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| 5865590562 | Conditions for Bernoulli Trials | 1. There are two possible outcomes (success and failure). 2. the probability of success, p, is constant. 3. the trails are independent. Ex. Flipping a coin; rolling a die and noting whether or not it came up as a six. | 0 | |
| 5865590563 | Geometric probability model | - Tells us the probability for a random variable that counts the number of Bernoulli trials UNTIL THE FIRST SUCCESS - Denoted by: Geom(p) |  | 1 |
| 5865590564 | Mean & Standard Deviation of Geometic model | Mean = 1/p SD = sqrt(q)/p | 2 | |
| 5865590565 | Binomial probability model | - Tells us the probabilty for a random variable that counts the NUMBER OF SUCCESSES in a fixed number of Bernoulli trials. - Denoted by: Binom(n,p) | 3 | |
| 5865590566 | Mean & Standard Deviation of Binomal model | Mean = np SD = sqrt(npq) | 4 | |
| 5865590567 | 10% Condition | Bernoulli trials must be independent. It is only okay to proceed if the sample is smaller than 10% of the population. | 5 | |
| 5865590568 | Success/Failure condition | - A binomal model is approximately Normal if we expect at least 10 successes and 10 failures: np > or equal to 10 and nq > or equal to 10 | 6 | |
| 5865590569 | Difference between Geometric and Binomial models | - Both involve Bernoulli trials, but the issues are different. - Geometric probability = trials until first success - Binomial probability = number of successes in a specified number of trials | 7 | |
| 5865590570 | Difference between Normal and Binomial models | - Binomial gives probabilities for a specific count - Normal gives continous random variable that can take place on any value | 8 |
