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slide1

Probability

The word probability derives from the Latin probare (to prove, or to test). Informally, probable is one of several words applied to uncertain events or knowledge, being closely related in meaning to likely, risky, hazardous, and doubtful. The theory of probability is the branch of mathematics that studies chances and the long-term patterns of random outcomes.

slide3

Have you ever wondered how gambling be business for the casino? It is a remarkable fact that the aggregate result of many thousands of random outcomes can be known with near certainty.

Individual gamblers can never say whether a day at the casino will turn a profit or a loss. But the casino is not gambling. It does not need to load the dice, mark the cards, or alter the roulette wheel. It knows that in the long run each dollar bet will yield it five cents or so of revenue.

slide4

The scientific study of probability is a modern development. It began with the study of chances in games and gambling.

The cofounders of probability theory, Pierre de Fermat and Blaise Pascal (1654) were proposed of the “Gambler’s Dispute” problem by a gambler, Chevalier de Méré, who wanted to know whether the payoff in a certain game is fair (more on this later).

Nowadays probability plays the key role in casino business, but it is also heavily used in insurance business, economics, and industry.

slide5

In our classroom however, we still use coins, cards, dice, and wheels as examples because their mathematical models are easy to define and their associated experiments can be performed hundreds of times in the classroom.

On the other hand, the mathematical model for life insurance companies is very complicated and we cannot perform experiments in the classroom to collect data for checking the correctness of that model.

slide6

Relative Frequency

If we flipped a normal coin 1000 times and observed that it landed on head 465 times, then we say that

“the relative frequency of getting a head is 465/1000 = 0.465” in these 1000 repetitions of the experiment.

In another terms, we can also say that the “empirical probability” of getting a head is 0.465 (in these 1000 repetitions of the experiment).

The original purpose of building a mathematical theory of probability is to find a formula that can predict the empirical probability of any event to a high accuracy. In addition, we want the prediction to be more accurate when the number of repetitions increases.

If we succeed, we can save many hours from repeating the same experiment millions of times.

slide7

Note:

Relative frequency = Empirical probability = Experimental probability

These are just different terms for the same thing.

slide8

Mathematical models for Probability

For any specific experiment (or random phenomenon) E, it sample space S is the set of all possible outcomes in that experiment.

Example 1. Flipping a coin

In this case, if we do not allow the coin to land on its edge, there will be only two outcomes.

Hence the sample space S = {Head, Tail}

(Note that the size of the sample space is 2, but the sample space itself is not 2.)

slide9

Mathematical models for Probability

For any specific experiment (or random phenomenon) E, it sample space S is the set of all possible outcomes in that experiment.

Example 2. Rolling a (6-face) die

If we observe just the face landing on top there will be 6 possible outcomes.

Hence the sample space

S = {1, 2, 3, 4, 5, 6}

(Again, note that the size of the sample space is 6, but the sample space itself is not 6.)

slide10

Mathematical models for Probability

For any specific experiment (or random phenomenon) E, it sample space S is the set of all possible outcomes in that experiment.

Example 3. Spinning a Roulette wheel

There are 38 slots for the ball to drop into.

Hence the sample space

S = {00, 0, 1, 2, 3, 4, 5, 6, …, 35, 36}

(Again, note that the size of the sample space is 38, but the sample space itself is not 38.)

slide11

A

2

3

10

4

9

8

5

7

6

Q

J

K

Mathematical models for Probability

For any specific experiment (or random phenomenon) E, it sample space S is the set of all possible outcomes in that experiment.

Example 4:

2 cards are drawn simultaneously from the following set.

The sample space will then be

S = {A&K, A&Q, A&J, A&10, A&9, A&8, …,

K&Q, K&J, K&10, …,

… … …

…, 4&3, 4&2, 3&2} and there should be 78 elements in S.

slide12

Mathematical models for Probability

A probability model for an experiment E is a mathematical description of E consisting of two parts: a sample space S and a way of assigning probability to its outcomes.

  • Rules
  • The probability of any outcome is a number between 0 and 1.(and the probability of an impossible outcome must be 0.)
  • All possible outcomes together must have probability 1.

Example 1. Flipping a coin.

If the coin is fair and the person is flipping the coin randomly, then we believe that the head is equal likely to land on top as the tail. Hence

p(Head) = p(Tail) =

slide13

Mathematical models for Probability

A probability model for an experiment E is a mathematical description of E consisting of two parts: a sample space S and a way of assigning probability to its outcomes.

