Everybody force a Jeff here. We've got 23 diamond icons and baseball hobby one box number two random hit style how it works We'll open the box go through all of the hits type him in and a spreadsheet as we go And then we'll random to see who gets what at the very end if there are more than 10 hits which happened last break We'll combine two of the lesser value hits as one spot together to even it up just a bit But we like we like extra hits extra hits is good Good luck everybody. Let's go ahead and get started It is a good thing extra hits extra read all about yeah, it's my this is one of them This isn't the one that I was super excited as my palo in New Jersey, but this is just one of the replicas It's not one of the fancy ones the only fancy one they had was like the That's great. You're like Star Wars. No, I create your own Jersey. So I got the regular Palo. Hey, Palo is not good It's got to be something in it though, right? Yeah, but Alright broke out into like hives and a rash two days in a row. It's okay like now or Yeah, we took them to merges it well Sarah took them to emergency room last night. Gotcha Now we gotta see an hour just to do all that stuff. Oh, yeah Poor guy all the free results back See how it goes for Rocco. That's the look man. Thanks. I'm starting off with a mark McGuire auto 12 of 15 Big Mac let's go back. That's a HGH Mac Real look at his uh, I was gonna say look at his face Seiko Mac D's Mark McGuire auto 12 of 15 This will look cool with a Cardinals car with that red like if he wanted to he could just squeeze his hands and turn That bottom of that bat into like toothpicks You should like grind it to you can literally see the bat remnants coming off. Oh, man I like that David Ortiz With that silver ink auto 12 of 15. Yeah, that's nice. I like that a lot you too. So these are whatever BPA is BPA I'm gonna get to know all thanks Ryan. Yeah, I don't think it's super serious.   That's a cheap too. That's a hundred twenty five hundred twenty nine bucks. That's what I think Yeah, that's another guy that like Hadn't signed live and product for a bit there. So it's really nice to see him. Yeah Good. This is good to see him like definitive and icons. Yeah Watch eggs wrong way. Yeah, I mean, that's kind of how you find out, right? So there's never a right way. I guess oh First rookie auto of the day Michael Harris the second 15 of 15. That's a nice hit that one is really cool Yeah, that'll go into a Mac. Good luck on that one everybody rookie of the year in 2022 The Braves team she's gonna be are they just gonna be good forever probably Christian yellowed 16 to 25 on the auto and you can make the bow as well Yeah, these 16 to 25 Yeah, Harris is cool Noah. Yeah, these look awesome. What's up, Ryan? Spencer with the ATL Michael thing there The bow that we got here Mag that card I'll type you know, I got you. I already got my mag that card Next up got Edgar. Whoa the purple ink. I like that Best DH that we've been saying Jeff. We pulled them all right. He's right here is David Ortiz. Mm-hmm. He's okay Three of 15 purple ink Whoa that next one is a Harold Bains pretty fire That was a monster So good Right anchor was the man. Look at me wrong. He's a Hall of Famer Yeah as a DH you look at this next one. I like jib you better. Oh boy. Oh my god, dude Mani Machado with the uh, I guess it's one of the regular camos Six of 25 three-color patch auto It's part of his number. I would I would assume I thought that was somebody else, but it's still nice. I Thought so too, but yes, I agree All right, nice Machado on the patch auto out of 25. I Like those up six jumbo patch autos man. They did that definitive too. I like that. They carried that over to icons Dude check this one this one's nasty to local kid Ryan Mountcastle number to five three color patch auto The number patch as well Man, these are sick. These are like raised edges to that like gold outcropping in front of the black Yeah, those are like raised. These are really really nice cards. They're actually older Ryan We gave a bunch of these away at the national two years ago Like I mean we we gave away hundreds of them I don't know. I don't even know if we have any I Don't think we do we have some of the some of the black ones in stock, but the blue is the blue is like retired It's like yeah, it's like retro now. Yeah, it's three years Retro. Yeah, it is retro. We got a cut. Oh Oh Luke Appling that's cool. Okay, one of three cut signature. Nice. I mean he was a white sign very nice Yeah, this is white size. So this would go to white size in a break But good luck on that one always good to get a cut Luke at what rich. Oh, just do Facts Luke Appling Certified cut say that's one thing about icons as far as opposed to other tops products You see cuts pretty almost like yeah, like almost often one a case one every other case something like that Yeah Next up you've got a Nolan Ryan's who have ten black parallel auto Nick's cards very cool the Ryan Express We will have 11 you gotta get that And that's too small of a mag you gotta get like a 100 point for that and get out of the penny sleeve Nolan Ryan auto number two of ten Now that's a 55 point mag and yeah, that one is sick. We got gold ink. You have that Sammy Sosa black chromatic Seven of ten. I like that with the gold Sammy It seems like they had the guy sign and then they cut those like strips out of it for like the style But they like signed on the car because that happened with one of the autos last break Seven of ten Sammy auto Sammy Sosa like Sam Sammy and mark in the same case is cool. The best one was Combo yellow in Edgar There was no relic was there no Can't see the beginning of this a Pretty sure yeah, it would be no relic because I have all autos What was that last one again that was Sosa Yeah, so yellow and Anger yeah Well, how about that dude? We didn't even get a relic. We just got all autos plus a cut auto like that Yeah, that's that's a that's a great box right there Everyone's walking there with ink which may not happen all the time It's supposed to be ten hits, but we had 11 in both the first couple here All right, so we got McGuire what's he's bow Harris Edgar and Yelich Machado Mountcastle Appling Ryan and so so cool cool cool All right, let's randomize best of luck everybody Roy box Yeah, we started with mark and we ended with Sosa Three on our random best of luck on this random y'all 11th card goes to Ryan D. No, I changed rules mid-break. No more no more Three on our random names then hits one two and three Tom at the top and the bottom here and the hits best of luck everybody a one two and three the Machado patch are that thing was gorgeous down to the Ortiz Every Ortiz is an amazing Ortiz. They're all the best cards You should have seen what Lane was trying to buy for Almanac earlier was an Ortiz booklet from like definitive or something. I was I was like drooling Okay, there are the results here Tom see gets the Machado patch auto the Harris rookie auto Nick P with the Ryan auto John a with the Mountcastle patch auto to five that thing was gorgeous, too Tom see with the Martinez and Yelich both those autos in one spot and the Sosa auto John a with the Bo Jackson auto to five Tom see with the Appling cut Kevin T with the McGuire auto and Tom see with the Ortiz auto That'll do it. I'll get those posted and we're gonna keep moving on. Thanks you very much everybody


Naval Medical Center San Diego, or NMCSD, commemorates 25th anniversary of the hospital's 1988 opening and rededication. The event featured the Marine Corps Recruit Depot and NMCSD Color Guard, a musical performance by Navy Band Southwest and a cake cutting ceremony. Commander, NMCSD and Navy Medicine West delivered remarks during the ceremony. And it fills me with pride to see the one thing that has not changed, caring for others because they needed our help and because it was the right thing to do. NMCSD's origins began as a tent dispensary at Balboa Park in 1914, caring for Marines who served in the Mescan Civil War and is currently the largest military treatment facility in the Western Pacific. From Naval Medical Center San Diego, I'm Seaman Payani.


