1 00:00:00,130 --> 00:00:03,510 A basketball coach has 20 players available. 2 00:00:03,510 --> 00:00:06,500 Out of them, he needs to choose five for the starting 3 00:00:06,500 --> 00:00:10,450 lineup, and seven who would be sitting on the bench. 4 00:00:10,450 --> 00:00:14,870 In how many ways can the coach choose these 5 plus 7 players? 5 00:00:14,870 --> 00:00:18,660 It is certainly a huge number, but what exactly is it? 6 00:00:18,660 --> 00:00:20,800 In this lecture, we will learn how to answer 7 00:00:20,800 --> 00:00:22,810 questions of this kind. 8 00:00:22,810 --> 00:00:26,190 More abstractly, we will develop methods for counting 9 00:00:26,190 --> 00:00:29,680 the number of elements of a given set which is described 10 00:00:29,680 --> 00:00:31,750 in some implicit way. 11 00:00:31,750 --> 00:00:34,530 Now, why do we care? 12 00:00:34,530 --> 00:00:37,340 The reason is that in many models, the calculation of 13 00:00:37,340 --> 00:00:39,530 probabilities reduces to counting. 14 00:00:39,530 --> 00:00:43,320 Counting the number of elements of various sets. 15 00:00:43,320 --> 00:00:46,050 Suppose that we have a probability model in which the 16 00:00:46,050 --> 00:00:50,570 sample space, Omega, is finite, and consists of n 17 00:00:50,570 --> 00:00:52,500 equally likely elements. 18 00:00:52,500 --> 00:00:56,450 So each element has probability 1/n. 19 00:00:56,450 --> 00:00:59,340 Suppose now that we're interested in the probability 20 00:00:59,340 --> 00:01:03,500 of a certain set, A, which has k elements. 21 00:01:03,500 --> 00:01:08,750 Since each one of the elements of A has probability 1/n, and 22 00:01:08,750 --> 00:01:13,150 since A has k distinct elements, then by the 23 00:01:13,150 --> 00:01:17,289 additivity axiom, the probability of A is equal to k 24 00:01:17,289 --> 00:01:19,800 times 1 over n. 25 00:01:19,800 --> 00:01:23,440 Therefore to find the probability of A, all we have 26 00:01:23,440 --> 00:01:27,370 to do is to count the number of elements of Omega and the 27 00:01:27,370 --> 00:01:30,580 number of elements of A, and so determine the 28 00:01:30,580 --> 00:01:33,180 numbers k and n. 29 00:01:33,180 --> 00:01:36,390 Of course, if a set is described explicitly through a 30 00:01:36,390 --> 00:01:40,090 list of its elements, then counting is trivial. 31 00:01:40,090 --> 00:01:42,610 But when a set is given through some abstract 32 00:01:42,610 --> 00:01:45,950 description, as in our basketball team example, 33 00:01:45,950 --> 00:01:48,350 counting can be a challenge. 34 00:01:48,350 --> 00:01:52,340 In this lecture, we will start with a powerful tool, the 35 00:01:52,340 --> 00:01:56,085 basic counting principle, which allows us to break a 36 00:01:56,085 --> 00:01:58,350 counting problem into a sequence of 37 00:01:58,350 --> 00:02:01,190 simpler counting problems. 38 00:02:01,190 --> 00:02:05,230 We will then count permutations, subsets, 39 00:02:05,230 --> 00:02:08,139 combinations, and partitions. 40 00:02:08,139 --> 00:02:12,220 We will see shortly what all of these terms mean. 41 00:02:12,220 --> 00:02:15,340 In the process we will solve a number of example problems, 42 00:02:15,340 --> 00:02:18,600 and we will also derive the formula for the binomial 43 00:02:18,600 --> 00:02:21,740 probabilities, the probabilities that describe 44 00:02:21,740 --> 00:02:24,430 the number of heads in a sequence of 45 00:02:24,430 --> 00:02:26,710 independent coin tosses. 46 00:02:26,710 --> 00:02:28,329 So, let us get started.