Terms : Hide Images
| 8087174645 | Discrete | Countable | 0 | |
| 8087174646 | R.V. | Random variable | 1 | |
| 8087174647 | Random Variable | A Variable whose value is a numerical outcome of a random phenomenon. It is a function that assigns a probability to a numeric outcome experiment | 2 | |
| 8087174648 | Discrete Random Variable | X, is a random variable that has a countable number of possible values. Can be represented in a histogram or table, but not in a bell curve. | 3 | |
| 8087174649 | Probability distribution of X looks like... |  | 4 | |
| 8087174650 | Probability histogram | Can be used to display the probabilty of a distributin of a discrete R.V. | 5 | |
| 8087174651 | Continuous Random Variable | X, takes on all values within an interval of numbers. Isn't a set amount of outcomes. Cannot be represented in a histogram or table, but in a bell curve. | 6 | |
| 8087174652 | Density Curve | Continous R.V. | 7 | |
| 8087174653 | The area at any given # on a density curve is | 0 | 8 | |
| 8087174654 | To be a probability distribution... | 1) All values must add to 1 2) All values must be in between 0 and 1 | 9 | |
| 8087174655 | Given | 1) Normal 2) SRS 3) n= 4)p= 5)σ= | 10 | |
| 8087174656 | Probability Notation | N(p, σp) | 11 | |
| 8087174657 | Z-score formula | z=(p̂-p/ σp) | 12 | |
| 8087174658 | Expected Value | μx, the mean of a random variable (where X=R.V.) | 13 | |
| 8087174659 | Mean of a discrete random variable | μx=∑(xi)(pi) 1) xi = every value 2) pi = every probability | 14 | |
| 8087174660 | When a distribution is symmetric, the mean is... | The center of the distribution (μx = median) | 15 | |
| 8087174661 | If X is a random variable and Y = a+bx then μy... | =μ(sub)a+bX | 16 | |
| 8087174662 | If X and Y are 2 random variables then μ(sub)x+y... | =μx+μy | 17 | |
| 8087174663 | Variance of discrete random variable | σ²x=∑(xi-μx)²(pi) | 18 | |
| 8087174664 | Standard deviation of a discrete random variable | σx=the square root of ∑(xi-μx)²(pi) | 19 | |
| 8087174665 | Variance rules | 1) Variances always add (because it measures distance, not negative) 2) σ²x+y = σ²x + σ²y 3) σ²x-y = σ²x + σ²y (because they always add) | 20 | |
| 8087174666 | σx+y = | the square root of σ²x + σ²y | 21 | |
| 8087174667 | σx+y is not equal to σx + σy because | The variances must always be combined, and rooted, instead of comining the standard devitations | 22 | |
| 8087174668 | To combine standard deviations and variances, X and Y must be | Independent random variables (so that there is no overlap) | 23 | |
| 8087174669 | To combine the means, X and Y can be | Independent or dependent | 24 | |
| 8087174670 | Law of large numbers | 1) When there is a small # of observations the mean will be all over the place 2) A larger sample size will settle chaos and the sample value will get closer to μ |  | 25 |
| 8087174671 | Law of large numbers: When n increases, x bar | Gets closer to μ | 26 | |
| 8087174672 | Law of large numbers proves that... | Averaged results of many independent observations are both stable and predictable | 27 |