So, we will be talking about the course on probability theory and its applications. Somehow, when you utter the word probability to a layman, it does not sound very familiar or you know one does not feel very comfortable with it, but my attempt in this course would be that at the end of the course you feel that you can understand what all goes behind computing the probability of an event. So, basically, probability theory is estimating the possibility of outcome of an event. So, this word possibility that what are the, how possible, how probable the event is, is actually done by counting and so therefore, before I start giving you axioms of probability theory, I would like to begin with the basic concepts of counting because that helps you in estimating the possibility of the occurrence of an event and I will try to explain what we mean by an event and so on. So, as we go on, hopefully you will understand all these terms. So, let me just begin why, so therefore, the first topic I am going to talk about is counting and I will start with an example. So, suppose there is a small community which consists of 12 women and each of whom has two children. Now, if one woman and one of her children, so it has to be the pair, a woman and her child. So, if the pair has to be chosen as mother and child of the year, how many different choices are possible? So, let us just, there is a very simple way of counting. So, what I am saying here is that first experiment would be number of possibilities of the, the first experiment would be choosing the mother out of the 12 women. We choose one of the women as the mother of the year and so the number of possible outcomes is 12 because they are 12 women and one of them will be appointed or chosen as the woman of the year and then we will, the second experiment would be selecting one of her children as the year, as the child of the year. So, once you have chosen the mother of the year, then she has two children. So, only one of them can qualify to be the year of the child, I mean child of the year. So, therefore, the possible outcomes are 2. So, then we say that the total number of choices is 24. So, I sort of broke up this event, I will call it event of choosing the mother and child of the year by first saying that I will choose the mother and then one of her children would be chosen as the year, child of the year. So, therefore, the number of choices are 24. So, if now I want to sort of formulate this into the basic principle of counting, then the basic principle says that if there are two experiments to be performed and experiment one results in any one of m possible outcomes. So, I have way of finding out that whatever my first experiment and the possible outcomes of that event are m. So, for example, here my first experiment was choosing the mother of the year and since there are 12 women out of which I have to choose the mother of the year. So, there were 12 possible outcomes of this experiment. Any of the 12 women could have been chosen as the mother of the year. So, the first experiment results in m possible outcomes. Now, for each outcome of experiment one that means for each possible out each of the m possible outcomes here, then I want to know what are the possible outcomes of the second experiment. So, in that example, the mother had two children. So, it could only be one of the two children who could be chosen as the mother of the year. So, now here we will say that if for each outcome of experiment one, there are n possible outcomes of experiment two, then together there are m n possible outcomes of the two experiments. So, I hope this once you understand this basic principle of computing of counting, then things become simple because you have to first find out what are the possible outcomes and then we will talk about when we define the concept of an event, then we will try to find out in how many ways a particular event can occur. So, here the total number of this thing are this. Now, if you want to generalize this concept of counting. So, this will be generalized basic principle of counting and here we will now say that suppose I have r experiments which could be 1 2 3 whatever the number and then again we will start saying first one the first experiment results in n 1 possible outcomes. So, the first experiment results in n 1 possible outcomes. Now, for each possible n 1 outcomes of the first experiment for each possible n 1 outcome of the first experiment, there are n 2 possible outcomes of the second experiment and then the third stage we will then count for each possible outcomes of the first two experiments because two experiments have taken place. So, for each possible outcome of the first and the second that means now you have n 1 possible outcomes of the first experiment, n 2 for each of the experiment outcome here you have n 2 possible outcomes. So, total number of outcomes become n 1 n 2. Now, for each possible outcome of n 1 n 2, the third experiment the possible outcomes will be n 3 and so on. So, you will go on counting this and therefore, by simple arithmetic you can see that the total number of possible outcomes would be n 1 into n 2 into n r that means at each for each experiment whatever the possible outcomes you will multiply them all. So, this gives you the generalized principle of computing. So, let us look up at this example, example 1.2 how many 8 place license plates are possible if first 4 places are to be occupied by letters and last 4 by numbers. That means the first 4 can be any of the alphabet A, B, C, D. So, there are 26 choices for the first 4 places and then for the last 4 have to be the numbers. So, which means they can be any of the digits 0, 1, 2 up to 9. So, now again as I quoted the generalized principle of counting. So, number of the first experiment would be choosing the first place of the license plate. So, then I have 26 choices because 26 alphabets are there. Then again for each of the alphabet I choose here for each of the 26 alphabets I choose here I can again choose the 26 alphabets again. If I have a letter A here for example, then I can choose any of the A, B, C, D, 26 alphabets here. So, again the next outcome that means the outcome for each outcome here there are again 26 choices of the second experiment and so on. So, again for the third that means now once have chosen the first 2 alphabets here then again for any each of these outcomes 26 into 26 I can again choose in the third place any of the 26 alphabets. So, then the third choices again the number of possible outcomes are the number of choices are 26 and the same argument goes for the fourth place. And similarly for the numbers I have chosen one of the 10 numbers here then again after having chosen all this I can again choose any of the 10 numbers here and then in the third place also any of the 10 numbers and so on. So, this will be the and you can just see I have left a question mark for you to compute the number it will be a big number. So, this many license plates can be there if you have this kind of arrangement that the first 4 places have to be occupied. So, it is a 8 letter license plate number and so the first 4 places have to be occupied by alphabets and the last 4 by digits. Now, if I change the experiment and I say that repetition of letters and numbers is not permitted. So, the moment I say that once I have chosen a letter here then the same letter will not be repeated here or here. So, then my way of counting will be this now for the first place I have any of the 26 choices any of the 26 letters can be chosen here. But once I have chosen a letter here then that same letter is not permitted to be chosen here. So, now my choice at for the second experiment for every possible outcome here goes down to 25 because whatever alphabet I have chosen here I cannot choose it again. So, therefore, my choices here are 25 and once I have chosen alphabet for the first place and the second place two alphabets have been selected then both these are not allowed to be chosen again. So, therefore, my choice for the third place would come down to 24. So, out of 26 two letters have been already chosen and they are not allowed to be chosen again. So, this would choice would be 24 and then similarly at the fourth place I will be having a choice of 23. So, you can if you just keep using this generalized principle of counting which I enumerated some time ago then you see this is how you will write down the possible outcomes. And similarly for the numbers for the first place I will have choice of 10, but once I choose a number here then my choice is limited to 9 and after this again I can only choose out of the 8 which are not repeated which have not been already chosen and similarly for the fourth place my choice will be left limited to 7 numbers which have not been repeated. So, this is the kind of so the generalized principle of counting helps you to come out with the possible number of outcomes of an experiment or event here what you want to say because this was as I am counting I am saying that this is 8 experiments choice of each letter for each place of the license plate and the choice of a number I counted as a experiment. So, I had 8 experiments here and use the generalized