1. Probability Probability Principles of EngineeringTM
Unit 4 – Lesson Statistics
Probability
Principles Of Engineering
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2. Probability The calculated likelihood that a given event will occur
Principles of EngineeringTM
Unit 4 – Lesson Statistics
The calculated likelihood that a given event will occur

3. Methods of Determining Probability
Principles of EngineeringTM
Unit 4 – Lesson Statistics
Empirical
An estimate that the event will happen based on how often the event occurs from data collection or an experiment (large # of trials).
Closely related to relative frequency.
Experimental observation Example – Process control
In practice, engineers will often blend these approaches. For instance, engineers will assume that each one of the widgets produced at a factory has the same (unknown) chance of failure, then make observations to determine the likelihood of failure.

4. Methods of Determining Probability
Theoretical
Number of ways an event can occur divided by the total number of outcomes.
Uses known elements
Example – Coin toss, die rolling
Subjective Assumptions Example – I think that . . .

5. Methods of Determining Probability
In practice, engineers will often blend these approaches. For instance, engineers will assume that each one of the widgets produced at a factory has the same (unknown) chance of failure, then make observations to determine the likelihood of failure.

6. Probability Components
Principles of EngineeringTM
Unit 4 – Lesson Statistics
Experiment An activity with observable results
Sample Space A set of all possible outcomes
Event A subset of a sample space
Outcome / Sample Point The result of an experiment

7. Probability Components
For example, You might test a brass sample to find its tensile strength. [experiment]
It might break under any load from 0 to 1000 pounds in the tester, or it may not break at all. [sample space]
One possible outcome is that a sample could break at 200 pounds. [Event]
When you perform the test, it breaks at a particular load. [Outcome]

8. Probability What is the probability of a tossed coin landing heads up?
Principles of EngineeringTM
Unit 4 – Lesson Statistics
What is the probability of a tossed coin landing heads up?
Experiment
Sample Space
Probability Tree
Event
The probability tree lists all possible outcomes. Many experiments have far too many possible results to write out a probability tree.
Outcome

9. Probability
Probability
Principles of EngineeringTM
Unit 4 – Lesson Statistics
A way of communicating the belief that an event will occur.
Expressed as a number between 0 and 1 fraction, percent, decimal, odds
Total probability of all possible events totals 1
We’ll deal only with probabilities in this lesson. The odds of an event are slightly different than the probabilities, but if you know one, you can find the other.

10. Relative Frequency
Probability
Principles of EngineeringTM
Unit 4 – Lesson Statistics
The number of times an event will occur divided by the number of opportunities
= Relative frequency of outcome x
= Number of events with outcome x
n = Total number of events
Expressed as a number between 0 and 1 fraction, percent, decimal, odds
Total frequency of all possible events totals 1
We’ll deal only with probabilities in this lesson. The odds of an event are slightly different than the probabilities, but if you know one, you can find the other.

11. Probability What is the probability of a tossed coin landing heads up?
Principles of EngineeringTM
Unit 4 – Lesson Statistics
What is the probability of a tossed coin landing heads up?
How many desirable outcomes? 1
How many possible outcomes?
Probability Tree 2
What is the probability of the coin landing tails up?

12. Probability
Probability
Principles of EngineeringTM
Unit 4 – Lesson Statistics
What is the probability of tossing a coin twice and it landing heads up both times? HH
How many desirable outcomes? 1 HT
How many possible outcomes? 4 TH TT

13. Probability
Probability
Principles of EngineeringTM
Unit 4 – Lesson Statistics
3rd
HHH
What is the probability of tossing a coin three times and it landing heads up exactly two times?
2nd
HHT
1st
How many desirable outcomes?
HTH 3
HTT
How many possible outcomes?
THH 8
THT
TTH
TTT

14. Probability
Binomial Process
Principles of EngineeringTM
Unit 4 – Lesson Statistics
Each trial has only two possible outcomes yes-no, on-off, right-wrong
Trial outcomes are independent Tossing a coin does not affect future tosses
The probability of heads occurring on subsequent coin flips does not change.
Notice that a binomial does not have to be a chance. Getting a “6” on a die roll is also binomial (you either get the 6 or you don’t).