  • Rules
  • The probability of any outcome is a number between 0 and 1.(and the probability of an impossible outcome must be 0.)
  • All possible outcomes together must have probability 1.

Example 2. Rolling a die.

If the die is fair, then each is equal likely to land on top. Hence

p(1 on top) = p(2 on top)= … = p(6 on top) =

slide14

Casino Dice are carefully machined, and their drilled holes, called pips, are filled with white material in density equal to the plastic body. This guarantees that the side with 6 pips has the same weight as the opposite side which has only one pip.

Thus each side is equally likely to land upward. All the odds and playoffs of dice games depends on this carefully planned randomness.

Dice balancing Caliper.

slide15

Mathematical models for Probability

An event A is any single outcome or a collection of outcomes in the experiment.

In other words, it is a subset of the sample space S.

The probability of an event A, p(A), is the sum of the probabilities of all the outcomes in A.

Example 1:

Let us roll a fair 6-face die, and let A be the event of getting an even number on top. Then

p(A) =

In particular, if every outcome in the experiment is equal likely to occur (which is very common assumption), then

p(A) =

slide16

Mathematical models for Probability

Example 2:

Let us drop a ball in to a turning roulette, and let A be the event of getting a number between 1 and 18 inclusive.

Since the roulette is almost perfectly balanced, every outcome in the experiment is equal likely to occur

p(ball landing on any specific number) =

Hence

p(between 1 and 18) =

slide17

Mathematical models for Probability

Example 3. Spinning the pointer of the following wheel.

The sample space S = {red, yellow, green, blue}

If the pointer is perfectly balanced and the bearing is very smooth, then it is equally likely to stop at any position, hence

p(red) =

p(blue) =

p(green) =

p(yellow) =

slide18

0.3

0.6

0

0.4

1

0.7

slide20

The law of Averages

(also called the Law of large numbers)

Consider an experiment E in which the theoretical probability of an event A is p. Suppose that the single trial of this experiment is repeated many times, and that the outcome of each trial is independent of the others.

If the number of trials increases, the experimental probability of A will approach the theoretical value p.

Example

Suppose that the theoretical prob of winning a game X is 26%, then

slide21

A famous puzzle

In America during the gold-rush era, a very ingenious gambling game garnered a lot of money for its perpetrators.

Three cards were placed in a hat:

one was gold on both sides,

one was silver on both sides, and

one was gold on one side and silver on the other side.

The gambler would take one card and place it on the table showing (for instance) gold on the top side of the card.

Then he would bet the on lookers even money that gold would be on the reverse side, his reasoning being that the card (on the table) could not be sliver/silver, hence there were only two possibilities: gold/silver or gold/gold. A fair and even bet, isn’t it?

slide22

State Lotteries

The most popular game in state lotteries is Lotto. By 2006, there are only 7 states without Lottos.

To play the California Super Lotto plus you need to pick 5 number from 1 to 47 and one mega number from 1 to 27.

Prior to June 6, 2000, the format was to pick 6 numbers from 1 to 49, hence the nick name 649. This format is still being used in many states.

Lottos are a bad bet, because the state pays out only about half of the money wagered. The only compensation almost all Lotto players receive is the pleasure of dreaming themselves rich.

slide23

Raffle vs. Lottery

  • there may not be a winner
  • the prob of winning is fixed
  • several tickets can share the same grand prize.
  • the chance of winning is usually extremely small.
  • there must be a winner
  • the prob of winning depends on the number of tickets sold
  • only one winner per prize
area models for probability
Area Models for Probability

When a student was walking across the room (with tiles on the floor as illustrated below), a small button fell off from her dress. What is the probability that the (center of the) button landed on a blue tile?

Answer:

Since there are totally 80 square tiles and 20 of them are blue, hence the probability of landing on a blue tile should be

slide25

5

π

5

=

16

π

16

The following target is made up of concentric circle with radii 1, 2, 3, and 4 units. If a dart was thrown randomly and hit the target, what is the probability that it hit the red ring?

Answer:

area of target = π(4)2 = 16 π

area of red ring = π(3)2 – π(2)2

= 5π

4

3

2

Hence

Prob(hitting red ring) =

tree diagrams
Tree Diagrams

In some experiments it is inefficient to list all the outcomes in the sample space. Therefore, we develop alternative procedures to compute probabilities such as drawing a tree diagram.