principle of counting to find out the possible number of outcomes. Now, another possible another way of counting is done by through permutations and combinations and let me now here try to explain the difference between permutations and combinations. So, here let us say we have collection of 3 novels and so I will just say that the authors are A, B and C then 2 mathematics books and again I will distinguish them or differentiate them by the authors and call the authors as D and E and one physics book and I will call the author as P. So, there are 6 books and since I am distinguishing the books by the authors. So, it is not just novels I am saying that the I mean the authors are A, B and C. So, there are different novels I am distinguishing similarly I am differentiating between the 2 mathematics novels and of course, there is one physics book. So, the question is how many arrangements are possible if the books are to be distinguished by the authors. So, I am wanting to make arrangements of these books and arrangements would be differentiated because I will be referring to the books by the authors. So, it is not just novels or a mathematics book. So, therefore, possible arrangements now here again let us just say that we are yeah right. So, here that means in the first place if we now count the thing then that means I can say that there are 6 places. So, now the choice for the first place that means I am just arranging the books like this in a row suppose. So, the choice for the first place would be any of the 6 books because I am differentiating the books by the authors. So, therefore, for the first place the choice is 6 books. Once a book is placed here then there will be 5 more left and again so therefore, out of the 6 experiments I am just saying that what are the possible outcomes. So, once I have chosen 2 books here then I am left with picking out the book for the third place from among the remaining 4 books and then 3, 2 and 1. So, the total number is 6 factorial which is 720. Now, the 720 arrangements are known as permutations of the 6 books and the permutation word is essentially saying that the permutations are the ordered arrangements of the books. Now, by order I mean that if I am writing say for example, ordered arrangement I want to say ordered arrangement. So, if I am writing here say it could be that I am right I have chosen all the 3 novels here right and then of course, let us say mathematics books you have D and E. So, maybe you have this and U right. Now, if I have the order B arrangement B A C E D P then you see this arrangement is different from this because now here the second author if you call the book by B as a second novel then the second novel is occupying the first place and this is first the novel by the first author is occupying the second place. So, here the arrangement and therefore, this order is also considered here in this arrangement and therefore, I will say that this is different from this order or this arrangement is different from this order that arrangement B A C E D P. So, this is the idea that your 720 equations are the ordered arrangements of the 6 books and so, how you place them depends on that means, I am distinguishing between which author is occupying the first place, which author is occupying the second place and so on. So, therefore, this way it will be 720, but if you do not distinguish between the authors that means, I just treat the 3 novels as novels and in that case this and this arrangement would be the same right and in fact, you can see how many possible arrangements see I can have here C A B E D P. Now, you can tell me 3 more because these 3 letters A B C themselves I can arrange in 3 factorial ways that will be 6 ways I can arrange these 3 and therefore, all those arrangements once if I do not consider the I do not distinguish between the 3 novels by the authors as long as they are just novels then that arrangement all those 6 arrangements will be counted as 1 arrangement. So, when we are looking at the arrangement of the books the novels 3 novels 2 maths books and 1 physics book. So, once we do not order we do not worry about the differentiating the books with respect to the authors in that case you see as I tried to show you that the 3 novels since they will not be distinguished by the authors. So, then for every arrangement for every arrangement in which only the arrangement of the the arrangement of the authors has been changed the other books remain the same in that case 6 of those arrangements will amount to 1 arrangement because the 3 novels can be arranged in 6 different ways and if I leave the maths books and the physics book intact then all 6 arrangements in which only the arrangements of the novels have been changed then those 6 arrangements will amount to 1 arrangement. And similarly corresponding to each of those if I leave everything intact and only disturb the arrangement of the maths books then since I am not differentiating between the authors those arrangements will not be different because the 2 maths books whether the author D or E whichever comes first does not matter to me. So, in that case I will then divide. So, that means when I am counting the arrangement where the order is not important then that comes out to be 6 factorial it was the arrangement total number of arrangements where the order was important, but if I do not care about the order or that means I do not distinguish between the authors in that case the arrangements will become 60. So, this is what now I am trying to come to. So, the in the permutations the order was important and then I said total number of permutation that means that the order of the objects was also being considered. So, now the general principle that I am trying to and see it that in general number of arrangements of n objects where n 1 are alike n 2 are alike and n are alike n r are alike. That means there are only are different kinds of objects and since. So, therefore, surely here n 1 plus n 2 plus n r adds up to n. So, you have n objects, but n 1 of them are the same then n 2 are alike and similarly n r of them are alike. And in that case when I am wanting to make arrange the n objects then the total number of objects would be because n factorial would be the arrangements of n objects when I am distinguishing between each of them right whatever the way of distinguishing the objects whatever it is. So, that was the total arrangement, but since as I tried to show you through this example of arranging the books that if n 1 are alike then those n 1 can be arranged in n 1 factorial ways and they will amount to the same arrangement because I am not differentiating between the order. So, therefore, I will divide this total number of arrangements here by n 1 factorial, n 2 factorial and n r factorial. So, that will give me the total number of so that becomes the number of combinations. That means the arrangements where the order is not important would be the number of total number of combinations. Now, let us look at this example. So, a tennis tournament has 9 competitors and then 3 from India, 2 from Japan and 4 from Malaysia. Now, if the results of the tournament are announced by nationalities of the players in the order in which they are placed. So, it is not you know you are not distinguishing the players by their names only their nationalities being considered. In that case 3 from India would be just 3 Indians and it does not matter which one gets the first position, third position or whatever it is. So, here it will be just all the 3 players would be considered as players from India. So, another way of looking at is that you can think of these 3 people as one entity because they are just representing the same country. So, similarly 2 from Japan would be just distinguished by their nationality and not by their names and from Malaysia. So, therefore, if you want to now find out the how many such lists are possible. That means, what is the arrangement of the position that these 3 players from 3 nationalities occupy in your list. Now, first, second, third, up to there will be 9 positions. So, then the total number of lists that you can have would be 9 factorial. So, 9 is the total number of players and if I was going to distinguish them by the names. In that case, there would have been 9 factorial arrangements of the way in which the order in which a players would occupy a position in the tournament, but since we are not considering the names we are only considering the nationalities. So, for example, we will then divide this number by 3 factorial, 4 factorial and 2 factorial to get the total number of lists. So, the 3 Indians can occupy any of the positions and they will be the same. So, that means, there are 6 lists in which if 3 Indians were occupying different positions. In the sense that I just rearrange the positions that are that means, 3 Indians if they just appear. See, suppose you consider the first position, second, third, fourth, fifth, sixth, seventh, eighth, ninth and same thing here. So, if this was Indian, this is an Indian and let us say this is an Indian. Now, whatever the 3 names, if I see if you had a name here is the Indian 1, Indian 2 and Indian 3, then I can have Indian 2 here, Indian 1 