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TUM School of Management Production and Supply Chain Management
Prof Martin Grunow Technische Universitt Mnchen
Quality Engineering & Management
Session 2.1: Probability vs Statistics
Dr. Holly Ott Production and Supply Chain Management
Chair: Prof. Martin Grunow TUM School of Management
Holly Ott Quality Engineering & Management Module 2.1 1
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TUM School of Management Production and Supply Chain Management
Prof Martin Grunow Technische Universitt Mnchen
Holly Ott Quality Engineering & Management Module 2.1 2
Learning Objectives
Explain the difference between "Probability" and "Statistics."
Give examples of "measurement" and "attribute" data. Describe how
statistics are used in the field of quality engineering. List major
statistical methods used in quality engineering.
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TUM School of Management Production and Supply Chain Management
Prof Martin Grunow Technische Universitt Mnchen
Probability and Statistics
Holly Ott Quality Engineering & Management Module 2.1 3
Reiner Hutwelker
Why are you complaining?How can you justify your complaint,
considering the- Sample size?- The relative frequency of each
result?
Game: I will flip a coin (one "Euro")
For every "number", you receive 1. But for every "eagle," you
have to pay me 1.
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TUM School of Management Production and Supply Chain Management
Prof Martin Grunow Technische Universitt Mnchen
Probability
Inference
Statistics
Argon Chen
Holly Ott Quality Engineering & Management Module 2.1 4
Model (Population) Statistics
Probability and Statistics
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TUM School of Management Production and Supply Chain Management
Prof Martin Grunow Technische Universitt Mnchen
An Example: Taiwan Big Lotto
Holly Ott Quality Engineering & Management Module 2.1 5
http://www.taiwanlottery.com.tw/Lotto649/index.asp
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TUM School of Management Production and Supply Chain Management
Prof Martin Grunow Technische Universitt Mnchen
An Example: Taiwan Big Lotto
6 numbers chosen from 49 numbers Mr. Chang chooses numbers
randomly and never believes in any
historical analysis of number appearance.
Mr. Fang chooses numbers that most frequently appear in the
history.
Mr. Wang chooses numbers that most rarely appear in the history.
Mr. Yang chooses meaningful numbers, such as the date of
birthday.
Who is correct?
Holly Ott Quality Engineering & Management Module 2.1 6
Argon Chen
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TUM School of Management Production and Supply Chain Management
Prof Martin Grunow Technische Universitt Mnchen
Probability of Taiwan Big Lotto
6 numbers chosen from 49 numbers What is the probability of
winning the first prize?
Answer: From probability theory, we know that this is 1 over the
number of the possible combinations of 49 distinct objects taken 6
at a time.
Is this answer based on probability or statistics?
The winning probability inferred from the model. What are the
assumptions behind this answer?
Every number has the identical probability to be chosen
independently each time!
Holly Ott Quality Engineering & Management Module 2.1 7
Argon Chen
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TUM School of Management Production and Supply Chain Management
Prof Martin Grunow Technische Universitt Mnchen
Statistics of Taiwan Big Lotto
6 numbers chosen from 49 numbers What numbers do we expect to
appear?
Answer: Statistics is used to estimate the appearance
probability of each number.
How do we do this? We use statistics to infer the model from the
sample.
Holly Ott Quality Engineering & Management Module 2.1 8
Argon Chen
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TUM School of Management Production and Supply Chain Management
Prof Martin Grunow Technische Universitt Mnchen
Taiwan Big Lotto (Contd)
Back to Mr. Chang, Mr. Fang, Mr. Wang and Mr. Yang, Who is
correct?
Mr. Chang chooses numbers randomly and never believes in any
historical analysis of number appearance.
Mr. Fang chooses numbers that most frequently appear in the
history.
Mr. Wang chooses numbers that most rarely appear in the history.
Mr. Yang chooses meaningful numbers, such as the date of
birthday.
Holly Ott Quality Engineering & Management Module 2.1 9
Argon Chen
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TUM School of Management Production and Supply Chain Management
Prof Martin Grunow Technische Universitt Mnchen
A Note on Models
All models are wrong, but some are useful. - George E. P. Box
(1979)
Holly Ott Quality Engineering & Management Module 2.1 10
