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# Normal distribution excel exercise

## Video transcript

In this video we're going to cover what is arguably, the single most important concept in all the statistics. Well if you go into almost any scientific field you might even argue that it's the single most important concept. I've actually told people that it's kind of sad they don't cover this in the core curriculum. Everyone should know about is because it touches on every single aspect of our lives and that's the normal distribution or the Gaussian distribution or the bell curve. And just to kind of give you a preview of what it is, my preview will actually make it seem pretty strange but as we go through this video hopefully you'll get a little bit more intuition of what it's all about. The Gaussian distribution or the normal distribution, they're two words for the same thing. It was actually Gauss who came up with it. I think he was studying astronomical phenomenon when he did. But it's a probability density function just like we studies the Poisson distribution. It's just like that. And just to give you the preview it looks like this. The probability of getting any x, and it's a class of probability distribution functions. Just like the binomial distribution is and the Poisson distribution, it's based on a bunch of parameters. This is how you would traditionally see it written in a lot of textbooks and if we have time, I'd like to rearrange the algebra just to get a little bit more intuition of how it all works out. Or maybe we could get some insights on where it all came from. I'm not going to prove it in this video, that's a little bit beyond our scope. Although, I do want to do it and there's actually some really neat mathematics that might show up. If you're a math lead there's something called Sterling's formula what you might want to do a Wikipedia search on, which is really fascinating. It approximates factorials with essentially a continuous function. But I won't go into that right now. The normal distribution is 1 over -- this is how it's normally written -- the standard deviation times the square root of 2 pi times e to the minus 1/2. Well, I like to write it this way, it easier to remember, times whatever value you're trying to get minus the mean of our distribution divided by the standard deviation of our distribution squared. And so if you think about it, actually this is a good thing to just notice right now. This is how far I'm from the mean and we're dividing that by the standard deviation of whatever our distribution is. This is a preview of actually a normal distribution that I've plotted, the purple line here is a normal distribution. Initially the whole exercise -- I know I jump around a little bit -- is to show you that the normal distribution is a good approximation for the binomial distribution and vice versa. If you take enough trials in your binomal distribution and we'll touch on that a second. The intuition of this term right here I think is interesting because we're saying, how far are we away from the mean, we're dividing by the standard deviation. So this whole term right here is how many standard deviations we are away from the mean. This is actually called the standard z score. One thing I found in statistics is there's a lot of words a lot of definitions and they all sound very fancy, the standard z score. But the underlying concept is pretty straightforward. Let's say I had a probability distribution and I get some x value that's out here and it's 3 and a half the standard deviations is away from the mean, then it's standard z score is 3 and a half. Anyway let's focus on the purpose of this video. So that's what the normal distribution, I guess the probability density function for the normal distribution looks like. But how did it get there? By the end of this video you should at least feel comfortable that this is a good approximation for the binomial distribution if you're taking enough trials. And that's what's fascinating about the normal distribution is that if you have the sum -- and I'll do a whole other video on the central limit theorem -- but if you have the sum of many independent trials approaching infinity, that the distribution of those, even though the distribution of each of those trials might have been non-normal but the distribution of the sum of all those trials approaches the normal distribution. I'll talk more about that later. But that's why it's such a good distribution to kind of assume for a lot of underlying phenomenon. If you're kind of modeling weather patterns or drug interactions and you we'll talk about where it might work well and where it might not work so well. Like sometimes people might assume things like a normal distribution in finance and we've see the financial crisis that's led to a lot of things blowing up but. Anyway, let's go back to this. This is a spreadsheet right here. I just made a black background and you can downloaded it at khanacademy.org/downloads Actually, if you just do that you'll see all of the downloads. I haven't put it there yet, I'm going to do it right after I record the videos downloads/normal distribution.xls. If you just go up to khanacademy.org/download/ you'll see all the things there and you'll see this spreadsheet. I encourage you to play with it and maybe do other spreadsheets were you experiment with it. So this spreadsheet what we do is we're doing a game or let's say I'm sitting I'm on a street and I flip a coin, I flip a completely