here, Indian 3 here and this way you can go on. Indian 2, Indian 3, Indian 1 and just see that you can write, you can rearrange these 3 in 6 possible ways. So, other arrangements will be in the same, but if these 3 I can arrange in 6 different ways, but for me they are the same because in my list it will appear as I, I, I. So, this will not be there. So, all these arrangements will be the same and so all these 6 arrangements will amount to the same. Therefore, I am dividing this number by 3 factorial and similarly for the 2 from I mean sorry here in this case here, that was the example for the books. So, here I am dividing this number by 3 factorial, 4 factorial and 2 factorial to get the possible number of lists. So, just trying to make the concept of counting clear, so that you can then you know when you have to compute the possibility of the occurrence of an event all these things will come in handy. So, now again I am trying to differentiate this principle that if you have a collection of n objects in how many ways can we select r objects that is again the same thing because you can say that you can put the n objects in a row and then you are picking up r out of them. So, that is a different arrangement there is another way of saying the same thing. So, here by a principle of counting when you have you can say that you have arranged all these n objects in a row and then you are picking up one of them. So, then for the first play that means when I want to pick up r objects from this list of n objects the first object there are n possible ways of selecting the first. So, we are counting the same thing again by a different way. So, here you have n ways of selecting the first object then since you have already selected the first object you are left with now n minus 1 ways of selecting the second object. And similarly, it goes on depleting and finally, for the r th object that you want to choose you are left with n minus r plus 1 because you have already selected r minus 1 objects. So, n minus r minus 1 this is the number. So, this many ways you can select the r th object. Now, since it does not matter again because I am not distinguishing between the r objects that means the order is not important. Once you see out of these n objects I have collected r objects. So, it does not matter which one came out first which one came out second as long as the set of objects is the same. So, that means my way of selecting may have been you know because I picked up the first object that counted as the first object second and so on. But then finally, what I have with me is the set of r objects and in that set the third or the fourth object could have been selected first does not matter. So, therefore, what we are saying is now you have n minus r plus 1. So, that means the total number I should have written the number here let me write somewhere here. So, the order is when the order is immaterial I am saying that this is let me just write it here. So, that means what I am saying is that the first object I could pick in any of the n ways then for the second object to pick up I had only n minus 1 choice is available with me and so on up to n minus r plus 1. So, this number and since what we are saying is that the it was not important the order in which the r objects were selected it is only the final subset of r objects that I have with me. So, therefore, I will divide this by r factorial and so you can write this number you see if you have up to this number you can write as n factorial divided by r factorial n minus r factorial. So, this is the final. So, that means this is the number of combinations I can have because by selecting r objects I can call an arrangement that was here that means the n objects that I have I pick up r I put them first and then the remaining n minus r. So, essentially the r objects that you pick it does not matter in what order you pick. So, therefore, the total number of ways in which I can arrange my n objects gets divided by r factorial and obviously, the last n minus r objects also the order is not important because finally, you have divided these n objects into r and n minus r. So, it is simply which r objects get selected it does not matter in what order they got selected. So, therefore, the total number of ways in which you can pick up r objects from n objects would again be given by this number and our normally we say n choose r this is the notation for this number n factorial divided by r factorial and minus r factorial. So, this is n choose r. So, again let us take up an example. So, a jury of 7 is to be formed from a group of 30 people how many different juries can be formed. So, just carry over the what we discussed just now. So, there are 30 people and you have to pick up 7 people from this set of 30 people which will form a jury and so you want to know how many the question is how many different juries can be formed. So, this is like we discussed n objects you want to pick up r objects from there and how many ways are there how many possible combinations are there that is what we are looking at. So, obviously, you can see that the number of juries would be. So, first person in the set can be chosen out of the 30 people then once you have chosen that person you are left with 29 choices then again after these two you are left with 28 and so on. So, this number is 24 and now what we are saying is that it was not at all important as to when we have selected the 7 people since this is the subset of 7 people it does not matter which one of them got selected first. So, the order is not important it is only that finally, I have this subset of 7 people. So, therefore, the 7 we will have to divide by 7 factorial to get the possible number of juries because this 7 set of people that you are forming the jury the you are not looking at when the first when the in what order they got selected that is not important. That is what I am emphasizing again and again that the here the order of selection is not important it is just that we want this subset of 7 people and therefore, this will be the total number of arrangements and so here with this number we can write because this is up to 24 remember this is n minus r plus 1. So, here your n is 30 and your r is 7. So, when you want to divide from 36 you are left with 24 and so this is the total number if it was important as to in what order the people got selected for the jury. Since it is not important it is not being considered it is immaterial. So, therefore, I divide by 7 factorial and this number can be written as 30 factorial divided by 7 factorial into 23 factorial which our by our notation is 30 choose 7. Now, in case the group of 30 consists of 10 women and 20 men and if it is required and if it is required that 2 women and 5 men should form the jury. So, now we are saying that out of the 10 women 2 must get selected and out of the 20 men 5 men should get selected to form the jury. So, in that case the number of groups of women that you can form out of you know by choosing 2 out of 10 this will be 10 choose 2. Here again the order is not important I just want to form select a subset of 2 people 2 women from out of the 10. So, that will be total number of ways in which I can select the 2 women from the 10 women and similarly I can 20 choose 5 is the number of ways in which I can select 5 men from the set of 20 men and so the total number would be because for each subset that I choose here of women there will be this many. So, I am now using here by generalized principle of counting and so the total number of ways in which I can the number of juries of 2 women and 5 men can be formed is this. So, just through examples I am trying to make the concept clear and now let us see that you can all of you have used binomial theorem and you know that if you want to expand this number x plus y raise to n then the formula is n choose k x raise to k y raise to n minus k and you have done it by induction and so on the proof. Now, let me give you this combinatorial proof of binomial theorem using this concept of counting that we have learnt here we can use this and show that the expansion of this term can be written in this way and so let me consider the product x 1 plus y 1 into x 2 plus y. So, the n terms here now when I am multiplying each term here final in the product would be consisting of n factors a factor of n of the x i's and y j's that means each term of this project product will contain when you count the number of x i's and y j's that total number would be n because you have n factors, but certainly in any factor when you look at the x i's and y j's if an x i appears then the same index will not be for y because you see x 1 and y 1 are in the same term here. So, therefore, when I multiply either I will be multiplying x 1 by the other terms and so then y 1 will not appear in that. So, therefore, in any product you the x i's and y j's that you have if an x i appears the corresponding y j y i will not appear in that product. So, in other words what am I saying that the n the any term of this product which will contain let us say k of the x i's and then the remaining n i minus k of the y j's would be such that. So, you are choosing essentially each term here would