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Unformatted text preview: Probability: Joint: P(A or B) = P(A) + P(B) P(A & B) | Conditional : P(A|B) = P(A & B)/P(B) Coefficient of Variation: ( sample std dev/sample mean)*100% Shape of Distribution: mean < median: negative/left skewed | mean=median: symmetric (normal) | mean > median: positive/right skewed Expected Value of a Discrete Random Variable (E(X)) : X i = the i th outcome of the discrete random variable X; P(X i ) = probability of occurrence of the i th outcome of X; multiply for each X value and then sum for the mean. Variance of Discrete Random Variable ( 2 ) : 2 = (X-Expected Value) 2 (probability of X) sum all values of X Standard Deviation () : take the square root of the Variance Covariance ( xy ) : (X-Expected Value of X)(Y-Expected Value of Y)(probability of XY) sum all values of XY; positive covariance=positive relationship; negative covariance=negative relationship; Variance of the Sum of Two Random Variables : 2 X+Y = 2 X + 2 Y +2 2 XY BINOMIAL DISTRIBUTION Four Essential Properties : (1) sample consists of a fixed number of observations, n. (2) each observation is classified into one of two mutually exclusive and collectively exhaustive categories, usually called success and failure. (3) probability of an observation being classified as success, p , is constant from observation to observation. Thus, the probability of an observation being classified as failure, 1- p is constant over all observations. (4) The outcome, success or failure, of any observation is independent of the outcome of any other observation. To ensure independence, the observations can be randomly selected either from an infinite population without The outcome, success or failure, of any observation is independent of the outcome of any other observation....
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This note was uploaded on 07/07/2008 for the course COB 191 taught by Professor May during the Fall '07 term at James Madison University.
- Fall '07