fair coin. If I get heads, this is heads, I take a step backwards or let's say a step to the left. And if I get a tails I take a step to the right. So in general I always have a -- this is a completely fair coin -- I have a 50 percent chance of taking a step to the left and I have a 50 percent chance of taking a step to the right. So your intuition there is if I told you I took a you a thousand flips of the coin you're going to keep going left and right. If by chance you get a bunch of heads, you might end up really kind of moving over to the left. If you get a bunch of tails you might move over to the right. And we learned already the odds of getting a bunch of tales or many more tails than heads is a lot lower than things kind of being equal or close to equal. Right here what I've done -- let me scroll down a little bit, I don't want to lose the whole thing -- is I have this little assumption here and I encourage you to fill that out and change it as you like. This is the number of steps I take. This is the mean number of left steps and all I did is I got the probability and we figured out the mean of the binomial distribution. The mean of the binomial distribution is essentially the probability of taking a left step times the total number of trials. So that's equal to 5, that's where that number comes from. And then the variance -- and I'm not sure if I went over this and I need prove this to you if I have and I'll make a whole other video on the variance of the binomial distribution -- is essentially equal to the number of trials, 10 times the probability of taking the left step or kind of a successful trial -- I'm defining left as a successful trial, that could be right as well -- times the probability of 1 minus the successful trial or non successful trial. In this case they're equally probable and that's where I got the 2.5 from. And that's all on the spreadsheet. If you actually click on the cell and look at the actual formula I did that. Although sometimes when you see it in Excel it's a little bit confusing. And this is just the square root of that number. The standard deviation is just the square root of the variance. That's just the square root of 2.5. And so if you look here this says, OK what is the probability that I take 0 steps? So I take a total of 10 steps -- just to understand this spreadsheet -- what is the probability that I take 0 left steps? And just to make clear, if I take 0 left steps that means I must have taken 10 right steps. And I calculate this probability -- I should have drawn maybe a line here -- I calculate this using the binomial distribution. And how do I do that? Let me actually switch colors just to make things interesting. Do they have a purple here? I'll do a blue. So blue for binomial. So what I have here is how many total steps? There's a total of 10 steps. So 10 factorial, that's kind of the number of trials I have. Of that I'm choosing 0 to go left. So 0 factorial divided by 10 minus 0 factorial. This is 10 choose 0. I'm choosing 0 left steps of the total 10 steps I'm taking times the probability of 0 left steps so, it's the probability of a left step, I'm only taking 0 of them times the probability of a right step, and I'm taking 10 of those. So that's where this number came from, this .001. That's what the binomial distribution tells us. And then this one similarly, is 10 factorial over 1 factorial over 10 minus 1 factorial. That's how I get that one. And once again, if you click on the actual cell you'll see that explained. We've done this multiple times. This is just a bionomial calculation. Then right here, after this line right here, you can almost ignore it. I did that so that I can do a bunch of different scenarios. For example, if I were to go to my spreadsheet, and instead of doing 10 I wanted to do 20 steps then everything changes. And that's why down here after you get to a certain point the whole thing just kind of repeats. I'll let you think about why I do that. Maybe I should have made a cleaner spreadsheet. But it doesn't affect the scatter plot chart that I did. And so this plot in blue, and you can't see it because the purple is almost right over. Actually let me make it smaller so that you can see. Let's say I only took 6 steps. Well it's still hard to see the difference between the two. Once again the whole point of this is to see that the normal distribution is a good approximation. But they're so close that you can't even see the difference on mine. If you only took four steps, OK, I think you can see here. Let me get my screen draw on. The blue curve is right around there. This is the binomial. There's only a few points here, you the points only go up to here. This is if I take 0 steps left, 1 step left, 2 steps left, 3 steps left, 4 steps left. And then I plot it and then I say what's the probability using the binomial distribution? And this is my final position right? If I take 0 steps to the left then I take 4 steps to the right so my final position is at 4, so that's the scenario right here. Let me switch my color back to yellow, it's easier to see. If I take 4 steps to the left, I take 0 steps to the right and so my final position is going to be at minus 4. It's going to be here. If I take an equal amount of both, that's this scenario, then I'm neutral. I'm just stuck in the middle right here. I take 2 steps to the right and then I take 2 steps to the left or vice versa, I take 2 steps to the left and then I take 2 steps right and I end up right there. Hopefully that makes a