Study your flashcards anywhere!
Download the official Cram app for free >
 Shuffle Toggle OnToggle Off
 Alphabetize Toggle OnToggle Off
 Front First Toggle OnToggle Off
 Both Sides Toggle OnToggle Off
 Read Toggle OnToggle Off
How to study your flashcards.
Right/Left arrow keys: Navigate between flashcards.right arrow keyleft arrow key
Up/Down arrow keys: Flip the card between the front and back.down keyup key
H key: Show hint (3rd side).h key
A key: Read text to speech.a key
66 Cards in this Set
 Front
 Back
Probability is defined as...

The numeric value representing the change, likelihood or possibility that a particular even will occur.


An event that is certain to happen has a probability of

1


An event that has NO certainty to happen has a probability of

0


The three types of priority are...

A priori
Empirical Subjective 

A Priori is defined as...

Probability of an occurrence is based on prior knowledge of the process involved.


When each outcome is equally likely, the probability is known as...

A Priori


The equation for 'A Priori' is

# of ways the event can occur DIVIDED BY
Total # of possible outcomes 

A example of A priori is...

Choosing black or red from a deck of cards


Empirical Probability is defined as...

Probability that is based on observable data.


Probability that is based on observable data is known as...

Empirical Probability


An example of Empirical Probability is...

Surveys


Each possible outcome of a variable is referred to as an

event


This is described by a single characteristic

A simple event


Each side of a dice is described as

A simple event


An event with two or more characteristics is defined as

A Joint Event


Getting two heads when you toss a coin is an example of a

A Joint Event


The complement of something is represented by

An apostrophe (A' means complement of A)


What are the complements of the following....
*Heads on a coin *Five dots on a die 
*Tails
*Not getting five dots on a die 

The collection of all possible events is called the

Sample Space
(Heads & Tails on a coin) (One, Two, Three, Four, Five & Six on a die) 

Two things to help you visualize sample spaces are...

Contingency tables and Venn Diagrams


In a Venn Diagram, A U B means...

the total area of A and B


In a Venn Diagram, A n B means...

the intersection of A and B


A simple probability refers to the probability of occurrence of a

Simple Event


An event that consists of a set of Joint Probabilities is know as

Marginal Probability


When two events cannot occur at the same time it is called

Mutually exclusive


When there is a sex of events and one of the events must occur it is called

Collectively exhaustive


A coin toss is mutually exclusive because

You cant have heads and tails at the same time.


A coin toss is collectively exhaustive because

If heads does not occur, tails must occur


Male and Female, Heads and Tails are both what kinds of events

Mutually exclusive and Collectively exhaustive


When dealing with an OR problem with A and B as variables... the equation would look like..

P(A)+P(B)  P(A&B)


Explain the following statement...
P(AB) = P(A and B) / P(B) 
The Probability of B given A is equal to the probability of A and B divided by the probability of A


When dealing with an ALSO problem with A and B as variables... the equation would look like..

P(AB) = P(A and B) / P(B)


A decision tree is an alternative to

A Contingency table


When the outcome of one event does not affect the probability of occurrence of another even, the events are said to be

Independent


When dealing with dependence, if A and B percentages are equal then the events are...

Independent


When dealing with the Multiplication rule, multiply...

The first possibility times 1 the second. (this is due to the first probability being subtracted from the second portion of the equation)


A mathematical expression that defines the distribution of values for a continuous probability

Probability density function


The three types of distribution are...

Normal, Uniform and Exponential


This distribution is symmetrical and bell shaped.

Normal Distribution.


Normal Distribution has this type of visual...

Bell shaped and symmetrical


Most values tend to cluster around the mean when dealing with this type of distribution

Normal Distribution


During normal distribution, the vales tend to cluster around the...

Mean


During normal distribution, the mean is equal to the

Median


The Median is equal to the Mean during this type of distribution

Normal


There is normally no large positive or negative values when dealing with this type of distribution

Normal Distribution


A distribution that is shaped like a box is known as...

Uniform Distribution


During uniform distribution, each value has an equal probability of occurrence anywhere in the range of

The smallest and largest value.


Uniform distribution is symmetrical which means

The Mean and the median are equal


The skewed distribution is known as...

Exponential Distribution


When dealing with exponential distribution, it skews to the ______ and the _____is larger than the ________

Right, Mean is larger than the median


The range of an exponential distribution is...

Zero to positive infinity


When dealing with exponential distribution, large values are...

Unlikely!


When dealing with normal distribution, the probability of a single value and not a range is

ZERO


During normal distribution, the interquartile range is equal to

1.33 standard deviations


'e' is equal to

2.718


'π" is equal to

3.1415


"μ" is equal to

The Mean


"σ" is equal to

Standard deviation. the Stan


How do you calculate "σ"

(1) Find the mean
(2) Subtract the mean from each single number to get a list of deviations (3) Square all these deviations (4) Sum all these deviations (5) Divide by one less than the total # of #'s (6) SQUARE ROOT of this number 

How do you calculate "μ"

Calculated by adding up all the numbers and dividing by the # of #'s


"Z" is equal to

Any continuous variable where
INF < X < INF 

The transformation formula is...

Z= X  µ DIVIDED BY σ


Uniform Distribution, probability density function

1 DIVIDED BY b  a


Uniform Distribution, probability density function...A and B... which is the larger function

A is the MIN
B is the MAX 

Uniform Distribution, Mean of the distribution

µ = a+b DIVIDED BY 2


Uniform Distribution, standard deviation formula

σ = SQUAREROOT (ba)^2 DIVIDED BY 12
