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Chapter 4 Probability Theory 4.1 What is Probability?

Chapter 4 Probability Theory 4.1 What is Probability?

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Page 1: Chapter 4 Probability Theory 4.1 What is Probability?

Chapter 4Probability Theory

4.1 What is Probability?

Page 2: Chapter 4 Probability Theory 4.1 What is Probability?

Law of Large Numbers

Jacob Bernoulli: “For even the most stupid of men… is convinced that the more observations have been made, the less danger there is of wandering from one’s goal”

Law of Averages is what people say when they assume that eventually they will win the lottery. Law of averages compensates for loss.

Law of Averages does not exist.

Page 3: Chapter 4 Probability Theory 4.1 What is Probability?

Imagine..

You have a hankering for an egg and cheese on a roll (ketchup, salt, pepper..). It is the first day of open campus for seniors, so during your free period you get in the car and drive down to Neils. You get to the light at the end of Martinsville road and it is red. Are you anxious? Do you worry about getting back in time, on this your first day of open campus?

The next day you have the same hankering… and the light is red again – what are the odds???

The following day.. .you guessed it. Would you then decide to go to O’Bagel?Do you really think that the probability of hitting the

red light is 100%Probably not…

Page 4: Chapter 4 Probability Theory 4.1 What is Probability?

Probability

Probability is the long run relative frequency of an event. Randomness eventually settles to probability.

Lets say you keep track….

1 Red 100% (1 out of 1)

2 Green 50% (1 out of 2)

3 Green 33% (1 out of 3)

4 Red 50% (2 out of 4)

5 Red 60% (3 out of 5)

6 Green 50% (3 out of 6)

Day Light is… % of time it is red

Page 5: Chapter 4 Probability Theory 4.1 What is Probability?

What would the graph look like over time

0

0.2

0.4

0.6

0.8

1

1.2

1 2 3 4 5 6 7 8 9 10 11 12

Series1

There is no stop light elf in there watching for your car… Therefore, if the light is going to be red a certain percentage of the time, over time you should see the prediction level out.

Page 6: Chapter 4 Probability Theory 4.1 What is Probability?

Predicting

Predicting particular results is difficult (call heads or tails on a coin toss, win vs. loss for a football pool)

Long run prediction is easier for certain events (in the long run, the coin should be heads roughly 50% of the time)

Each trial is an AttemptWhat happen is the OutcomeCombination of outcomes is called the Event

Page 7: Chapter 4 Probability Theory 4.1 What is Probability?

Imagine this:

The probability of winning Mega Millions = 1/175,711,536.

Imagine 175,711,536 quarters in a row.One is purple on the underside. You will win

the lottery if you pick up the quarter that is purple.

How long is the row of quarters?(5280 feet in a mile)That would get us to….Fresno CA, if we stopped in San Francisco

first… (as the crow flies)

Page 8: Chapter 4 Probability Theory 4.1 What is Probability?

Probabilityhttp://www.ncaa.org/research/prob_of_competing/

http://anthro.palomar.edu/mendel/mendel_2.htm

http://www.wunderground.com/ndfdimage/viewimage?type=pop12&region=us

Probability – the numerical measure of the likelihood of an event.

0 ≤ P(A) ≤ 1

Page 9: Chapter 4 Probability Theory 4.1 What is Probability?

Probability (cont)

What does it mean if P(A) is close to 0?

What does it mean if P(A) is close to 1?

What does it mean if P(A) = 0? = 1?

Page 10: Chapter 4 Probability Theory 4.1 What is Probability?

Probability (cont)

What does it mean if P(A) is close to 0?

What does it mean if P(A) is close to 1?

What does it mean if P(A) = 0? = 1?

fP(A) Relative Frequency =

n

Page 11: Chapter 4 Probability Theory 4.1 What is Probability?

Probability (cont)

Lets try rolling dice. You keep track, and when we are all done we will put the results on a giant chart…

Page 12: Chapter 4 Probability Theory 4.1 What is Probability?

