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- [Instructor] Another data distribution
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is called the binomial distribution.
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And that deals with trials
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where there are only two possible outcomes, true or false.
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A customer might either sign up for,
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or not sign up for your company's mailing list for example.
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In this movie,
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I will show you how to calculate the probabilities
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for outcomes described by the binomial distribution.
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So we will need our chance of success,
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and the number of trials,
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that is the number of customers who appear.
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And we can use those calculations to graph the likelihood
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of a specific number of customers signing up.
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With that introduction, I will switch over to Excel.
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My Excel sample files is 04 05 binomial,
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and you can find it in the chapter four folder
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of the exercise files collection.
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In this workbook, I have a worksheet with a graph
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that will show the sign of percentage
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based on calculations we make in the table on the left.
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We have the two pieces of information we need in column B.
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So in B1, which is already selected,
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I have the probability of success,
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and we're assuming that a customer will be 35% likely
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to sign up for our company's mailing list,
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if we present it to them,
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and we are going to work with 20 customers at a time.
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And from there, we can evaluate our data.
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To create our formula, I will click and cell B5,
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and this is an Excel table, so when I enter this formula
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in the first row of this column,
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the formula should be copied down.
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So I will type in the equals sign,
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and the function we'll use is binom.dist.
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So B-I-N-O-M dot D-I-S-T.
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The number of successes is in cell A5,
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so the first calculation will ask about the probability
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of getting zero successes, in other words, no one signs up.
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And a comma, then the number of trials,
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and that is in cell B2.
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I don't want that reference to change,
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so I'll press F4, that's command T on the Mac, that comma.
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Probability of success, that's in B1 so I'll click there,
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and again, F4, comma, and a question of whether we want
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the probability of getting that value or less,
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or the specific value exactly.
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In this case, I want the specific value,
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so I will highlight false for the probability mass function,
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also called the point probability.
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Press tab, right parenthesis and enter.
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And the formula didn't copy down,
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but if I click the action button that appears,
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that's the auto correct options button,
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and then click overwrite all cells in this column
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with this formula.
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Then I get the updated calculations,
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and the chart that you see on the right.
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Looking at the data, based on 35% probability
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of an individual signing up,
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and 20 as the number of customers we approach,
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we can see that the most likely outcome is seven.
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And that makes sense, because 0.35 times 20 is seven.
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So it's very unlikely that no one will sign up.
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And most of the time it looks like we will have between,
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say about four and 11 individuals signing up.
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And again, it appears that our most common value
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will be seven.
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If you want to determine the probability
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of a specific number of successes or less occurring,
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you can change the last argument from false to true.
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So I will double click and cell B5,
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which contains my first formula.
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Backspace far enough so I get true,
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the cumulative distribution function.
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Press tab to accept it, enter.
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And this time the formulas in the column did update.
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So as you can see, the probability
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of getting five individuals or less,
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or fewer, I guess, is about 21%,
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and then six or fewer is about 40%, 41 in this case.
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60% for seven or less, and that continues on up to 20.
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And you can see that there is not much of a change
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in the probability between getting 14, so that's 96.97%,
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and 15, which is 99.995%.
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So it is very unlikely that we will have
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anything more than about 10 people showing up,
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or signing up in a particular hour.
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Analyzing data using the binomial distribution
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lets you determine the likelihood
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of achieving certain results,
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when faced with a series of yes or no trials.
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If you're not hitting your targets for manual signup,
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or sales opportunities, you should consider
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whether your approach needs to be changed,
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or if your expected success rate is too high.
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