A Tree diagram is a diagram consisting of line segments connected like the branches and twigs of a tree. In particular, there is never a loop in a tree diagram.

The starting point of a tree diagram is called the root. Each branching point is called a node.The number of levels in a tree diagram is equal to the number of steps in the corresponding experiment.

slide27

H

H

T

H

H

T

T

H

H

T

T

H

T

T

3rd time

2nd time

1st time

Tree Diagram

for the experiment

“a coin is flipped 3 times”

start

slide28

H

H

H

H

H

H

H

H

H

H

H

H

H

H

H

H

H

H

T

T

T

T

T

T

T

T

T

T

T

T

T

T

T

T

H

H

T

T

H

H

H

H

T

T

T

T

H

H

H

H

T

T

T

T

H

H

T

T

T

T

A coin is flipped 5 times

H

start

T

The orange path represents the sequence of THTHH

slide29

Probability trees and one stage experiments

If we label each branch of the tree with the appropriate probability, then we get a probability tree.

red

A ball is drawn from the following jar at random.

2/9

3/9

green

start

4/9

blue

complex experiments and probability trees

4

2

2

4

3

1

2

2

4

3

3

9

9

8

8

8

8

8

8

8

8

8

3

9

Blue

Blue

Blue

Red

Red

Red

Green

Green

Green

Complex Experiments and Probability Trees

Start

A Jar contains: 2 red balls, 3 green balls, and 4 blue balls.

If two balls are taken out sequentially and randomly without replacement, what is the probability of getting two balls of the same color?

Blue

Red

Green

Reset

Reset

Reset

slide31

The Los Angeles Lakers and Portland Trailblazers are going to play a “best 2 out of 3” series. Suppose that the probability that the Lakers win an individual game with Portland is 3/5, draw a probability tree to show possible outcomes.

LA

3/5

LA

3/5

LA

3/5

2/5

P

P

2/5

3/5

LA

LA

3/5

2/5

P

P

2/5

2/5

P

multiplicative property of probability
Multiplicative Property of Probability

Suppose that an experiment consists of a sequence of simpler experiments. Then the probability of each final outcome is equal to the product of the probabilities of the simpler experiments that make up the sequence.

Example:

Suppose that we roll a regular die twice. Then

Prob(rolling a 3 followed by rolling a 5) = Prob(rolling a 3) × Prob(rolling a 5) = =

additive property of probability
Additive Property of Probability

Suppose that an event A is the union of two (or more) mutually exclusive simpler events A1, A2. Then Prob(A) = Prob(A1) + Prob(A2)

Example:

Suppose that we roll 2 dice simultaneously. Then Prob(getting a sum of 11) = Prob(rolling a 5 on the 1st die and rolling a 6 on the 2nd die) +

Prob(rolling a 6 on the 1st die and rolling a 5 on the 2nd die)

=

=

independent events
Independent Events
  • Two events A and B are independent events if the occurrence of either event will in no way affect the probability of occurrence of the other.
  • Examples:
  • Event A is rolling a sum of 7 from a pair of dice, and event B is flipping a head in a coin.
  • Event A is winning the super lotto, event B is winning in a horse race in Del Mar.

Multiplication rule

If events A and B are independent, then the probability that both events occur (either simultaneously or one after the other) is

P(A and B) = P(A)×P(B)

slide36

"A gambler's dispute in 1654 led to the creation of a mathematical theory of probability by two famous French mathematicians, Blaise Pascal and Pierre de Fermat. Antoine Gombaud, Chevalier de Méré, a French nobleman with an interest in gaming and gambling questions, called Pascal's attention to an apparent contradiction concerning a popular dice game. The game consisted in throwing a pair of dice 24 times; the problem was to decide whether or not to bet even money (i.e. 1 to 1 payoff) on the occurrence of at least one "double six" during the 24 throws.

A seemingly well-established gambling rule led de Méré to believe that betting on a double six in 24 throws would be profitable, but his own calculations indicated just the opposite.

section 11 3 additional counting techniques
Section 11.3 Additional Counting Techniques

Permutations

An ordered arrangement of objects is called a permutation.

