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Basic Models of Probability. Instructor: Ron S. Kenett Email: [email protected] Course Website: www.kpa.co.il/biostat Course textbook: MODERN INDUSTRIAL STATISTICS, Kenett and Zacks, Duxbury Press, 1998. Course Syllabus. Understanding Variability Variability in Several Dimensions

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slide1

Basic Models of Probability

Instructor: Ron S. Kenett

Email: [email protected]

Course Website: www.kpa.co.il/biostat

Course textbook: MODERN INDUSTRIAL STATISTICS,

Kenett and Zacks, Duxbury Press, 1998

(c) 2000, Ron S. Kenett, Ph.D.

slide2

Course Syllabus

  • Understanding Variability
  • Variability in Several Dimensions
  • Basic Models of Probability
  • Sampling for Estimation of Population Quantities
  • Parametric Statistical Inference
  • Computer Intensive Techniques
  • Multiple Linear Regression
  • Statistical Process Control
  • Design of Experiments

(c) 2000, Ron S. Kenett, Ph.D.

slide3

The Paradox of the Chevalier de Mere - 1

Game A

Success = at least one “1”

(c) 2000, Ron S. Kenett, Ph.D.

slide4

The Paradox of the Chevalier de Mere - 2

Game B

Success = at least one “1,1”

(c) 2000, Ron S. Kenett, Ph.D.

slide5

The Paradox of the Chevalier de Mere - 3

Game A

Game B

P (Success) = P(at least one “1”)

P (Success) = P(at least one “1,1”)

Experience proved otherwise !

Game A was a better game to play

(c) 2000, Ron S. Kenett, Ph.D.

slide6

The Paradox of the Chevalier de Mere - 4

The calculations of Pascal and Fermat

Game A

Game B

P (Failure) = P(no “1”)

P (Failure) = P(no “1,1”)

P (Success) = .518

P (Success) = .491

What went wrong before?

(c) 2000, Ron S. Kenett, Ph.D.

slide7

P(outcomes add up to “10”) =?

To add or to multiply ?

(c) 2000, Ron S. Kenett, Ph.D.

slide8

1,1 1,2 1,3 1,4 1,5 1,6

2,1 2,2 2,3 2,4 2,5 2,6

3,1 3,2 3,2 3,4 3,5 3,6

4,1 4,2 4,3 4,4 4,5 4,6

5,1 5,2 5,3 5,4 5,5 5,6

6,1 6,2 6,3 6,4 6,5 6,6

P(outcomes add up to “10”) 36 / 3 =

(c) 2000, Ron S. Kenett, Ph.D.

slide9

Mutually Exclusive Events

Two events are mutually exclusive (or disjoint) if it is impossible

for them to occur together.

If two events are mutually exclusive, they cannot be independent

and vice versa.

Example:

A subject in a study cannot be both male and female, nor can

they be aged 20 and 30. A subject could however be both male

and 20, both female and 30.

(c) 2000, Ron S. Kenett, Ph.D.

slide10

Independent Events

Two events are independent if the occurrence of one of the

events gives us no information about whether or not the other

event will occur; that is, the events have no influence on each other.

If two events are independent then they cannot be mutually

exclusive (disjoint) and vice versa.

(c) 2000, Ron S. Kenett, Ph.D.

slide11

Example

Suppose that a man and a woman each have a pack of 52 playing

cards. Each draws a card from his/her pack. Find the probability

that they each draw the ace of clubs.

We define the events

A = \'the man draws the ace of clubs\'

B = \'the woman draws the ace of clubs\'

Clearly events A and B are independent so,

That is, there is a very small chance that the man and the woman

will both draw the ace of clubs.

(c) 2000, Ron S. Kenett, Ph.D.

slide12

Conditional Probability

In many situations, once more information becomes available, we

are able to revise our estimates for the probability of further

outcomes or events happening. For example, suppose you go out

for lunch at the same place and time every Friday and you are

served lunch within 15 minutes with probability 0.9. However,

given that you notice that the restaurant is exceptionally busy, the

probability of being served lunch within 15 minutes may reduce to

0.7. This is the conditional probability of being served lunch

within 15 minutes given that the restaurant is exceptionally busy.

(c) 2000, Ron S. Kenett, Ph.D.

slide13

The usual notation for "event A occurs given that event B has

occurred" is A|B (A given B). The symbol | is a vertical line and

does not imply division. P(A|B) denotes the probability that event

A will occur given that event B has occurred already.