1 2 3 4 5 6

1 2 3 4 5 6 7

2 3 4 5 6 7 8

3 4 5 6 7 8 9

4 5 6 7 8 9 10

5 6 7 8 9 10

11

6 7 8 9 10

11

12

Predicted Frequency of 2, 12? .028

Predicted Frequency 3, 11? .056

Predicted Frequency of 4, 10? .083

Predicted Frequency of 5, 9? .111

Predicted Frequency of 6, 8? .139

Predicted Frequency of 7? .167

Page 13: Chapter 4 Probability Theory 4.1 What is Probability?

Probability (cont)

Was your probability close to predicted?

Did it get better the more sums considered?

For Equally likely outcomes,

Page 14: Chapter 4 Probability Theory 4.1 What is Probability?

Probability (cont)

Was your probability close to predicted?

Did it get better the more sums considered?

For Equally likely outcomes, # of ways favorable to AP(A)

total # of outcomes

Page 15: Chapter 4 Probability Theory 4.1 What is Probability?

Probability (cont)Some Definitions

Statistical Experiment (Observation) – any random activity that results in a definite outcome.

Event – a collection of 1 or more outcomes of a statistical experiment

Simple event – one outcome of a statistical experiment

Sample Space – set of all Simple Events

Sum of Probability of all Simple events = 1

Page 16: Chapter 4 Probability Theory 4.1 What is Probability?

Probability (contr)

The complement of event A = AC = describes the event NOT occurring

ThereforeP(A) + P(AC) = 1

Page 17: Chapter 4 Probability Theory 4.1 What is Probability?

4.2 Probability Rules

Cards, Dice, etc

Page 18: Chapter 4 Probability Theory 4.1 What is Probability?

Dependent vs Independent

First, we need to define an independent vs a dependent event.

Rolling two dice Tossing two coins

Drawing two cards from a deck

Drawing three marbles from a bag

Page 19: Chapter 4 Probability Theory 4.1 What is Probability?

What is a distinguishing factor of these four things

Independent events have no effect on each other. That is, tossing one coin has no impact on what you might get when you toss the second.

Dependent events do. Draw a card from a deck. Can you draw this card again? Not without replacement.

Page 20: Chapter 4 Probability Theory 4.1 What is Probability?

Independent EventsLets look back at our dice chart.What is the probability of rolling a 6 and 1 (in that

order)? What about rolling a 1 and 6 (in that order)?What is the probability of getting two sixes (chart)?

What is the relationship between those numbers?

P(A and B) = P(A) P(B)

Note: if A and B and C, then P(A)P(B)P(C)

Order matters!

Page 21: Chapter 4 Probability Theory 4.1 What is Probability?

Dependent Events

Kind of changes, but looks the same. That is, the probability of the second event will be slightly altered assuming success on the first. The basic concept is the same

P(A and B) = P(A) P(B, given A occurs)

P(A and B) = P(B) P(A, given B occurs)

Page 22: Chapter 4 Probability Theory 4.1 What is Probability?

Dependent Events (cont)

Drawing cards from a deck, without replacement, is a Dependent event. Once you draw the Ace of Hearts, you can’t draw it again.

What is the probability in Texas Hold’Em of being dealt two aces?

What is the probability of being dealt two red aces?

Page 23: Chapter 4 Probability Theory 4.1 What is Probability?

Conditional Probability

If P(A and B) = P(B) P(A, given B), then

Page 24: Chapter 4 Probability Theory 4.1 What is Probability?

Conditional Probability (cont)

If P(A and B) = P(B) P(A, given B), then

P(A and B)

P(A,given B) P(A B)P(B)

That bar notation means probability of A, given B has occurred…

Page 25: Chapter 4 Probability Theory 4.1 What is Probability?

