Ch 12; Introducing Probability Flashcards
Chance Behavior
Unpredictable short term but has a regular, predictable pattern long-term.
Randomness
Individual outcomes uncertain but still a regular pattern/ distribution of outcomes in a large number of repetitions
Probability
Probability of any outcome of a random phenomenon is the proportion of times the outcome would occur in a very long series of repetitions
Probability Model
A mathematical description of a random phenomenon consisting of 2 parts; a sample space (s) & a way of assigning probabilities to events
Sample Space (s)
Set of all possible outcomes
Event
An outcome/ set of outcomes of a random phenomenon (A subset of sample space)
Probability of Any Event Equation
(# of ways the event could occur) / (Total # of outcomes in the sample space)
Probability Rules (4)
1) The probability P(A) of any event (A) satisfies
0≤ P(A)≤ 1
2) If s is the sample space in a probability model then P(s)=1
3)Two events A & B are disjoint if they have no outcomes in common, so can never occur together.
If A & B are disjoint; P(A or B) = P(A) + P(B)
4) For any event A, P(A does not occur) = 1 - P(A)
Finite/Discrete Probability Model
A probability model w/ a finite sample space (All probabilities must be #s b/w 0 & 1, as well as add to 1)
Probability of any Event in a Finite Probability Model
The sum of the probabilities of the outcomes making up the event
Continuous Probability Models
Assigns probabilities as areas under a density curve. The area under the curve & b/w any range of values is the probability of an outcome in that range
Uniform Distribution
Same height over all intervals
Example of Continuous Probability Model
Normal Distributions
Random Variable
A variable whose value is a numerical outcome of a random phenomenon
Probability Distribution of a Random Variable X
Tells us what value X can take & how to assign probabilities to those values
