Reading...
Play button
Play button
Use LEFT and RIGHT arrow keys to navigate between flashcards;
Use UP and DOWN arrow keys to flip the card;
H to show hint;
A reads text to speech;
23 Cards in this Set
- Front
- Back
|
Addition Rule
|
If A and B are disjoint events, then the probability of A or B is P(AorB)=P(A) + P(B)
|
|
|
Complement Rule
|
The probability of an event occurring is 1 minus the probability that it doesn't occur:
P(A) = 1-P(A^C) |
|
|
Conditional probability
|
P(BIA) is read "the probability of B given A"
P(BIA)=P(A and B)/ P(A) |
|
|
Disjoint (or mutually exclusive) events
|
Two events are disjoint if they have no outcomes in common. If A and B are disjoint, then knowing that A occurs tells us that B cannot occur. AKA mutually exclusive
|
|
|
Empirical probability
|
When the probability comes from the long-run relative frequency of the event's occurrence, it is an empirical probability
|
|
|
Event
|
A collection of outcomes. Usually, we identify events so that we can attach probabilities to them. We denote events with bold capital letters.
|
|
|
General Addition Rule
|
For any two events, A and B, the probability of A or B is:
P(A or B) = P(A) + P(B) - P(A and B) |
|
|
General Multiplication Rule
|
For any two events, A and B, the probability of A and B is:
P(A and B) = P(A) x P(BIA) |
|
|
Independence (informally)
|
Two events are independent if the fact that one event occurs does not change the probability of the other
|
|
|
Independence (used formally)
|
Events A and B are independent when P(BIA) = P(B)
|
|
|
Joint probabilities
|
The probability that two events both occur
|
|
|
Law of large numbers (LLN)
|
States that the long-run relative frequency or repeated, independent events settles down to the true relative frequency as the number of trials increase
|
|
|
Marginal probability
|
In a joint probability table a marginal probability is the probability distribution of either variable separately, usually found in the rightmost column or bottom row of the table
|
|
|
Multiplication Rule
|
If A and B are independent events, then the probability of A and B is:
P(A and B) = P(A)xP(B) |
|
|
Outcome
|
The outcome of a trial is the value measured, observed, or reported for an individual instance of that trial
|
|
|
Personal probability
|
When the probability is subjective and represents your personal degree of belief
|
|
|
Probability
|
A number between 0 and 1 that reports the likelihood of the event's occurrence. A probability can be derived from a model (such as equally likely outcomes), from the long-run relative frequency of the event's occurrence, or from subjective degrees of belief. We write P(A) for the probability of the event A.
|
|
|
Probability Assignment Rule
|
The probability of the entire sample space must be 1:
P(S)=1 |
|
|
Random phenomenon
|
A phenomenon is random if we know what outcomes could happen, but not which particular values will happen
|
|
|
Sample space
|
The collection of all possible outcome values. The sample space has a probability of 1.
|
|
|
Theoretical probability
|
When the probability comes from a mathematical model (such as, but not limited to, equally likely outcomes)
|
|
|
Trial
|
A single attempt or realization of a random phenomenon
|
|
|
Tree diagram (or probability tree)
|
A display of conditional events or probabilities that is helpful in thinking through conditioning
|
