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Formative Review #2

Probability of an event is the proportion of times the event occurs in the long run, as a probability experiment is repeated over and over again. (Can only be positive numbers.)
Law of large numbers is another way to state our definition of probability.
Sample Space collection of all the possible outcomes of a probability experiment
Event a collection of outcomes of a sample space
Compound Event is an event that is formed by combining two or more events
simple event any single outcome from a probability experiment
Classical probability the frequency of an event occurs divided by total number of possible outcomes. This can only be used when all events are equally likely.
Empirical probability the ratio of the number of outcomes in which a specified event occurs to the total number of trials, not in a theoretical sample space but in an actual experiment.
Relative frequency the ratio of the number of times an event occurs to the number of occasions on which it might occur in the same period.
Complement The complement of an event A is the event that A does not occur. (The events “Rain” and “No rain”)
Addition rule The method of subtracting in order to adjust for double counting
mutually exclusive impossible for both events to occur.
Multiplication rule Use the General Multiplication Rule to compute probabilities of the form P(A and B).
independent events if the occurrence of one does not affect the probability that the other event occurs
dependent events If two events are not independent
permutations r items chosen from n items is an ordering of the r items. It is obtained by choosing r items from a group of n items, then choosing an order for the r items
combinations Each distinct group of objects that can be selected, without regard to order
A discrete random variable assigns probabilities to its inputs.
The sum of the probabilities of a discrete random variable is equal to one.
The expected value of a discrete random variable is equal to the mean.
Probabilities are numbers that range between zero and one (0  P(A)  1).
The multiplication rule can be used to find probabilities involving rolls of dice, how many different car keys can be made from a single key blank, the odds of getting true-false questions correct, or probability of restaurant reservations (all independen
Combinations can be used to find the odds of winning the lottery or how many groups can be formed.
The binomial probability formula can be used to find the probability of getting a certain number of girls/boys in a given number of births or number of defective items in a lot.
Permutations can be used to find the number of different seating arrangements possible or the number of different batting line-ups that are possible for a baseball team.
The range a probability will fall within is 0 < P(x) < 1
A probability distribution gives the probability for each value of the random variable True
A discrete random variable has either a finite number of values or a countable number of values. True
Arrangements or sequences where order does not matter are called permutations. False
The Empirical approach to probability is when you run an experiment and check the outcome to estimate the probability of a similar experiment. True
If a garage door opener has one set of 10 switches where the switches could be in one of three positions, a person could set all possible positions in less than ten minutes.(one position per second). False
A person can be convicted at a legal trial exclusively on probabilities. False
Random variable a numerical outcome of a probability experiment.
Discrete random variable are random variables whose possible values can be listed. The list may be infinite — for example, the list of all whole numbers
Continuous random variable are random variables that can take on any value in an interval. The possible values of a continuous variable are not restricted to any list.
probability distribution for a discrete random variable specifies the probability for each possible value of the random variable.
Expected value Another name for the mean of a random variable
Created by: rjoljo82