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Experiment
A process that leads to the occurrenceof one and only one of several possible observations.
Ex: Rolling a die and recording the number of spots on the top face of the die

Outcome
A particular result of an experiment. ␣ Ex: rolling a 3 on a dice is an outcome

Event
A collection of one or more outcomes of an experiment. Ex: 1,4,5

Sample space
The collection of all outcomes of an experiment␣ Denoted by S Ex:Is sample space an event? Yes.

Probability
  A number between 0 and 1
  Reflects the likelihood of occurrence of an event

Classical Probability
 Assumption:
 Definition: Number of outcomes leading to the event divided by the total number of possible outcomes
 Examples: probability of a head on a coin flip
 probability of a 3 on a die roll =
 Probability of S, P(S) =
 Properties: determined a priori
 objective

Empirical Probability
Definition: Number of times an event occurred divided by thetotal number of trials
Properties: determined a posteriori

Intersection of Events
The intersection of two events, A & B, contains only the common outcomes in both events.
Denoted as A∩B
Examples: A = {1, 2, 4, 7, 9}, B = {2, 3, 4, 5, 6} A∩B=␣ A = {Blue, Purple, Red, White}, B = {Green, Red, Yellow} A∩B=

Mutually Exclusive Events
 Events with no common outcomes
 Occurrence of one event excludes occurrence of theother

Union of Events
The union of two events, A & B, contains all the outcomes in both events.
Denoted as A∪B
Examples:
A = {1, 2, 4, 7, 9}, B = {2, 3, 4, 5, 6}
A∪B= 1,2,3,4,5,6,7,9
A
A = {Blue, Purple, Red, White}, B = {Green, Red, Yellow}
A∪B= Red

Partition of Sample Space
Events are both mutually exclusive and collectively exhaustive

Complementary Events
The complement of an event A is the event that A does not occur
Denoted by Ac P(A) + P(Ac) = 1
A and Ac form a partition.

Addition Law
 P(A ∪ B) = P(A) + P(B) – P(A ∩ B)

Addition Law; Mutually Exclusive Events Case
 If A and B are mutually exclusive, then P(A ∪ B) = P(A) + P(B)

Independent Events
Occurrence of one event does not affect the occurrence/nonoccurrence of the other event
 The conditional probability of A given B is equal to the marginal probability of A.
 P(A  B)=P(A)
Likewise, P(B  A)=P(B)
 The pizza represents the sample space
 You are blindfolded and asked to select a slice of
 pizza
 Event A = your slice contains pepperoni
 Event B = your slice contains mushroom


Conditional Probability
P(A B) = P(A∩B) / P(B)

Multiplication Law
P(A∩ B) = P(A)⋅P(B  A)

Multiplication Law; Independent Events Case
If A and B are independent, then
P(A ∩ B) = P(A)· P(B)

Random Variables (R.V)
a variable whose value may change from one experimental unit to another

Discrete Distributions
 Discrete r.v. — takes on a finite or countably infinite number of possible values
  Number of bad checks received by a restaurant
  Number of absent employees on a given day␣
Discrete distribution — probability distribution of a discrete r.v.

Continuous Distributions
 Continuous r.v. — takes on values at every point over a given interval
  Elapsed time between arrivals of bank customers
  Percentage of the labor force that is unemployed
Continuous distribution — probability distribution of a continuous r.v.

Binomial Distribution
 Binomial Distribution
  Model experiment involving n independent identical trials
  Each trial results in either success (S) or failure (F)
 Examples
 ␣ Flipping a fair coin with “success = heads”
 ␣ Rolling a die with “success = six ”
 ␣ Conducting political opinion poll with “success = voting for a specific candidate”
 ␣ Define X as total # of successes in n trials

Normal Distribution
 Widely used to model natural characteristics ␣ height, weight, length ␣
 IQ scores ␣ years of life expectancy
 ␣ Many variables in business and industry are also normally distributed. ␣ annual cost of household insurance ␣ cost per square foot of renting warehouse space
 ␣ amount of fill in soda cans ␣ Also referred to as Gaussian distribution

