀𝒊 ≡ 𝒀, then 𝑺𝑺𝑬 = 𝑺𝑺𝑻𝑶 and 𝑹𝟐 =0
49
Regression Analysis (4/4)
■ Example
– Reed Auto periodically has a special week-long sale. As part of the advertising campaign Reed
runs one or more television commercials during the weekend preceding the sale. Data from a
sample of 5 previous sales are shown in the below box
(𝒙𝒊 − 𝒙)(𝒚𝒊 − 𝒚) 𝟐𝟎
=
=𝟓
𝒙𝒊 − 𝒙 𝟐
𝟒
• Slope for the Estimated Regression Equation:
𝒃𝟏 =
• y-Intercept for the Estimated Regression Equation:
𝒃𝟎 = 𝒚 − 𝒃𝟏 𝒙 = 𝟐𝟎 − 𝟓 𝟐 = 𝟏𝟎
• Estimated Regression Equation:
𝒚 = 𝟏𝟎 + 𝟓𝒙
 If the company puts 5 TV ads, it is expected for the company to sell about 35 cars
50
AHP (1/2)
■ What is AHP(Analytic Hierarchy Process)?
– A modeling structure for representing multicriteria (multiple goals, multiple objectives)
problems – with sets of criteria and alternatives (choices) – commonly found in business
environments
– Benefits
• Helping capture both subjective and objective evaluation measures
• Providing a useful mechanism for checking the consistency of the evaluation measures and alternatives
suggested by the team
■ How to use AHP
Step 1
Step 2
Step 3
Step 4
Step 5
Step 6
Goal
1.00
Defining policy issues and establishing policy-making members
Constructing the hierarchy layer structure of the problems
Criterion 1
0.75
Conducting questionnaire surveys and expert preference integration
Criterion 2
0.25
Establishing pair-wise comparison matrices
SubC 1
0.15
Conducting consistency test
Selecting the most optimal option
SubC 2
0.2
SubC 3
0.4
SubC 3
0.1
SubC 3
0.15
Hierarchy layer structure
51
AHP (2/2)
■ Example
Step 1
Defining policy issues and
establishing policy-making members
• Issue: Which car is the best car for you?
Choice of a Car
Step 2
Constructing the hierarchy layer
structure of the problems
Step 3
Conducting questionnaire surveys
and expert preference integration
(1-9 scale preference questionnaire)
Step 4
Step 5
Step 6
Function
Level (FL)
PL
Establishing pair-wise comparison
matrices
1
(𝑎𝑖𝑗 = 𝑎 if i≠j; 𝑎𝑖𝑗 = 1 if i=j)
𝑗𝑖
(Weight: normalization of geometric
means of each row; ∑Weight = 1)
Price
Level (PL)
Safety
Level (SL)
Design
Level (DL)
98765432123456789
SL
FL
PL
SL
DL
Weight
FL
1
5
6
9
0.641
PL
1/5
1
2
7
0.198
SL
1/6
1/2
1
5
0.123
DL
1/9
1/7
1/5
1
0.038
• FL>PL, PL>SL, FL>SL → Consistency
• FL>PL, PL>SL, SL>FL → Inconsistency
Conducting consistency test
Selecting the most optimal option
by comparing each alternative’s
weighted sum of criteria values
52
FL
PL
SL
DL
Score
Car A
80
60
60
90
73.96
Car B
60
90
80
50
68.02