little sense. My phone is ringing. I'll ignore that because the normal distribution is so important. Actually, my 9 week old son is watching so this is the first time I have a live audience. He might pick up something about the normal distribution. So the blue line right here -- I'll trace it maybe in yellow so you can see it -- is the plot of the binomial distribution. I connected the lines but you see the binomial distribution look something more like this. This is the probability of getting to minus 4. This is the probability of going to minus 2. This right here is the probability of ending up nowhere. Then this is the probability of ending up 2 to the right and this is the probability of ending up 4 to the right. This is the binomial distribution, I just plotted these points right here. This is 0.375. This is 0.375. That's the height of that. Now what I wanted to show you is that the normal distribution approximates the binomial distribution. So this right here, I wanted to say what does the normal distribution tell me is the probability of ending up with exactly 0 left steps? This is a little bit tricky. The binomial distribution is a discreet probability distribution. You can just look at this chart or look here and you say, what is the probability of having exactly 1 left step and 3 right steps which puts me right here? Well you just look at this chart and you say oh, that puts me right there. I just read that probability, it's actually .25. And I say oh, I have a 25 percent chance of ending up 2 steps to the right. There's a 25 percent chance. The normal distribution function is a continuous probability distribution so it's a continuous curve. It looks like that, it's a bell curve and it goes off to infinity and starts approaching 0 on both sides. It looks something like that. This is a continuous probability distribution. You can't just take a point here and say, what's the probability that I end up 2 feet to the right? Because if you just say that there's the actual the probability of being exactly -- and you should watch with my video on probability density functions -- but the probability of being exactly 2 feet to the right, exactly, I mean I'm talking to the atoms, is close to 0. You actually have to specify a range around this. What I assume in this within a half a foot in either direction. Right? If we're talking about feet. To figure that out what I did here is I took the value of the probability density function there. And I'll show you how I evaluated that. And then I multiply that by 1. So it gives me this area. And I use that as an approximation for this area. If you really want to be particular about it what you would do is you would take the integral of this curve between this point and this point as a better approximation. We'll do that in the future. But right now I just want to give you the intuition that the binomial distribution really does converge to the normal distribution. So how did I get this number right here? Well I said, what is the probability that I think 1 left step -- I kind of used less steps as success -- of one? And that equaled 1 over the standard deviation. When I only took 4 steps the standard deviation was 1. So 1 over 1. Actually let me change this. Let me change it to a higher number. We'll go back to the example where I'm at 10. So if this is at 10. Let me go back to my drawing tool. Let me do this calculation. Actually, even better let me do this calculation. So what's the probability that I have 2 left steps? If I have 2 left steps I took a total of 10 steps so I'm going to have 8 right steps and that puts me 6 to the right. So that's this point right here. So what's that probability? How do I figure this out using the probability density function? How do I figure this height? Well I say the probability of taking 2 left steps -- that's how I calculate it, if you actually click on the cell you'll see that -- is equal to 1 over the standard deviation, 1.581 -- and I just directly reference the cell there -- times the square root of 2 pi. I'm always in awe of the whole notion of e to the i pi is equal to negative 1 and all of that. But there's another amazing thing. That all of a sudden as we take many trials we have this formula that has e and pi in it and square roots but once again these two numbers just keep showing up. It tells you something about the order of the universe with a capital o. But let's see, times e to the minus 1/2 times x. Well x is what we're trying to calculate, two successes. To to have exactly 2 left, so it's 2 minus the mean. So the mean is five, 2 minus five divided by the standard deviation, divided by 1.581, all of that squared. That's where this calculation came from. So I told you in the last one this right here just tells me this value up here. If I want to know this exact probability, it's the area of this. And if I just take one line the are is 0. Remember, in this case you can only be 2 feet away because we're taking very exact steps. But what the normal distribution is it's the continuous probability density function so it can tell us what's the probability of being 2.183 feet away? Which obviously can only happen if we're taking infinitely small steps every time. But that's what it's use is. It happens when you start taking an infinite number of steps. But it can approximate the discreet. And the way I approximate it is I say oh, what's probability of being within a foot of that. And so I multiply this height, which I calculate here, times 1. So let's say this has a