A rule that can be used to determine a conditional probability

from unconditional probabilities is

P(A|B) = P(A andB) / P(B)

where,

P(A|B) = the (conditional) probability that event A will occur given

that event B has occurred already

P(A andB) = the (unconditional) probability that event A and event

B occur

P(B) = the (unconditional) probability that event B occurs

(c) 2000, Ron S. Kenett, Ph.D.

slide14

Binomial Distribution

X = Number of successes in n trials

n = 6, x = 0

n = 6, x = 1

n = 6, x = 3

0

x

n

(c) 2000, Ron S. Kenett, Ph.D.

slide15

Binomial Distribution

(c) 2000, Ron S. Kenett, Ph.D.

slide16

Binomial Distribution

13 16 20 11 12

10 12 20 16 15

10 12 9 18 12

11 11 9 11 14

13 8 4 13 12

14 11 14 15 12

18 13 7 11 9

15 11 8 11 16

9 12 12 18 15

13 9 15 12 12

(c) 2000, Ron S. Kenett, Ph.D.

slide17

Poisson Distribution

X = Number of occurrences of an event

events

x = 0

x = 2

x = 8

x

0

(c) 2000, Ron S. Kenett, Ph.D.

slide18

Poisson Distribution

(c) 2000, Ron S. Kenett, Ph.D.

slide19

Negative Binomial

(c) 2000, Ron S. Kenett, Ph.D.

slide20

Normal Distribution

(c) 2000, Ron S. Kenett, Ph.D.

slide21

Normal Distribution

N(0,1)

X P(<X) P(Xi< <Xi+1)

-3.0 0.001350 0.004432

-2.9 0.001866 0.005953

-2.8 0.002555 0.007915

-2.7 0.003467 0.010421

-2.6 0.004661 0.013583

-2.5 0.006210 0.017528

-2.4 0.008198 0.022395

-2.3 0.010724 0.028327

-2.2 0.013903 0.035475

-2.1 0.017864 0.043984

-2.0 0.022750 0.053991

-1.9 0.028717 0.065616

-1.8 0.035930 0.078950

-1.7 0.044565 0.094049

-1.6 0.054799 0.110921

-1.5 0.066807 0.129518

-1.4 0.080757 0.149727

-1.3 0.096800 0.171369

-1.2 0.115070 0.194186

-1.1 0.135666 0.217852

-1.0 0.158655 0.241971

-0.9 0.184060 0.266085

-0.8 0.211855 0.289692

-0.7 0.241964 0.312254

-0.6 0.274253 0.333225

-0.5 0.308538 0.352065

-0.4 0.344578 0.368270

-0.3 0.382089 0.381388

-0.2 0.420740 0.391043

-0.1 0.460172 0.396953

0.0 0.500000 0.398942

X P(<X) P(Xi< <Xi+1)

0.0 0.500000 0.398942

0.1 0.539828 0.396953

0.2 0.579260 0.391043

0.3 0.617911 0.381388

0.4 0.655422 0.368270

0.5 0.691462 0.352065

0.6 0.725747 0.333225

0.7 0.758036 0.312254

0.8 0.788145 0.289692

0.9 0.815940 0.266085

1.0 0.841345 0.241971

1.1 0.864334 0.217852

1.2 0.884930 0.194186

1.3 0.903200 0.171369

1.4 0.919243 0.149727

1.5 0.933193 0.129518

1.6 0.945201 0.110921

1.7 0.955435 0.094049

1.8 0.964070 0.078950

1.9 0.971283 0.065616

2.0 0.977250 0.053991

2.1 0.982136 0.043984

2.2 0.986097 0.035475

2.3 0.989276 0.028327

2.4 0.991802 0.022395

2.5 0.993790 0.017528

2.6 0.995339 0.013583

2.7 0.996533 0.010421

2.8 0.997445 0.007915

2.9 0.998134 0.005953

3.0 0.998650 0.004432

(c) 2000, Ron S. Kenett, Ph.D.

slide22

Normal Distribution

7.9006 11.5151 9.9542 9.4493 8.2387

10.4707 9.4041 9.3517 10.5664 10.9079

10.0077 12.5188 9.6937 10.0757 10.1616

10.2881 9.8560 10.0014 9.8467 11.5006

10.2982 9.6023 9.7238 11.5413 8.4595

9.2372 11.0408 12.8996 9.5590 9.1041

8.9170 9.7734 7.9844 8.3484 11.3703

10.6260 10.0952 11.4019 8.9842 9.3783

9.7574 7.9312 8.1566 9.9305 9.1158

8.6436 10.4689 9.3356 10.8788 7.8790

(c) 2000, Ron S. Kenett, Ph.D.

slide23

The t Distribution

(c) 2000, Ron S. Kenett, Ph.D.

slide24

The F Distribution

(c) 2000, Ron S. Kenett, Ph.D.

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