For example, the permutations of the letters C,A,S,T are

ACST CAST SACT TACS

ACTS CATS SATC TASC

ASCT CSAT SCAT TCAS

ASTC CSTA SCTA TCSA

ATCS CTAS STAC TSAC

ATSC CTSA STCA TSCA

You can see that there are 4×3×2×1 = 24 many permuations.

section 11 3 additional counting techniques38
Section 11.3 Additional Counting Techniques

Fundamental Counting Property

If an event A can occur in r ways, and for each of these r ways, an event B can occur in s ways*, then event A and B can occur, in succession, in r×s ways.

* this condition can also be rephrased as “the number of outcomes in event B is independent of event A.”

Example

Suppose that in a local diner, a supper consists of a starter, an entrée, and a beverage.

If there are 3 choices for the starter, 5 choices for the entrée, and 7 choices for beverages, how many different suppers can be created?

slide39

Examples

1. The license plates in Utah consist of 3 digits followed by 3 letters. How many such license plates are possible?

Passenger vehicle License Plates in California

If the 1st and the 3rd letters cannot be an I and O, how many possible combinations are there?

slide40

2. Given the set of digits {5, 6, 7, 8, 9}, how many 4-digit numbers can be formed such that a) the digits are different? b) the digits are different and the number is divisible by 5? c) the digits are different and the number is > 6000?

d) the digits are different and the number is < 8000?

slide41

Theorem

The number of permutations for n different objects is 1×2×3×4× ··· × n

The factorial notation

The product 1×2×3×4× ··· × n is called nfactorial and is written as n!.

Examples

1! = 1

2! = 1×2 = 2

3! = 1×2×3 = 6

……

10! = 3,628,800

Remark: 0! is defined to be 1.

slide42

Examples

1. Miss Murphy wants to seat her 12 students in a row for a class photo. How many different seating arrangements are there?

Answer: 12!

2. Seven of Miss Murphy’s students are girls and 5 are boys. In how many different ways can she seat the 7 girls on the left, then the 5 boys on the right?

Answer: 7! × 5!

slide43

Permutations of a set of objects taken from a larger set

Example:

In a certain lottery game, four different digits are taken from the digits 1 to 9 to form a 4-digit number. How many different numbers can be made?

Answer: 9×8×7×6

This answer can also be written as 9!/5!

Theorem

The number of permutations of r objects taken from n (≥ r) objects is

nPr =

combinations
Combinations

A collection of objects, in no particular order, is called a combination.

Example

Suppose that there are 5 ingredients – Pepperoni, sausage, green pepper, olive, and mushroom – three are chosen to make a pizza. How many possible combinations are there?

slide45

Pick 3 items from:

Pepperoni, Sausage, Green pepper, Olive, Mushroom.

PSG,

PSO,

PSM,

PGS,

PGO,

PGM,

POS,

POG,

POM,

PMS,

PMG,

PMO,

SPG,

SPO,

SPM,

SGO,

SGM,

SGP,

SOP,

SOG,

SOM,

SMP,

SMG,

SMO,

GPS,

GPO,

GPM,

GSO,

GSP,

GSM,

GOP,

GOS,

GOM,

GMP,

GMS,

GMO,

OPS,

OPG,

OPM,

OSP,OSG,

OSM,OGP,OGS,OGM,OMP,

OMS,

OMG,

MPS,

MPG,

MPO,MSP,MSG,

MSO,MGP,MGS,MGO,MOP,MOS,MOG,

We can see that every combination repeats 6 times. Hence we need to divide the answer by 6.

slide46

6

!

6

!

=

=

15

-

´

´

(

6

2

)!

2

!

4

!

2

!

20

!

20

!

=

=

125

,

970

-

´

´

(

20

12

)!

12

!

8

!

12

!

Theorem

The number of combinations of r objects chosen from n objects, where 0 ≤ r ≤n, is

[Note: Occasionally, nCr is denoted and read “n choose r”]

  • Examples
  • 6C2 =
  • 20C12 =
slide47

Pascal’s Triangle

0C0

1C01C1

2C02C12C2

3C03C13C23C3

4C04C14C24C34C4

. . . . .

1

1 1

1 2 1

1 3 3 1

1 4 6 4 1

1 5 10 10 5 1

slide48

Examples

1. A coin is tossed 7 times, how many ways are there to get 3 heads and 4 tails?

slide50

3. In a class of 16 girls and 14 boys, how many ways are there to form a committee of 5 girls and 4 boys?

slide51

4. From a standard deck of cards, how many ways can we make one pair?

Answer: (4C2)×13

5. From a standard deck of cards, how many ways can we make a flush poker hand? (a flush means all 5 cards in the same suit.)

