| A | B |
|---|
| Probability Model | A function that associates a probability P with each possible outcome for a discrete random variable X, or a interval of possible values for a continuous random variable. |
| Disjoint events | Events that share no outcomes. Knowing that one event has occurred tells us that another event cannot have occurred. |
| Dependence | When knowing whether one event has occurred does alters the probability that another event has occurred. |
| Expected value | The theoretical long run average value found my adding up the products of all possible outcomes and the probability of the outcomes. Acts as the center of the model. |
| Variance | Measure the deviations of outcomes from the expected value by adding up the square of the difference of an outcome from the expected value, and then multiply each square by the probability of the outcome. |
| Random Variable | Has one of several possible values depending on the outcome of a random event. |
| Law of Large Numbers | The long-run relative frequency of repeated independent events settles down to the true relative frequency as the number of trials increases. |
| Independence | When knowing whether one event has occurred does not change the probability that another event has occurred. |
| Event | A collection of outcomes. |
| Standard deviation | Is the square root of the variance. |
| Outcome | The value measured, observed, or reported for a trial. |
| Sample space | The collection of all possible outcome values. |
| Disjoint events | wo events that have no outcomes in common |
| Discrete Random Variable | A random variable that has a countable number of possible outco |
| Trial | A single attempt or realization of a random phenomenon |
| Continuous Random Variable | A random variable that can take on an infinite number of possible outcomes, possibly within a range of specified values. |
| Probability | A number between 0 and 1 that reports the likelihood of an event's occurrence. |