contain let us say when I am looking at the term containing only k of the x i's and so remaining n minus k are y j's. So, then how many ways can be there how many such products can be there in which. So, you know this is containing the each term in this product has n factors containing x i's and y j's. So, then if I am looking at all the terms which contain k of the x i's then that means I am out of the x 1 x 2 x n these n x i's I am choosing k of the x i's and then of course, the moment I choose my k x i's then the y j's got the n minus k y k's y j's got get selected automatically because the once which are not appearing in my x 1 x 2 the product of x i's that I have taken the remaining indices will be will go to the y j's right. So, here that means and that ways the way of choosing k x i's out of the n x i's x 1 x 2 x n is n n choose k right. So, therefore, the number of terms which contain k of the x i's is this many terms and. So, this whole product either what will happen see for example, if you look at the product y 1 y 2 y n no x i appears. So, either so the k can vary from 0 to n the terms will contain either 0 x i's then 1 x i 2 x i's and so on. So, you want to add up all such things. So, you want counting the total number of terms in this product and that total number will be given by summing up n choose k from 0 to n and when you choose like this then that means your k of the x i's are here and n minus k are here you are adding up right. So, this thing then I put x i equal to x for all i and y j equal to y for all j. So, in that case your k of the x i's that you took from here they all become equal to x to x raise to k y raise to n minus k. So, then therefore, this product and when 1 you put the x i's together equal y i's all together then this whole product coincides with this and so you have a nice way of you know proving the binomial theorem. Now, I can immediate application of this is that if you want to count you have n objects and you want to count how many subsets can be there of these n objects you know when of course, the empty set also we consider as a subset that means nothing gets chosen from the n objects. So, you can have subsets consisting the empty set then you can have subsets consisting of only one element from one object from these n objects subsets containing 2 objects 3 objects and so on. So, if you want to count the total number of subsets that you can form of these n objects then since I told you that n c k gives you the number of subsets of size k and so you want to count this number n c k from k varying from 0 to n and which if you look at this expansion here essentially you are putting x equal to 1 and y equal to 1. So, that this all becomes 1 and so you are summing up. So, on this side when you put x equal to 1 and y equal to 1 this reduces to 2 raise to n. So, the total number of subsets this is another nice way of counting the number of subsets that you can form given n objects. So, extending this concept that I just enumerated for you for the binomial theorem now we can take over go up to multinomial coefficients. So, this is a set of n distinct objects is to be divided into r distinct groups of sizes n 1 n 2 n r for the binomial theorem r was 2. So, now it is n 1 plus n 1 n 2 n r where of course, all the n i is add up to n. So, I am dividing these n objects into r subgroups and the size of each group the first subgroup is n 1 size of second group is n 2 and so on right. So, then using the same principle I want to choose from n n 1 possible groups. So, the number of possible groups of size n 1 is n n 1 and n choose n 1 right or you also write this as actually this is also written as n c n 1. So, combinations so n 1 combination that means you want to choose n 1 objects out of n. So, what are the possible number of ways. So, any of these notations is acceptable fine. So, the first group will be the number of groups of sizes n 1 would be n choose n 1 then since I have already chosen n 1 objects out of n. So, then I want to choose the second set of objects n 2 consisting of the set of objects must be n 2 in size and then so out of n minus n 1 I want to choose n 2. So, therefore, each group of size n 1 the number of choices of the second group is n minus n 1 choose n 2 right and so you extend this concept and so finally, the last choice would be because now you will be left with n minus n 1 minus n 2 minus n r minus 1 r minus 1 subsets or subgroups have already been chosen. So, then this many you are left and out of this you want to choose n r objects. So, again the number of possible ways is n minus n 1 n 2 minus n r minus 1 choose n r right and so we will say that the total number of groups is the product of all these right and so via notation this will be the first one would be n factorial divided by n 1 factorial n minus n 1 factorial then the second one would be n minus n 1 factorial divided by n 2 factorial n minus n 1 minus n 2 factorial and so on. So, if you see that this will be my total expression and then the terms cancel out because you have n minus n 1 factorial here and this is n minus n 1 factorial. So, it cancels out. So, these terms will cancel out you know in pairs and so in the denominator you will be left with n 1 factorial n 2 factorial n r n 3 factorial up to n r factorial and this is what again what we are saying is that we are deciding whatever some objects we are putting in one group we are saying they are all alike the same principle you are using and so their arrangements will not matter in whatever way you choose in whatever order you choose is in material. So, therefore, again I am I can arrange my possible objects n objects in n factorial ways, but since I am grouping them into r different subgroups and each group the number of objects is we are not differentiating between the objects in one group. So, then the total number of ways would be n factorial divided by n 1 factorial n 2 factorial n n r factorial and now this helps us to expand for example, this gives the multinomial theorem that if you want to expand x 1 plus x 2 plus x 3 plus x r raise to n then it will be because remember in this product just as I showed you for the binomial theorem you want to choose. So, because each product here will consist of some powers of x 1, some powers of x 2, some powers of x 3 and x r and so here I am now saying that these n 1 n 2 n r will vary from because every product here will contain 0 x 1 1 x 1 2 x 2 2 x 1 and so on and different. That means, whatever the powers each term here you can see in the fact in the product these terms will appear and so your n 1 plus n 2 plus n r must be because each term this is raise to n. So, each term in this expansion will contain n x i's together. So, that means you add up the indices of x 1 x 2 x r they should add up to n and that is what is given by this. So, therefore, using this concept this is how you can write the expansion and if you look at this example where x 1 plus x 2 plus x 3 raise to 3 then you see I can choose my n 1 to be 3. So, that means this subset contains just the x component the powers of x 1 powers of x 2 and x 3 are missing here. So, therefore, the expansion this is 3 3 0 0 x 1 raise to 3 then here again you are choosing a subset from 3 which consists only of powers of x 2. So, 0 3 0 and so this will be x 2 3. So, I showed you the idea behind this and then now I am using it repeatedly to say that how you can write the expansion of this. And so therefore, you can see that the way I can choose my numbers n 1 n 2 n r such that they add up to n. So, in the case when you have your r is 3 and your n is also 3. So, then these numbers must add up to 3 and you are dividing your number 3 in 3 possible ways. So, that they add up to 3. So, here these are the possible and the final thing is 3 1 1 1 and when each x 1 x 2 x 3 has only power 1. So, this is the expansion and now in the exercise sheet that we will just show you I am asking you how many terms are there in the in the multinomial expansion. And remember because which is actually counting the number of subsets right here essentially you are counting the number of subsets that you can have you know like you have r this thing here x 1 x 2 x 3 x r. So, essentially my question is how many terms are there in this expansion and so I have already discussed this case with you for the binomial how I used for answering the number of subsets of total number of subsets of n objects. So, here you have to use the same concept and tell me how many terms are there in this multinomial expansion. So, let me just show you the discuss the exercise sheet. Assign one is straightforward 18 workers are to be assigned to 18 different jobs. So, the important part here is that each job is different from the others and therefore, this will be an ordered arrangement of the 18 workers. So, you can write down how many possible assignments are possible that means an assignment would mean that you assign one particular worker to a particular job. So, then how many ways can you do this this is the idea here right. Now, consider a group of 25 people if everyone shakes hands with everyone else how many hands shakes take place. So, that means that a person shakes hands with another person then both have shaken hands with each other. So, just keep that in mind and you can immediately write down what the answer will be for number 2. Now, here in question 3 4 separate awards it can be you know somebody getting the highest marks the highest cumulative performance index best sportsmen leadership quality etcetera. So, you can have 4 different awards given to these students and the idea is to select from a class of 36 students who can be given this award. Now, in case a student can receive any number of awards then it will be a different number of arrangements that you can have or different ways in which the 4 awards can be given to a student or more than one student, but in number 1 the condition is that the student can receive any number of awards. So, you have to do a counting in that way in number 2 we say each student can receive at most one award. So, that means either a student receives an award or a student does not receive an award. So, this is the way you have to count. Now, in question 4 is interesting using combinatorial argument prove that n choose r that means selecting r items from n items is equal to selecting r minus 1 items from n minus 1 plus selecting r items from n minus 1 items.   