15. Bernoulli Process P = Probability
Principles of EngineeringTM
Unit 4 – Lesson Statistics
P = Probability
x = Number of times for a specific outcome within n trials
n = Number of trials
p = Probability of success on a single trial
q = Probability of failure on a single trial
! = factorial – product of all integers less than or equal

16. Probability
Bernoulli Process
Principles of EngineeringTM
Unit 4 – Lesson Statistics
Technically a Bernoulli process happens only once (flip one coin), while a binomial process comes by adding many Bernoulli processes. The formula here is for a binomial process (combining the results of n independent Bernoulli trials).
n! (read “n factorial”) is the product of all integers from 1 to n. For instance, 5! = 5 x 4 x 3 x 2 x 1 = 120

17. Probability
Bernoulli Process
Principles of EngineeringTM
Unit 4 – Lesson Statistics
nCx (read “n choose x”) is the number of distinct groups of x things that can be chosen from n distinct things.
Most scientific and graphing calculators have a ! key and a nCx key. Both functions can often be found in a probability menu.

18. Probability Distribution
Principles of EngineeringTM
Unit 4 – Lesson Statistics
What is the probability of tossing a coin three times and it landing heads up two times?
(3-2)

19. Probability
Law of Large Numbers
Principles of EngineeringTM
Unit 4 – Lesson Statistics
The more trials that are conducted, the closer the results become to the theoretical probability
Trial 1: Toss a single coin 5 times H,T,H,H,T P = .600 = 60%
Trial 2: Toss a single coin 500 times H,H,H,T,T,H,T,T,……T P = .502 = 50.2%
Theoretical Probability = .5 = 50%
It is still possible to flip a fair coin many times in a row and get significantly different numbers of heads and tails, but this is very unlikely.

20. Probability AND (Multiplication) P(A and B) = PA∙PB
Principles of EngineeringTM
Unit 4 – Lesson Statistics
AND (Multiplication)
Independent events occurring simultaneously
Product of individual probabilities
If events A and B are independent, then the probability of A and B occurring is:
P(A and B) = PA∙PB
*Independence means that one event’s outcome doesn’t affect the other event’s outcome.

21. Probability AND (Multiplication) 1 6 1 6
Principles of EngineeringTM
Unit 4 – Lesson Statistics
AND (Multiplication)
What is the probability of rolling a 4 on a single die? 1
How many desirable outcomes? 6
How many possible outcomes?
What is the probability of rolling a 1 on a single die? 1
How many desirable outcomes? 6
How many possible outcomes?
We can multiply these probabilities because they are independent.
What is the probability of rolling a 4 and then a 1 in sequential rolls?

22. Probability OR (Addition) P(A or B) = PA + PB
Principles of EngineeringTM
Unit 4 – Lesson Statistics
OR (Addition)
Independent events occurring individually
Sum of individual probabilities
If events A and B are mutually exclusive, then the probability of A or B occurring is:
P(A or B) = PA + PB
*If A and B are mutually exclusive, it is not possible for both to occur at the same time. For example, you might want to know the probability of drawing either one of the two cards: a 2 of diamonds or a 2 of spades from a single draw.

23. Probability OR (Addition) 1 6 1 6
Principles of EngineeringTM
Unit 4 – Lesson Statistics
OR (Addition)
What is the probability of rolling a 4 on a single die? 1
How many desirable outcomes? 6
How many possible outcomes?
What is the probability of rolling a 1 on a single die? 1
How many desirable outcomes? 6
How many possible outcomes?
We can add these probabilities because they’re mutually exclusive.
If two events aren’t mutually exclusive, we have to add their probabilities and then subtract the probability that they both occur.
What is the probability of rolling a 4 or a 1 on a single die?

24. Probability NOT P = 1 - P(A) Independent event not occurring
Principles of EngineeringTM
Unit 4 – Lesson Statistics
NOT
Independent event not occurring
1 minus the probability of occurrence
P = 1 - P(A)
What is the probability of not rolling a 1 on a die?

25. Probability
Probability
Principles of EngineeringTM
Unit 4 – Lesson Statistics
Two cards are dealt from a shuffled deck. What is the probability that the first card is an ace and the second card is a face card or a ten?
How many cards are in a deck? 52
How many aces are in a deck? 4
Dice rolls and coin flips are classic examples of independent events.
Drawing cards is a classic example of a set of dependent events.
How many face cards are in deck? 12
How many tens are in a deck? 4

26. Probability What is the probability that the first card is an ace?
Principles of EngineeringTM
Unit 4 – Lesson Statistics
What is the probability that the first card is an ace?
Since the first card was NOT a face, what is the probability that the second card is a face card?
Since the first card was NOT a ten, what is the probability that the second card is a ten?

