Data Mining Business Decisions Discussion Board 2. Components of Decision Analysis and Decision Criteria
Review the discussion questions below, think about them, and then respond: Explain the components of decision analysis and provide real world examples. Describe the five decision criteria in the module and explain how to use these criteria for decision making. In peer responses, compare real world examples. What can be added to the explanation of decision analysis? What can be added to the explanation of criteria use?
Probability and Statistics
A Foundation for Becoming a
More Effective and Efficient
Problem Solver
Data Descriptions- Histogram
A graph consisting of bars of equal width drawn
adjacent to each other (unless there are gaps in
the data)
The horizontal scale represents the classes of
quantitative data values and the vertical scale
represents the frequencies.
The heights of the bars correspond to the
frequency values.
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Example
IQ scores from children with low levels of lead.
IQ Score
Frequency
50-69
2
70-89
33
90-109
35
110-129
7
130-149
1
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Relative Frequency Histogram
has the same shape and horizontal scale as a histogram, but the
vertical scale is marked with relative frequencies instead of actual
frequencies
IQ Score
Relative
Frequenc
y
50-69
2.6%
70-89
42.3%
90-109
44.9%
110-129
9.0%
130-149
1.3%
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Graphical Methods of data
Descriptions -1
Histograms
Graphical Methods of data
Descriptions -2
Relative Frequency Diagrams
Probability & Statistics
Outline Probability and Statistics
2 types of probability
Rules of probability
Statistical Independence
Expected Value
Normal Distributions
2 Types of Probability
Subjective
Probability estimate based on what a person
believes or experiences
“I think there is a 60% chance of rain tomorrow.”
Objective
Probabilities that can be stated before or a priori
the occurrence of an event
Roll of a fair dice
Flip of a fair coin
Fundamentals and Rules of Probability
Rules of probability
1. 0 < P(A) < 1
2. ΣPi = 1
3. P(A or B) = P(A) + P(B), for mutually
exclusive A & B
Mutual Exclusivity
Only one event can occur at a time
A
B
Addition rule
P(A) + P(B) = P(A OR B)
Probability Types
Marginal
Probability of a single event
e.g., P(A) = 0.1
Joint
Probability of more than one event
e.g., P(A and B) = 0.2
Example of a Joint Probability
Probability of two non-mutually exclusive events
occurring
A
B
Shaded area is a joint probability
General addition rule