1. The problem statement, all variables and given/known data Two problems: 1) We're given a probability distribution function with possible values and their probabilities of occurring: X=1, P = .67 X=2, P = .19 X=3, P = .05 X=4, P = .04 X=5, P = .03 X=6, P = .02 And we need to find P(XBAR >=6) and P(XBAR >=5). I don't get this XBAR business... 2) Use Central Limit Theorem. Infinite population. 25% of the pop has the value 1, 25% 2, 25% 3, and 25% 4. What is the pop mean, pop std dev, sample mean, and sample std dev? 2. Relevant equations 1) I found that mu = E(X) = 1.69, variance = 1.494, std dev = 1.222, so mu of XBAR = 1.69 too, and std dev XBAR = stddev/(sqrt(n)) = .5 So I figure you must need to get the Z statistic for XBAR, right? That must mean P(Xbar >=6) = P(x=6) = (Xbar - mu of XBAR)/(stddev of XBAR) = 6 - 1.69/.5 however, this value is too big for the Z / normal distribution table I've been given. What do I do about the Xbar crap? Same for the XBAR >= 5, the Z value is too big... 2) The pop and sample mean are both 2.5, but shouldn't the std dev for an infinite pop be 0? 3. The attempt at a solution See above. Thanks.