Embed Code - If you would like this activity on your web page, copy the script below and paste it into your web page.

Normal Size Small Size show me how

Normal Size Small Size show me how

# Stats Exam #2

### Chapter 4-5

Question | Answer |
---|---|

definition of experiment | Process by which a measurement is taken or observations are made |

Examples of experiment | Flipping a coin or rolling a die |

definition of outcome | the result of an experiment |

examples of outcomes | Heads or rolling a 3 |

definition of sample space | the listing of all possible outcomes |

Example of sample space flipping a coin | S={H,T} |

Definition of event | an outcome or a combo of outcomes |

Example of event | even number of rolling a die |

Property 1 | "A probability is always a numerical value between 0 and 1" |

Property 2 | "The sum of probabilities for all outcomes of an experiment is equal to exactly 1" |

Empirical approach to probability | experimental |

Theoretical approach to probability | Classical (dont actually do the experiment) |

Subjective approach to probability | Expression of confidence (wheatherman) |

Empirical probability of A= | number of times A occured/ number of trials |

Law of Large Numbers | The more an even occurs the more the theoretical probablility is true |

Theoretical probability of A= | number of times A occurs in sample space/ number of the elements in the sample space |

odds in favor of event A | a to b or a:b |

odds against event A | b to a or b:a |

Proability of event A= | a/a+b |

Probability of event A will not occur= | b/a+b |

definition of conditional probability | Probability of an event GIVEN another event has occured |

definition of complimentary evnet | The compliment of A, Abar is the set of all sample points in the sample space that does not belong to event A |

Example of complimentary events | If A is heads the Abar is tails |

Formula for complimentary events | probability of A compliment= one- probability of A |

general addition rule: P(A or B)= | P(A)+ P(B)+ P(A and B) |

general multiplication rule: P(A and B)= | P(A) x P(B given A) |

definition of mutually exclusive events | Event that share no common elements |

Example of mutually exclusive events | heads or tails, red or black cards, number 2 and 5 |

P(A and B) in a mutually exclusive event | 0 |

Definition of an independent event | The occurrence or nonoccurrence of one gives us no information about the likliness of occurrence for the other. |

Formula for an independent event | P(A)= P(A given B)= P(A not given B) |

definition of dependent events | Occurrence of 1 event does have an effect on the probability of occurrence of the other event |

Special Multiplication rule | In 2 independent events P(A and B)=P(A) x P(B) |

definition of random variables | A variable that assumes a unique numerical value for each of he outcomes in the sample space of a probability experiment |

example of random variable | x={0,1,2} |

definition of discrete random variable | A quantitative random variable that can assume a countable number of values. |

definition of continuous random variable | A quantitative random variable that can assume an uncountable number of values. |

example of discrete random variable | number of heads when we flip a coin 10 times |

example of continuous random variable | distance from earth center to sun center. |

definition of probability distribution | A set of probabilities associated with each of the values of a random variable. it is a theoretical distribution used to represent populations |

ways to determine if there is a probability distribution | 1) each probability is between 0 and 1 2) the sum of the probabilities is 1 |

definition of probability function | A rule P(x) that assigns probabilities to the values of the random variable x |

sigma squared | variance |

sigma | standard deviation |

MU | mean |

definition of binomial experiment | an experiment with only 2 outcomes (success of failure). the trials are independent. p= success. |

MU in a binomial distribution | np |

variance in a binomial distribution | np(1-p) |

Created by:
o123runner321o