Probability of two events happening together

Back to the dice:What is the probability of getting a

total of 3? Look at your chart…How many ways are there to get a 3? How does this affect probability? Probability of A or B (1 then 2 or 2 then

1)It looks like we….

Page 26: Chapter 4 Probability Theory 4.1 What is Probability?

Probability of two events happening together

Add them..Yes, typically

P(A or B) = P(A) + P(B)

As long as the events are mutually exclusive. That is, if they cannot occur together.

Could one of the dice be 1 and 2 at the same time?? (P(A)+P(B)=0)

Page 27: Chapter 4 Probability Theory 4.1 What is Probability?

Mutually Exclusive Events

Imagine a deck of cards. What is the probability of drawing a diamond OR an ace?

P(diamond) + P(ace)But is there overlap?What if you draw the ace of diamonds?How many ace of diamonds are there?How to deal with this?

Page 28: Chapter 4 Probability Theory 4.1 What is Probability?

Mutually Exclusive Events

If events are mutually exclusive, then P(A or B) = P(A) + P(B)

If events are not mutually exclusive, then

P(A or B) = P(A) + P(B) – P(A and B)

Page 29: Chapter 4 Probability Theory 4.1 What is Probability?

Back to dice

What is the probability of rolling a sum greater than 7?

What is the probability of rolling a sum 7 or greater?

We can count on the chart, but how would it be written?

Page 30: Chapter 4 Probability Theory 4.1 What is Probability?

M&M’s

In 2001 the maker of M&Ms decided to add another color. They surveyed kids in nearly every country and asked them to vote among purple, pink and teal. The global winner was purple.

In the US and Japan the results were:

Purple Pink Teal

US 42% 19% 37%

Japan 16% 38% 36%

Page 31: Chapter 4 Probability Theory 4.1 What is Probability?

M&M’s (cont)

1. What is the probability that a Japanese M&M’s survey respondent selected at random preferred pink or teal?

2. If we pick two Japanese respondents, what is the probability that they both selected purple?

3. If we pick three, what is the probability that at least one preferred purple?

Page 32: Chapter 4 Probability Theory 4.1 What is Probability?

Suspicious driving

Police report that 78% of drivers stopped on suspicion of drunk driving are given a breath test, 36% a blood test, and 22% both tests. What is the probability that a randomly selected DWI suspect is given

A) A test?B) A blood test or a breath test, but not bothC) Neither test?

Page 33: Chapter 4 Probability Theory 4.1 What is Probability?

Same situation…

Are a blood test and breath test mutually exclusive?

Are they independent? (Independent means P(B│A)= P(B)

Probability of B happening given A occurs is the same as P(B)

P(A and B)

P(A B) P(A,given B)P(B)

Page 34: Chapter 4 Probability Theory 4.1 What is Probability?

Same situation…

Are a blood test and breath test mutually exclusive?

Are they independent? (Independent means P(B│A)= P(B)

Probability of B happening given A occurs is the same as P(B)

P(A and B)

P(A B) P(A,given B)P(B)

Page 35: Chapter 4 Probability Theory 4.1 What is Probability?

4.3 Trees and Counting

Page 36: Chapter 4 Probability Theory 4.1 What is Probability?

Trees

Consider how many ways a team can win or lose in a season…

Or how many sequences you can get if you toss a coin 3 times.

Or how many ways you can ride 4 particular roller coasters at Great Adventure.

A tree diagram allows you to look at all possibilities.

Page 37: Chapter 4 Probability Theory 4.1 What is Probability?

Trees (cont)

Lets set up a tree for that last situation.

The choices are El Toro, Rolling Thunder, Superman the Ultimate Flight, and Kinda Ka.

Page 38: Chapter 4 Probability Theory 4.1 What is Probability?

Trees (cont)

By labeling each branch with an appropriate probability, you can use the tree diagram to compute probability of a particular outcome.

In the reading there will be an example that discusses pulling balls out of urns.