So, when you add that to this selected group then it will become r objects from n objects. Now, the second one says that n minus 1 choose r that means that particular see the thing is that either the particular object or item that you have picked is either there in a collection of r objects or it is not there. So, the first case says that yes it is going to be in that collection of r objects. So, once you added to the set of r minus 1 objects that you have selected then it becomes r. Now, in the second one what you are saying is this is the set of this is the set of selections in which that particular object does not appear. So, you have separated that object then from the remaining n minus 1 you are choosing r. So, this is the kind of combinatorial argument you are giving to prove the identity. And so, you see I have already shown you that you can by combinatorial arguments you can give show you prove the binomial theorem. Now, similarly you can try to prove number of such identities through combinatorial arguments. Then question 5 we have already discussed in how many ways can r objects be selected from a set of n objects if the order of selection is also to be considered right. So, question 6 one delegate each from 10 countries that include delegates from India, Pakistan, Bangladesh and Sri Lanka are to be seated in a row. So, the arrangement has to be in a row. Now, delegates from India and Sri Lanka are to be seated next to each other and delegates from Bangladesh and Pakistan are not to be seated together. How many seating arrangements are possible? So, the idea here is that you know you have 10 different positions people are sitting in a row. And you want people delegates from India and Sri Lanka to be seated together right which means that I can treat those two as one person. In that case it will be 9 people then who have to be arranged because delegate from India and delegate from Sri Lanka have to be together. So, that means the total arrangements is 9 factorial, but since the people are sitting in a row that means the arrangement that first the Indian delegate is sitting and then the Sri Lankan or the Sri Lankan is delegate is sitting first and then the Indian. So, this will count as two different arrangements. And therefore, the total number of arrangements would be 2 into 9 factorial in which the delegates from India and Sri Lanka are together. Now, you do not want people delegates from Bangladesh and Pakistan to be sitting together. So, now again consider the situation when they are sitting together. So, we will subtract the number of. So, in that case if you now look at the arrangements in which delegates from India and Sri Lanka are together and delegates from Bangladesh and Pakistan are together then you know you will have 8 different positions to arrange because these two delegates will be together that means that they can be treated as one person. And therefore, it will be total number of arrangements would be 8 factorial, but then again you have to it can be 2 possible you know like as I told you the arrangement can be India, Sri Lanka or Sri Lanka India. Similarly, it can be Bangladesh, Pakistan, Pakistan, Bangladesh. So, therefore, essentially you will be subtracting from 2 into 9 factorial the number of arrangements in which all the 4 delegates from Bangladesh and Pakistan are together and delegates from India and Sri Lanka are together. So, that will be 2 square into 8 factorial. And so, you subtract this number from 2 into 9 factorial that should give you the required number of ways in which you can have the required arrangement. So, how many terms are there in the multinomial expansion of x 1 plus x 2 plus x r raise to n. So, here again I am wanting you to do this exercise. See the whole idea the way I explained to you this expansion was that you are essentially dividing your number n into r smaller numbers n 1 n 2 n r. So, then they sum up to n. So, in how many ways can you partition this set of n numbers into r subsets. So, that they all add up to n this is the whole idea. And so, you will see that from this identity x 1 plus x 2 plus x r raise to n you have to put all these x 1 x 2 x r equal to 1. And so, you get r raise to n is equal to the number of terms that appear in the expansion multinomial expansion of this term. Then finally, a total of 6 gifts are to be distributed among 9 children. So, that no child receives more than one gift. This is exactly the same as you know part 2 of 3. Because, here also we said that no student should get more than one award, should get at most one reward. And so, here also we are saying that a total of 6 gifts are to be distributed among 9 children. So, that no child receives more than one gift.


the ticks negative and the RSI in the bearish zone they are both fully aligned but what I'm saying is if you wanted to go into tomorrow's expiry which is over here and if you wanted to take a put option let's say this is a 31 delta this is a 39 delta and a five contract position here would be a $3,000 trade and the $3,000 trade has the ability to you know produce you anywhere from I would say 500 to 1200 in general and on a good trade if you catch a good trade you can certainly do about a thousand to 1500 on that trade so if you get a four point move you're going to make about five to seven hundred if you get a seven point move you're going to make 12 to 1400 so but you can see we got got into this position at I think it was 640 it's already up 50 cents so on a five contract position that's $250 already you can see the market is going down you can see the ticks developing and you can see that the customer RSI is you know is pointing downwards and so which means you know things are just getting weaker and so it tells you to stay in the trade so now we are up 60 cents 70 cents on this trade and so if you're you know and now we are up 80 cents so if you know if you're up $1 that means you're up $500 you know on the on the position so this is how this would work so meanwhile if you have any questions you can please you know put it onto the onto onto the chat box and we can discuss it as to how these indicators work wait for the ticks to actually do the job and then you want to look at the customer RSI so that it can tell you whether to stay in the trade or not now you can see $770 that's a $600 profit right there $790 that's a $700 so yeah this you know this trade has already gone close to about you know $800 at this point and it's still developing it's still going well $850 that's a $1000 profit right there and it's still telling you stay in the trade stay in the trade because ticks are going down this is this shows no sign of turning around and now when you have a very good profit like this even a small turnaround in these indicators you know you want to get out of the trade okay take your profit and that's it the morning trade is over go take a break go go go get a coffee whatever but you don't want to over trade you don't want to get back into it you know the the greed and everything all those emotions will come into play and you want to be very careful of those things here we go now we are close to we are all we are above $1000 right now so it's a yeah this has become a more than a $1000 trade profit and you we still don't see the RSI changing directions even though now you can see now you also you have to look at general technical analysis look at this volume it's even higher than the open so this is telling me that there was some support here because the bar ended in the middle and you can see now the RSI turning around that's it get out of the trade get out of the trade right there take your profits close to a thousand dollars you're out of this trade the SPX ticks as well as the custom RSI or custom indicators and algos if you have any questions you can go to this bit.ly link bit.ly slash SPX algo and you can find more information on how to get these indicators thank you