base of 1, to calculate this area which I use as an approximation. So you just multiply that times 1 and that's what you get here. And I just want to show you. Even with just 10 trials, the curves, the normal distribution here is in purple and the binomial distribution is in blue. So they're almost right on top of each other. As you can take many more steps they almost converge right on top of each other and I encourage you to play with the spreadsheets. Actually, let me show you that they converge. There's a convergence worksheet on this spreadsheet as well if you click on the bottom tab on convergence. This is the same thing but I just want to show you what happens at any given point. Let me explain this spreadsheet to you. So this is what's the probability of moving left, right? So this is just saying, I'm just fixing a point what's the probability -- and you can change this -- of my final position being 10. And this essentially tells you that if I take 10 moves, for my final position to be 10 to the right, I have to take 10 right moves and 0 left moves. That's a typo right there, it should be moves not movest. If I take 20 moves to end up 10 moves to the right then I have to make 15 right moves and 5 left moves. Likewise if I take a total of 80 moves, if I think 80 flips of my coin to make me go left or right, in order end up 10 to the right, I to take 45 right moves and 35 left moves in any order and it will end up with 10 to the right. So what I want to figure out is, as I start taking a bunch of total moves -- here I max it out at 170 -- if I start flipping this coin an infinite number of times, I want to figure out what's the probability that my final position is 10 to the right. And I want to show you that as you take more and more moves the normal distribution becomes a better and better approximation for the binomial distribution. So right here, this calculates the binomial probability, just the way we did before and you can look at the cell to figure it out. I used left moves as a success. So this is 10 choose 0 and we know what that is. It's 10 factorial over 0 factorial over 10 minus 0 factorial times 0.5 to the 0 times 0.5 to the 10th. That's where that number comes from. If I go to this one right here is calculated. Actually let me write it out because I think it's interesting. I have a total of 60 total moves, so it's 60 factorial over, I have to have 25 left moves so 25 factorial. So that's I'm 60 minus 25 factorial times the probability of a left move and have 25 of them, times the probability of a right move and I have 35 of those. So that's just what the binomial probability distribution will tell us. And then it figures out the mean and the variance for each of those circumstances and you could look at the formula. But the mean is just the probability of having a left move times the total number of moves. The variance is probability of left times probability of right times total number of moves. And then the normal probability, once again, I just use the normal probability. So I approximate it the same way. And Excel has a normal distribution function but I actually typed in the formula because I wanted you kind of see what was under the covers for that function that Excel actually has. So I actually say what's the probability of 25 left moves? No, 45 left moves. So I say the probability of 45 left moves is equal to 1 over the standard deviation. So in this situation the standard deviation is the square root of 25. So it's five times 2 pi times e to the minus 1/2 times 45 minus the mean, minus 50 over the standard deviation, which we figured out was 5, squared. So that tells me the value of what the normal distribution tells me for this situation with this standard deviation and this mean and then I multiply that by 1 -- you don't see that in the formula, I don't actually write times 1 -- to figure out the area under the curve. Because remember it's a continuous distribution function. This right here just gives me the value but to figure out the probability of being within a foot of it I have to multiply by 1. I'm approximating really. I really should take the integral from there to there but this little rectangle is a pretty good approximation. In this chart I show you that as the total number of moves gets larger and larger the difference between what the normal probability distribution tells us and the binomial probability of distribution tells us, gets smaller and smaller in terms of the probability of you ending up to 10 moves to the right. And you can change this number here. Let me change it just to show you. You could say what's the probability of being 15 moves to the right? I think that something is happening with the floating point error because when you get to large factorials I think something weird happens out here. You may just have to get even further out. For 10 you can see clearly that it converges and I'll trying to figure out why I was getting those weird saw tooth patterns. Maybe while I do screen capture something weird is happening. The whole point of this was to show you that if you want to figure out the probability of being 10 moves to the right, as you take more and more flips of your coin the normal distribution becomes a much better approximation for the actual binomial distribution. And as you approach infinity they actually converge to each other. Anyway, that's all for this video. I'll actually do several more videos on the normal distributions because it is such an important concept. See you soon.