desmond iking said:
sorry . it should be 10 defective ones, do you mean 92C2 is for replacement case? which means after I pick 1 item out of 92 items , then when i choose the 2nd item, i still have 92 options , which means the first ball chosen is put back ?
can i do on this way?
(10C1 x 9C1)/100C2) +( 10C2/100C2) = 0.1909 , but by doing so , i am assuming , the selection is with replacement am i right? but suprisingly, i also gt the same ans
This is a problem involving the
hypergeometric distribution; it is something you will see over and over again, so is worth learning about once and for all. You already have the germ of the idea in some of your workings.
If you have a population of ##N_1## items of type 1 and ##N_2## items of type 2 (with ##N = N_1 + N_2## items altogether), say you pick ##n## items at random without replacement. Then the probability of having ##k## type-1 items in your sample is
P\{ k\: \text{ type 1 }\} = \frac{{N_1 \choose k} {N_2 \choose n-k}}{{N \choose n}}= \frac{_{N_1}C_k \, \times \, _{N_2}C_{n-k}}{_NC_n}
This is not hard to get: what is the probability of 11...122...2 in that specific order (with k type 1s and n-k type 2s)? Well, it is
p = \frac{N_1}{N} \frac{N_1 - 1}{N-1} \cdots \frac{N_1 - k+1}{N - k+1} <br />
\frac{N_2}{N-k} \frac{N_2-1}{N-k-1} \cdots \frac{N_2 - (n-k)+1}{N- n+1}
The probability of any other string of k 1s and (n-k)2s is the same; so for example, the probability of 1221...122211...2 is also p, etc. This is because for each such string, when we compute the probability we have numerators ##N_1, N_1-1, \ldots, N_1 - k+1## and ##N_2, N_2 -1 , \ldots, N_2 - (n-k)+1## all multiplied together in some order, and we have denominators ##N, N-1, \ldots N-n+1## all multiplied together. So, the total numerators and denominators are the same, hence the probability is p. So, to get the probability of k type Is we need only multiply p by the number of different strings of k 1s and n-k 2s, which is ##_nC_k##. By manipulation, you end up with the formula given above.
For more details about the hypergeometric distribution, see, eg.,
http://en.wikipedia.org/wiki/Hypergeometric_distribution or
http://www.math.uah.edu/stat/urn/Hypergeometric.html