2011-09-08, 07:37 PM
Well, to add on to the topic, the general formula which I gave is to calculate something happening in exactly a certain amount of occurrences. (For example, the probability of rolling a 2 on a 6-sided die exactly 3 times out of 4.) You could also use this to calculate the probability of at least and at most events. For example, the probability of rolling a 2 on a 6-sided die at most 3 times out of 4. The at most includes rolling a two 0 times, 1 time, 2 times and 3 times. This could be done using the binomial probability distribution as well by using the formula for each number (in this cases there's 4; 0, 1, 2 and 3) and adding them together. Of course, this process can become rather tedious, which is why you'd probably want to use the Cumulative Distribution function mentioned earlier. I just find it interesting how they both work. (In case you were wondering, the probability of rolling a 2 at most 3 times in 4 rolls is 99.9%.)
Oh, also, the Cumulative Distribution only calculates "at most" situations. If you wanted to calculate an "at least" situation using it, you'd need to do "1 - at most value + P®"
So if you wanted to calculate the probability of rolling at 2 at least 3 times in 4 rolls, it'd be:
1 - .999 + .015 = .16%
I hope my explanations make sense. I feel like I tried to throw too much information into that one post... I'm a math major and can do stats all day.
Oh, also, the Cumulative Distribution only calculates "at most" situations. If you wanted to calculate an "at least" situation using it, you'd need to do "1 - at most value + P®"
So if you wanted to calculate the probability of rolling at 2 at least 3 times in 4 rolls, it'd be:
1 - .999 + .015 = .16%
I hope my explanations make sense. I feel like I tried to throw too much information into that one post... I'm a math major and can do stats all day.

