39 terms

Business Statistics Terms

A value between zero and one, inclusive, describing the relative possibility an event will occur
A process that leads to the occurence of one and only one of several possible observations
A particular result of an experiment
A collection of one or more outcomes of an experiment
Mutually Exclusive
The occurence of one event means that none of the other events can occur at the same time
Collectively Exhaustive
At least one of the events must occur when an experiment is conducted
Empirical or Relative Frequency
The probability of an event happening is the fraction of the time similar events happened in the past
Empirical Probability= # of Times Event Occurs/Total Number of Observations
Law of Large Numbers
Over a large number of trials, the empirical probability of an event will approach its true probability
Subjective Probability
The likelihood of a particular event happening that is assigned by an individual based on whatever information is available
Special Rule of Addition
P(A or B) = P(A) + P(B)
Compliment Rule
P(A)= 1-P(~A)
Joint Probability
A probability that measures the likelihood two or more events will happen concurrently
General Rule of Addition
P(A or B) = P(A) + P(B) -P(A and B)
The occurence of one event has no effect on the probability of the occurence of another event
Special Rule of Multiplication
P(A and B) = P(A)P(B)
Conditional Probability
The probability of a particular event occurring, given that another event has occurred
General Rule of Multiplication
P(A and B) = P(A)P(B/A)
Contingency Table
A table used to classify sample observations according to two or more identifiable characteristics
Prior Probability
The initial probability based on the present level of information
Posterior Probability
A revised probabilty based on additional information
Multiplication Formula
If there are m ways of doing one thing and n ways of doing another thing, there are m x n ways of doing both
Any arrangement of x objects selected from a single group of n possible objects
Probability Distribution
A listing of all the outcomes of an experiment and the probability associated with each outcome
Characteristics of a Probability Distribution
1) The probability of a particular outcome is between 0 and 1
2) Outcomes are mutually exclusive events
3) The list is exhaustive- Sum of probabilities is 1
Random Variable
A quantity resulting from an experiment that, by chance, can assume different values
Discrete Random Variable
A random variable that can assume only certain clearly separated values
Mean of a Probability Distribution
The sum of x values times probability of its outcome
Variance of a Probability Distribution
Sum of (X values minus the mean) squared times the values expected probability
Uniform Probability Distribution
Rectangular in shape and is defined by minimum and maximum values
Normal Probability Distribution
Bell-Shaped, Symmetrical, Asymptotic (Closer and closer to the x axis but never touches it)
Z Value
The signed distance between a selected value, designated X, and the mean, divided by the standard deviation(X-Mean/Standard Deviation)
Binomial Probability Distribution
Widely occurring discrete probability distribution
4 Characteristics of a Probability Distribution
1) Outcome on each trial of an experiment either success of failure
2) Random variable counts the number of successes in a fixed number of trials
3) Probability of success and failure stay same for each trial
4) Trials are independent
Hypergeometric Probability Distribution
When probability of success does not remain teh same from trial to trial
4 Characteristics Hypergeometric Probability Distribution
1) Outcome on each trial of an experiment either a success or failure
2) Random variable is number of successes in a fixed number of trials
3) Trials are not independent
4) Proability of a success changes for each trial
Poisson Probability Distribution
Describes the number of times some event occurs during a specified interval
3 Characteristics of a Poisson Probability Experiment
1) Random Variable is number of times some event occurs during a defined interval
2) Probability of the event is proportional to size of the interval
3) Intervals do not overlap and are independent
Tree Diagram
Graph that is helpful in organizing calculations that involve several stages
Bayes Theorem
Formula to arrive at the probability God does exist based on evidence available to him on earth