Chapter 2: Probability Concepts And Applications

Front 1

Bayes’ Theorem

Back 1

A formula that is used to revise probabilitiesbased on new information.

Front 2

Bernoulli Process

Back 2

A process with two outcomes in each of aseries of independent trials in which the probabilities of theoutcomes do not change.

Front 3

Binomial Distribution

Back 3

A discrete distribution that describesthe number of successes in independent trials of a Bernoulliprocess.

Front 4

Classical or Logical Approach

Back 4

An objective way of assessingprobabilities based on logic.

Front 5

Collectively Exhaustive Events

Back 5

A collection of all possibleoutcomes of an experiment.

Front 6

Conditional Probability

Back 6

The probability of one event occurringgiven that another has taken place.

Front 7

Continuous Probability Distribution

Back 7

A probability distributionwith a continuous random variable.

Front 8

Continuous Random Variable

Back 8

A random variable that canassume an infinite or unlimited set of values.

Front 9

Dependent Events

Back 9

The situation in which the occurrence ofone event affects the probability of occurrence of someother event.

Front 10

Discrete Probability Distribution

Back 10

A probability distributionwith a discrete random variable.

Front 11

Discrete Random Variable

Back 11

A random variable that can onlyassume a finite or limited set of values.

Front 12

Expected Value

Back 12

The (weighted) average of a probabilitydistribution.

Front 13

F Distribution

Back 13

A continuous probability distribution that isthe ratio of the variances of samples from two independentnormal distributions.

Front 14

Independent Events

Back 14

The situation in which the occurrenceof one event has no effect on the probability of occurrenceof a second event.

Front 15

Joint Probability

Back 15

The probability of events occurringtogether (or one after the other).

Front 16

Marginal Probability

Back 16

The simple probability of an event occurring.

Front 17

Mutually Exclusive Events

Back 17

A situation in which only oneevent can occur on any given trial or experiment.

Front 18

Negative Exponential Distribution

Back 18

A continuous probabilitydistribution that describes the time between customerarrivals in a queuing situation.

Front 19

Normal Distribution

Back 19

A continuous bell-shaped distributionthat is a function of two parameters, the mean and standarddeviation of the distribution.

Front 20

Poisson Distribution

Back 20

A discrete probability distributionused in queuing theory.

Front 21

Prior Probability

Back 21

A probability value determined beforenew or additional information is obtained. It is sometimescalled an a priori probability estimate.

Front 22

Probability

Back 22

A statement about the likelihood of an eventoccurring. It is expressed as a numerical value between 0and 1, inclusive.

Front 23

Probability Density Function

Back 23

The mathematical functionthat describes a continuous probability distribution. It isrepresented by f(X).

Front 24

Probability Distribution

Back 24

The set of all possible values of arandom variable and their associated probabilities.

Front 25

Random Variable

Back 25

A variable that assigns a number to everypossible outcome of an experiment.

Front 26

Relative Frequency Approach

Back 26

An objective way ofdetermining probabilities based on observing frequenciesover a number of trials.

Front 27

Revised or Posterior Probability

Back 27

A probability value that resultsfrom new or revised information and prior probabilities.

Front 28

Standard Deviation

Back 28

The square root of the variance

Front 29

Subjective Approach

Back 29

A method of determining probabilityvalues based on experience or judgment.

Front 30

Variance

Back 30

A measure of dispersion or spread of the probabilitydistribution.