Write the probabilities as fractions on each “branch” and then use the concepts from last section to compute P(A and B)

Page 39: Chapter 4 Probability Theory 4.1 What is Probability?

Application

According to a study by the Harvard School of Public Health, 44% of college students engage in binge drinking, 37% drink moderately and 19% abstain entirely. Another study published in the American Journal of Health Behavior, finds that among binge drinkers aged 21 to 34, 17% have been involved in alcohol related automobile accidents while among non-bingers of the same age, only 9% have been involved in such accidents.

What is the probability that a randomly selected college student will be a binge drinker that has had an alcohol related car accident?

Page 40: Chapter 4 Probability Theory 4.1 What is Probability?

We could do this with conditional probability

(That is, finding the probability of selecting someone who is a binge drinker AND a driver with an alcohol related accident)

Lets look at it from a tree point of view – this is sometimes organizationally a good way to consider…

It also is a good way to solve a problem that asks more than one question…

Page 41: Chapter 4 Probability Theory 4.1 What is Probability?

Going backwards

What if you instead wanted to know if a student has an alcohol related accident, what is the probability that the student is also a binge drinker?

Remember

Page 42: Chapter 4 Probability Theory 4.1 What is Probability?

Going backwards

What if you instead wanted to know if a student has an alcohol related accident, what is the probability that the student is also a binge drinker?

Remember

P(A and B)

P(A B) P(A,given B)P(B)

Page 43: Chapter 4 Probability Theory 4.1 What is Probability?

Tree gives P(accident | binge) but we want P(binge |accident)

Using the above formula, P(binge |accident) = P(binge and accident) P(accident)=.075/.108 (remember the

tree?)=69%

P(A and B)

P(A B) P(A,given B)P(B)

Page 44: Chapter 4 Probability Theory 4.1 What is Probability?

Trees (cont)

Why does this work? The Fundamental Theorem of Counting saysIf there are m1 ways to do a first task, m2

ways to do a second task, m3 ways to do a third task…… mn ways to do the nth task,

then the total possible “patterns” or ways you could do all the tasks is

m1· m2· m3...mn

Page 45: Chapter 4 Probability Theory 4.1 What is Probability?

Permutations

Now is the time we can introduce a few new mathematical operators (that you should already know)

! is called the factorial symbol

n! = n(n-1)(n-2)(n-3)…..13! = 3(2)(1) = 65! = 5(4)(3)(2)(1)= 1200! = 1

Calculators use a special formula to compute factorials; this is a large number formula but as result your calculator will give you an answer for 1.5! which is false

Page 46: Chapter 4 Probability Theory 4.1 What is Probability?

Permutations (cont)

So what is a permutation?

A permutation of “n” elements taken “r” at a time is an ordered arrangement (without repetition) of r of the n elements and it is called nPr.

Page 47: Chapter 4 Probability Theory 4.1 What is Probability?

Permutations (cont)

So what is a permutation? A permutation of “n” elements taken “r” at a

time is an ordered arrangement (without repetition) of r of the n elements and it is called nPr.

The thing to remember is that ORDER MATTERS!!

n r

n!P

(n r)!

Page 48: Chapter 4 Probability Theory 4.1 What is Probability?

How to recognize a permutation problem

The wording will imply somehow that order matters.

In how many different ways can you ride 5 out of 11 of the max rated rides at Great Adventure?

“different ways” means order matters

Page 49: Chapter 4 Probability Theory 4.1 What is Probability?

Combinations

What if order doesn’t matters?

How many combinations of 5 of the 11 max rated rides at Great Adventure are there?

Groupings, in which order doesn’t matter, are called combinations.

Smaller or larger?

Page 50: Chapter 4 Probability Theory 4.1 What is Probability?

Combinations (cont)

It looks like a permutation formula but with one crucial difference.

A Combination n elements, r at a time, is equal to

Dividing by r! gets rid of overlap

n!

r!(n r)!