Answer: (13C5)×4

slide52

Counting by Subtraction

Example 1.

In Mrs. O’Neill’s class, there are 20 students. If 16 are girls, how many are boys?

Example 2.

In a year of 365 days, if 104 days are weekends and holidays that you don’t work, how many day do you have to work?

slide53

Example 3.

Each student ID is a 5-digit number (including 00000), how many of these have duplicate digits?

slide54

Finding probability by subtraction.

Example 1.

In a certain raffle, the chance of winning is only 0.01%, what is your chance of losing?

Example 2.

The probability of raining tomorrow is 25%. What is the probability that there is no rain tomorrow?

slide55

Example 3

If 4 cards are drawn from a standard deck randomly, what is the probability that they are from different suits?

Example 4

If 4 cards are drawn from a standard deck randomly, what is the probability that some are from the same suit?

slide56

Permutations of not totally distinguishable objects

Suppose that you have3 indistinguishable yellow tulips,5 indistinguishable red tulips,

1 purple tulip,

2 indistinguishable pink tulips.How many different ways can you arrange them in a row?

slide57

11

!

´

´

´

3

!

5

!

1

!

2

!

Permutations of not totally distinguishable objects

Suppose that you have3 indistinguishable yellow tulips,5 indistinguishable red tulips,

1 purple tulip,

2 indistinguishable pink tulips.How many different ways can you arrange them in a row?

Answer:

Since there are totally 11 tulips we have

slide58

Another example of the same type

How many ways can we rearrange the letters in the word “MISSISSIPPI” to get a different string of 11 letters?

Answer: Since there are 11 letters totally, 1M, 4S’s, 4 I’s, and 2 P’s, there are

11

!

´

´

´

1

!

4

!

4

!

2

!

different arrangements.

mathematical expectations

4

48

´

-

´

»

$

10

$

0

.

50

$

0

.

308

52

52

Mathematical Expectations

The expected value of an experiment E is the average amount one expects to get when the experiment is repeated a large number of times.

Example

Suppose that in a certain game, you may draw a card from a standard deck. You will be paid $10 if you get an ace, otherwise you have to pay 50 cents.If you were to play this game a large number of times, what will be your average winnings per game? Should you play this game often for some extra income?

Answer: expected value =

and yes, you can play this game more often to get extra income.

slide60

Note:

The expected value is not necessarily a possible outcome in the experiment.

For example, the expected value of rolling a normal six-sided die is

and we know that it is impossible to roll a 3.5 from a normal die.

This is similar to the statistics that the “average number of children in an American family is 2.3”.

Mathematical expectation is an important application of probability in gambling, industry, insurance business, and many other practical fields.

We are going to see several examples right afterwards.

slide61

37

1

×

-

×

=

$

1

$

35

$

0

.

05

38

38

Example 1

A U.S. roulette wheel has 38 slots. If you bet on any single number such as “8”, the casino will pay you 35 to 1. In other words, if you bet $1 on the number “8”, and you win, the casino will return your $1 and pay you $35. If you lose, you lose $1.

What is the expected value of this game in the eyes of the casino?

Answer:

Expected value =

This means on average, the casino can get $0.05 for each dollar bet on wheel.

slide62

Example 2

Suppose that an insurance company has broken down yearly automobile claims for drivers from age 16 through 21, as shown in the table below. How much should the company charge as its average premium in order to break even on its cost for claims?

Answer:

Exp = $0×(0.80) + $2000×(0.10) + $4000×(0.05) + $6000×(0.03) + $8000×(0.01) + $10,000×(0.01)

= $760

slide63

Example 3

Walt, who is a realtor, knows that if he takes a listing to sell a house, it will cost him $1,000. However, if he sells the house, he will receive 6% of the selling price. If another realtor helps him to sell the house, he will get 3% of the selling price. If the house remains unsold after 3 months, he will lose the listing and receive nothing.

Suppose that the probability for selling $200,000 house are as follows:

prob(Walt sells the house) = 0.4

prob(another agent sells the house) = 0.2

prob(house unsold after 3 months) = 0.4

What is Walt’s expectation if he takes the listing?

Solution:

6% of $200,000 = $12,000

3% of $200,000 = $6,000

Expected value = $12,000×0.4 + $6,000×0.2 – $1,000

= $5,000