BUS 204 QUIZ 2
Experiment
a process that produces a single outcome whose result cannot be predicted with certainty.
Discrete random variable
a random variable that can assume any value in an interval.
Discrete random variable
a random variable that can assume only a countable number of possible values.
Event
a collection of elementary events.
Binomial distribution
a distribution that gives the probability of x successes in n trials in a process. A trial that has only two possible outcomes: either a success or a failure, has a fixed number of identical trials, the trials of the experiment are independent from each other.
Joint event
a joint event is an event that has two or more characteristics (and).
Mathematical model
a mathematical model is a mathematical expression that consists of: dependent variable, independent variable(s), coefficients of each independent variable(s), error.
Standard normal distribution
a normal distribution that has a mean=0 and a standard deviation=1.
Dependent event
a variable whose values are thought to be a function of, or dependent on, the values of another variable called the independent variable.
Tree diagram
another way of organizing the events of an experiment that aids in calculations of probabilities.
Correlation coefficient
aquantitative measure of the strength of the linear relationship between two variables. The correlation ranges from -1.0 to +1.0.The correlation of +/1 1.0 indicates a perfect linear relationship. The correlation of 0 indicates no linear relationship.
The Probabilities of Outcomes
include: classical probability, relative probability, subjective probability.
Normal distribution
is a bell-shaped distribution, unimodal, symmetrical, mean=mode=median are equal.
Combination
is an arrangement of r objects selected from a single group of n possible objects where the order of the selected objects is not important.
Permutation
is any arrangement of r objects selected from a single group of n possible objects.
Relative frequency of occurrence
is based on actual observations. P€ = Number of times E occurs/N. Where E = the event of interest and N = number of trials.
Classical probability assessment
is based on the assumption that the outcomes of an experiment are equally likely.
The value of "e"
is the exponential distribution equation is not a constant.
Independence
is the occurrence of one event has no effect on the probability of the occurrence of another event.
Poisson distribution
it is often used to describe situations such as the number of customers who arrive at a hospital emergency room per hour.Discrete distribution.
Exponential distribution
it is used to measure the time that elapses between two occurrences of an event. For example, the time between arrivals of customers at a bank.
Continuous random variable
random variable that can assume any valuein the interval, vary from trial to trial, can assume from the infinite number ofvalues.
Continuous random variable
random variables that can assume any value in an interval.
Expected value
the average value when the experiment that generates values for the random variable is repeated over the long run.
Probability
the chance that a particular event will occur. The probability of an event will be a value in the range of 0 to 1. Anything between 0 and 1 reflects the uncertainty of the event occurring.The probability is a numeric value from 0 to 1 that represents the chance, likelihood, or possibility that a particular event will occur.
Sample space
the collection of all elementary outcomes that can result from a selection, decision, or experiment.
Complement of an event
the complement of event A (represented by symbol A') includes all events that are not part of A.
Subjective probability assessment
the method that defines probability of an event as reflecting a decision maker's state of mind regarding the chances that the particular event will occur.In other words: shooting from the hip, best guess, based on experience and intuition.
Elementary event
the most rudimentary outcomes resulting from a simple experiment
Uniform probability distribution
the probability overany interval of the continuous random variable is the same as for any other interval of the same width.
Conditional probability
the probability that an event will occur given that some other event has already happened.
Random variable
the variable that takes on different numerical values based on chance.
Independent event
two events are independent if the occurrence of one event in no way influences the probability of the occurrence of the other event.
Mutually exclusive event
two events are mutually exclusive if the occurrence of one event precludes the occurrence of the other event.
Bayes' Theorem
used when new information requires that the prior probabilities are revised. Bayes' theorem is used to revise previously calculated probabilities based on new information.