Live from the Mandalay Convention Center in Las Vegas, Nevada, it's The Cube at IBM Insight 2014. Here are your hosts, John Furrier and Dave Vellante. Okay, welcome back everyone here live in Las Vegas for IBM Insight. This is The Cube, where we go out to the events, extract the silver from the noise. I'm John Furrier. My co-host Dave Vellante and our next guest is Glenn Finch, global leader of Big Data Analytics at IBM, 25 years in banking. You've been around the block, but Big Data is certainly driving a lot of value. Welcome to The Cube. Thanks John. Hey Dave, thanks. So the surveys are out there. We were going to talk about a survey you're getting behind and really that teases out the big thing, which is, and Kobielus was on earlier, and he's like, okay, speed is the key advantage. That's something that's in some of your work you've done, you're kind of teasing out the key trends. Speed sees me number one. Can you share some thoughts on this new survey? Sure. Every year for the last six years, we publish this survey. I got to tell you, in Vegas, to be talking about the number six, it's not a great day to be talking about. Anyway, so. Just add one. Yeah, that's right. I want to get to seven next year, you know what I'm saying? Anyway, so. We want to get to game seven. That's right, that's right. Six years doing this. Last year, everybody was talking about value. This year, everybody's talking about speed, and not just a little bit of speed, but radical change in speed. Big speed. Big speed. Big data, big speed. But we have what's goofy though. Last year, everybody was talking about volumes of data. Now they're talking about velocity of data. And in the consulting business, when things move a percent or two, that's like, it puts people to sleep. This year, we're talking about stuff moving 20, 30, 40%. Big swings around this whole concept of velocity. Does that make sense, guys? Yeah, totally. Well, so let me ask you, how were they defining value? How were they quantifying value? What was it? Saving money, making money? What was value last year? That's a great question, Dave. So, I wouldn't say that the metrics have changed around value from year to year, right? You kind of have this split last year, 75% of value definition was around customer acquisitions, revenue, growth, cross sell, and 25% was around operations. Big change this year, 53%. So the 75 goes down to 53% with customer, and the piece on operations grows from about 25% to 40%. So you've got a little bit more cost reduction creeping back in this year, right? But the wild thing that hit this year, 63%, 63% of all the clients said they're getting value from data projects within a year. So no more two year, three year, big data warehousing projects, 63% say that they're getting it in year. Massive, massive shift. Now, our data shows a couple interesting things. One is that there's a schism, actually, between the IT people and their definition of value and the business people. Are you seeing a sort of similar dissonance, or are you seeing more alignment? You know what's interesting, what's starting to happen is with big data, where people aren't taking two years to build a warehouse, right? Where you're throwing data into a reservoir and you're coming up with findings now, actually that schism is closing somewhat, right? Because the guys that are online in business can see that you're answering their questions quick. Because there's no way you can get to value in less than a year if you're not answering business questions, big, big difference, right? So value is a proxy for, I'm interested in the topic, speed isn't a proxy for this stuff happening. Right. What's next, get the speed? Outcomes, like results? I mean, so what's year seven? Let's go to your mind next year. What do you want to see in next year's report? If speeds today, that means I'm buying in memory flash, I'm doing some integration, I got some apps coming out there, I'm probably developing in real time, focusing on real time stuff, engagement, okay, I got that, do speed for a few years? So John, you hit it on this whole thing about outcomes, right? The concept of doing outcome-based work changes everything. So that instead of committing to project 32, you know what I'm saying? We commit to a change in a business outcome, right? Increased sales or, you know, add budget reduction or something. Right, not completing project 32. Right, right, not completing project two, but to change a fundamental business driver, right? So, we had a couple clients on stage with us today. One of them, they're going to drive hundreds of millions of dollars of benefit by shrinking how much fraud hits them. Hundreds of millions of dollars in fraud. It's an insurance company, right? They're paying out too much money in claims because people are doing some unscrup- Right, it's a bad outcome. Right, but back to the speed thing. We're doing that. In fact, we went live today in less than eight months. So the concept of moving quick and driving outcomes, that's what we're seeing. Now you know the goofy part. There's one thing we didn't talk about in this study. 7% of the respondents said that we're spending time on the office of the CFO. Now this is the goofiest darn thing I think. So he asked me what I expect to see next year. So I was on CNBC this year. We released our CFO study. CFO studies says CFOs are spending 250% more time integrating data. I'm thinking you better go to the place where the money is and help that person, right? Go help the CFO. So I think we're going to see more outcomes and I think we're going to see more stuff in the office of the CFO. You can appreciate this joke. Steve Herrod, who was used to be the CTO of VMware now as an adventure capitalist said, why do people rob banks? Because that's where the money is. That's where the money is. Exactly, exactly. So back to business outcomes. What do you work on first? Where's the money? Right, and that's what I was thinking like, it's the CFO for God's sakes, right? You're going to go ask the CFO to fund a project, but you're only spending 7% of your time with the CFO. There's a, you're biting the hand that's feeding you there. So I think we're going to see some pretty big swells.  You know when you say project 32, there's a handful of stuff going on that's going to like, okay, why are we doing that? Why are resources being committed to it? So internal analytics are also important. Well, or the metrics of success in project 32 is, hey, it works, the server light is green. Exactly. We're done. Right. And the business guy says, well, what did I get? Right. And that's the issue. That's the disconnect between IT and the business US. And that's the thing that's starting to draw business people and technology people more together because they can see outcomes from some of these, you know, quick little mercenary kind of analytical projects, right? Not the big two year, 100 million dollar, see you later and hope. But I know, so I got to ask you about the CFO. Yeah. Is the CFO, the office of the CFO qualified to do that data integration? Or is some, certainly in financial services, you've seen this chief data officer emerge. Who's responsible for data integration? That's where the money is. We see it in our surveys too. Data integration, number one tool set and challenge for big data practitioners. Right. How to integrate the data, data quality, data governance. Who owns that? What an awesome question. So, you know, this whole concept of the office of the CDO, the CFO, first of all, I'm not going to say that somebody is not qualified to do something because I don't want to whack at any, you know, CFOs out in the audience. But let me say this. Let me say this. But if I'm a CFO, I'm not sure I want to own it. No, no, you don't want to, right? You have had to build this cottage industry of fixing data as a CFO because nobody's doing it for you, right? So when somebody's spending two and a half times, more time doing something, think about the CFO for God's sake. They shouldn't be spending time there, right? So you see- And maybe it made sense post-Enron. It did, right? But now, a different animal. It doesn't. So that's why you're starting to see a lot of the chief data officer roles pop up, right? And you're starting to see a lot of people question, how can I integrate all this stuff that I just spent all this money putting in? Because there's a lot of SAP projects out there, a lot of Oracle projects, a lot of those kind of big financial transformation things. You would think that the amount of time integrating data would be going down, and it's not, right? So that tells me there's a real opportunity. And because the amount of data is exponentially growing, the metrics of success are changing, like you said, it's velocity now, it's not just amount. Yeah, unbelievable. Now, the office of the CFO, what we're seeing them start to do is to look deeper, you know, data breeds data, right? So you see the CFO starting to look for more and more things within the data. So they now start to have some level of data integration, and they say, I wonder what's causing this? So they dig deeper. Well, to dig deeper and find causality, they got to go get more data, right? So it's like a giant, ongoing mining exercise. So Glenn, I got to ask you back to my question earlier, because this is a good tie back into some of the things I was doing about the green light being on Project 32, server works, yeah, we're done. Speaks to what Wikibon is coming out in a different market in the Hadoop space where IT grades themselves, they're doing pretty good. They rolled out the POC and it's up and running and they give themselves high grades.  