27. Probability
Probability
Principles of EngineeringTM
Unit 4 – Lesson Statistics
Two cards are dealt from a shuffled deck. What is the probability that the first card is an ace and the second card is a face card or a ten?
If the first card is an ace, what is the probability that the second card is a face card or a ten?
31.37%

28. Conditional Probability
Principles of EngineeringTM
Unit 4 – Lesson Statistics
P(E|A) = Probability of event E, given A
Example: One card is drawn from a shuffled deck. The probability it is a queen is
P(queen) =
However, if I already know the next card is a face card
P(queen | face)=

29. Conditional Probability
Principles of EngineeringTM
Unit 4 – Lesson Statistics
Probability of two events A and B both occurring =
P(A and B)
= P(A|B)  P(B)
= P(B|A)  P(A)
If A and B are independent, then
P(A and B) = P(A)  P(B)

30. Probability
Bayes’ Theorem
Principles of EngineeringTM
Unit 4 – Lesson Statistics
Calculates a conditional probability, based on all the ways the condition might have occurred.
P( A | E ) = probability of A, given we already know the condition E =
**Bayes’ theorem is intuitive to most people, but computing it can be very difficult.
P(A1 | E) is read “The probability of A1 given E”: that means it’s the likelihood of event A given what we already know about event E.

31. Bayes’ Theorem Example
Probability
Principles of EngineeringTM
Unit 4 – Lesson Statistics
LCD screen components for a large cell phone manufacturing company are outsourced to three different vendors. Vendor A, B, and C supply 60%, 30%, and 10% of the required LCD screen components. Quality control experts have determined that .7% of vendor A, 1.4% of vendor B, and 1.9% of vendor C components are defective.
If a cell phone was chosen at random and the LCD screen was determined to be defective, what is the probability that the LCD screen was produced by vendor A?
Note that the vendors are mutually exclusive: any given screen is outsourced to only one company.

32. Bayes’ Theorem Example
Probability
Principles of EngineeringTM
Unit 4 – Lesson Statistics
Bayes’ Theorem Example
Notation Used:
P = Probability
D = Defective
A, B, and C denote vendors
Unknown to be calculated:
Probability the screen is from A,
given that it is defective

33. Bayes’ Theorem Example
Probability
Principles of EngineeringTM
Unit 4 – Lesson Statistics
Known probabilities:
Probability the screen is from A
Probability the screen is from B
Probability the screen is from C

34. Bayes’ Theorem Example
Probability
Principles of EngineeringTM
Unit 4 – Lesson Statistics
Known conditional probabilities:
Probability the screen is defective given it is from A
Probability the screen is defective given it is from B
Probability the screen is defective given it is from C

35. Bayes’ Theorem Example: Defective Part
Probability
Principles of EngineeringTM
Unit 4 – Lesson Statistics
Bayes’ Theorem Example: Defective Part
= P(screen is defective AND from A)
P(screen is defective from anywhere)

36. LCD Screen Example Probability Principles of EngineeringTM
Unit 4 – Lesson Statistics

37. LCD Screen Example
Probability
Principles of EngineeringTM
Unit 4 – Lesson Statistics
If a cell phone was chosen at random and the LCD screen was determined to be defective, what is the probability that the LCD screen was produced by vendor B?
If a cell phone was chosen at random and the LCD screen was determined to be defective, what is the probability that the LCD screen was produced by vendor C?
This is calculated in the same way as for vendor A. The denominator (.0103) will be the same in all three cases.
P (produced by B| defective) (probability the phone was produced by vendor B given the phone was defective) = .30*.014/.0103 = .4078
P (produced by C| defective) (probability the phone was produced by vendor C given the phone was defective) = .1*.019/.0103 = .1845
Notice that adding all three probabilities gives us = 1 (if we ignore rounding error) because the probability that the phone was manufactured by someone is 100%.
Notice also that even though vendor A is the best (only 0.7% defective parts), it is tied for the most defective phones because it manufactures so many phones.