 Statistics and Excel, Binomial Distribution Formula and Chart. Got data? Let's get stuck into it with Statistics and Excel. You're not required to, but if you have access to OneNote, we're in the icon left-hand side. OneNote Presentation 1556 Binomial Distribution Formula and Chart tab. We're also uploading transcripts to OneNote so that you can go to the View tab, Immersive Reader Tool, change the language if you so choose, be able to either read or listen to the transcript in multiple languages using the timestamps to tie in to the video presentation. OneNote desktop version here in prior presentations. We've been thinking about how we can represent our data sets both using mathematical calculations like the mean or average, the quartiles, the median, and pictorially using tools such as the box and whiskers and histogram. The histogram being the primary tool we visualize when thinking about the spread of data and we're able to describe the spread of data on a histogram using terms such as it's skewed to the left or it's skewed to the right. We're now thinking about curves that have a formula related to them which in some cases might be able to approximate the data sets that we are looking at in real life. And if that's the case, then we can have more predictive power oftentimes using these formulas that represent a smooth curve or a line. We're now looking at a binomial distribution in prior presentations. We looked at different families such as the uniform and distribution and the Poisson distribution. Now we're looking at a binomial distribution which oftentimes comes up quite often. There's a lot of scenarios actually where a binomial distribution could come up in practice such as in business scenarios. So let's look at the conditions of a binomial distribution. The binomial distribution describes the behavior of a count variable X if the following conditions apply. Number one, the number of observations in is fixed. So we have some observations we're going to be looking at. Those observations are going to be fixed. A common example is like a sales call type of situation when we're looking at business examples at least. So in a sales call situation, for example, you're going to have a fixed number of calls and each call that you're going to be making is going to have either one of two outcomes. A success, a sale or a non-success, a non-sale. So I'll use that as my example as we kind of think about these conditions. Number two, each observation is independent. So in other words, when we're looking at our sales calls observations to see whether or not we made a sale or didn't make a sale in each of the sale call, they're independent. One sale that we made on one sale call has no impact on the second sale and whether or not we get a sale on the second call. And if you look at this and think of it in terms of cards with a statistics situation, for example, if you draw one card from the deck, then you've kind of changed the odds when you draw the second card from the deck if you don't put that first card back because now you're talking about 51 cards instead of 52 cards, for example. So if you put the card back, now you have kind of an independent. You reshuffle the deck and you're back to a randomness of 52 cards in the deck. Number three, each observation represents one of two outcomes, success or failure. So anytime there's kind of this binomial to type of outcome situation, then we want to be able to say can I define whatever I'm looking at as either a success or a fail type of situation. So we're not looking at situations where there's gradients of success or sales, such as I had a sales...