I mean, we need more value. So that's been going on in IT in business for ever since computing's been on. It's been quiet, yeah, 25 years you've been banking. Certainly banks are doing a lot of cutting edges for the money, they got to protect it. So you see an early indicator. What are you seeing as the way to arbitrate between those two groups? What's the key force right now that you're seeing in the customer environments where people are making, where it's certainly a collision course, it has to be a team effort. That's clearly coming out of the data we're seeing from all the conversations is that hey, we've got to get along, it's a team effort, the cloud helps, there's a lot of leverage, more creativity. So how do they make that work? What are the successful use cases and don't name names, but you can just say success and where it's not working? Yeah, so one of my favorite stories is with the leading coffee company we've worked with. They had the same store sales problem, right? Now they thought their same store sales problem had to do with food products. So they went out and fundamentally changed all the food products that they had. Change the menu. Right, go buy a bakery. It's definitely not the coffee. For God's sakes, I drink so much of that stuff every day for this particular coffee. It was their sacred cow. It is, right. So they went and bought a bakery, didn't change things, and then they said okay, we don't know where to go, so let's pull a bunch of data together. So we pulled weather data and economic data and you name the data, right? To get that done, you can't have IT and data people there. You got to have the business people, everybody's staring at the data, looking for what it's about to say. Remember when Agile was so cool when you're building, remember that whole, sure. That's kind of what big data is right now because everybody's moving in a very agile manner. If in a space of a few weeks you can put data into a reservoir and start finding things, it's unbelievable. Guys, I got a question back for you. What do you think the single greatest predictor of same store sales for this coffee company was? What do you think? Ah, weather. Weather? What was the problem again? Same store sales. Yeah, same store sales. Same store, you say weather. Okay. Okay, I'll help you out. I'd say, go ahead. I'd say quality. It was the barista. The person, the barista. Oh yeah? Yeah, the person making the coffee. It's the service. It is, so it's that experience, exactly, the personal experience. They didn't know that. The data. How they greeted, how they exit. Did they get, they had a nice smoozing experience. Dave, John, you want the latte? It's whether or not they smile. Right, right. They know their name, you know. Yes, so I think what you're seeing is this kind of scrum-like environment, right? Where everything's moving very quickly. How do they do that? How do people, because that's what, I think people see that as like, you know, that's the value. Now the speed game kicks into your survey. Okay, now let's make that happen. You know, throw the holy water on the magic process. It's really a people issue, right? So how does that leadership get the two groups working together? Have you seen any successful formulas? I think when you have sponsorship from the top down to do something like this, as compared to the bottom up, you have people showing up game on, game up, ready to go. And just looking for new things, looking for cool, fun things that are going to happen. So approach, how they approach their job. Right, so it's about approach and showing up expecting something is going to be good. Not trying to figure out the tools. I feel like the giants beat the royals. They show up and keep on pounding away, scrapping away, you know. I don't even want you to see it's even harder, okay? Getting the nationals to play. But getting people to show up thinking that something's going to go right is a whole lot different than getting people showing up thinking that 27 things are going to go wrong if they can just figure out how to put a stop to this, right? But it's daunting when the data starts to speak. That's when new levels of leadership got to step up again. So it's unbelievable. Share what's going on in the banking financial sector because obviously that's a great early adopter on all things, engagements. So that's, I mean, all things in tech. They're buying the latest and greatest Ferrari, glass drives, drive, you name it. They got security issues and believe me, they got to protect the money. So there's certainly a lot of innovation going on, bleeding edge. What do they view security and engagement? Those two separate topics, security, clear, mandate. Engagement's a new concept because now the mobile consumer, it used to be my bank experience was how great the lobby was when I walked in, the teller, if you will. Now I have an app. Are the chairs comfortable? Now I have an online app and I've got to deal and do my online banking.  Talk about the dynamics in the financial sectors. And so John, you hit the topic of fraud. Clearly data finding data, this whole concept of context computing, right? Data's moving so quickly now that we don't have the ability to know what this piece of data is meaning, right? Especially in fraud, especially in risk. When you can start putting together disparate, seemingly unconnected data together, you find massive amounts of value around fraud, around risk. Now, that's kind of trying to find bad things that are happening. You can take that same approach to find the good things, right? So back to your concept of customer experience, you can know what people want and serve it up to them in a dynamically deployed app world in real time. So you've got data for good, data for bad, right? The same techniques kind of flow and drive through both things. So financial institutions are using it as you rightly put to protect the money around risk and fraud and then to drive consumer engagement. But the problem is, you got to get it linked first, right? Because just, there's so many bunny holes, there's so much noise in the data, right?  Well, you look at what's going on with Apple Pay, right? Elbow in their way into financial services, but everybody wants the data. Yeah, but I think first data is powering that back end so they're not really, they're just UI and putting the UX on in front of that. So I mean, direct business model, I mean, that's the web. I mean, the internet started with the web, now we're in social business. It's changing. It's completely, the leveling of the playing field, thanks to things like Amazon and Bluemix and all the stuff going on. So just throw out the projected for next year. We're going to end the segment here, getting pressed on time, getting the hook here. Glenn, tell us. I feel it, yeah. Well, no, we actually got the hook. I want to stretch it out. I want you to say seven years out, the seven year itch is coming for you on the next survey. What do you anticipate? Speed again?  Is that build out? Is there going to be more speed than now let's get organizational behavior? Or what are you anticipating? You have to guess. So I think we're going to see speed again because it's just come into the fray, right? I think secondly, you know, you hit on it. We are starting to get so fast that the human dimension is unable to cope. So we're going to see a bunch of folks trying to figure out how to get people to move as fast as the data and the analytics. And then third, I think you're going to see a couple of those segments, the office of the CFO. I think we're going to see them come into the fray, where they have not been before, right? Very, very important. The stakes are high. They've got to come into the kitchen. They've got to. Absolutely. Yeah, it makes a lot of sense. And we would tell the CDO concept is that there's also compliance issues too, right?  You go another half hour on that. We could. And you know, the office of the CDO, clearly, you know, five years ago, there were five of them. This year, there's 150. And by a couple years from now, there'll be 250, right? It is the hottest job role right now because people know they've got to go game up with that, right? So how that role plays in, how it's going to affect all these answers, we're going to see a lot of change. Creative, curiosity, good communication. That seems to be the skill set for big data. We are here doing that here on theCUBE. We're curious, we're communicating. And of course, we're creative. We're talking to all the great guests here at IBM Insights. Just bringing all the data, sharing that with you. This is theCUBE inside the Social Media Insight Go Center special presentation, amazing third experience, third screen, second screen digital experience here with an IBM with their social media team. This is theCUBE, we'll be right back after this short break.