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The equation P(X)Y * (1-Y) is maximized when P(X) is most accurate. Here’s how the Excel Solver knows when it has found the correct combinations of these 3 variables so that the resulting P(X) equation most accurately predicts whether Y = 1 or 0: Optimizing the Logit Variables in the Excel Solver
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The Excel Solver will then continuously try new combinations of these variables until the optimal P(X) is found. In Excel, the P(X) calculation is initially performed by the Excel Solver using Logit variables (Constant, A, and B) which are not optimal. The P(X) is the probability of purchase that will be calculated using the equation listed above.
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The Y refers to Y = 1 if the prospect bought and Y = 0 if the prospect didn’t buy. Using Excel, each recorded prospect has the following calculation performed: Here’s how the most optimal set of Logit variables (Constant, A, and B) are found in Excel: The Excel Solver will find the optimal combination of those 3 variables that causes the resulting P(X) to most accurately predict whether Y = 1 or 0 for all previous prospects.Įverything To the Right of the Above Is Continued Below:Ĭalculating the Logit Variables - A, B, and Constant Those 3 variables can be found in Excel by using the Excel Solver.
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They must be known before P(X) can be calculated. L, the Logit, has 3 variables: Constant, A, and B. The Logit, L = Constant + A * Age + B * Gender In other words, P(X) is the probability that Y = 1. P(X) represents the possibility of event X occurring.Įvent X is a purchase. This predictive equation will be in the form of: P(X) = eL/ (1+eL) With the above data, you could create a predictive equation that would calculate a new prospect’s probability of purchasing by inputting this new prospect’s age and gender. The data you have collected on each prospect was:Ģ) The prospect’s gender (1 = Male and 0 = Female)ģ) Whether the prospect purchased or not (Did purchase Y = 1, Did not purchase, Y = 0). The embedded video walks through this example in Excel as well: Suppose that you have collected three pieces of data on each of your previous prospects. Here is a marketing example showing how Logistic Regression works. Occasionally this type of output variable also referred to as a Dummy Dependent Variable.Īn Example of Logistic Regression In Action The predicted event either occurs or it doesn’t occur – your prospect either will buy or won’t buy. In other words, the output or dependent variable can only take the values of 1 or 0. In the case of Logistic Regression, this “Y” is binary. In general, the thing being predicted in a Regression equation is represented by the dependent variable or output variable and is usually labeled as the Y variable in the Regression equation. Logistic Regression calculates the probability of the event occurring, such as the purchase of a product. (Is Your Sound and Internet Connection Turned On?)Īmazon Kindle Users Click here to View Video
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Step-By-Step Video Showing How To Predict if a Prospect Will Buy Using Logistic Regression in Excel: The example that will be presented in the video will also be covered below in the article: On the following page is a video which will show you how to perform Logistic Regression in Excel and why it works. The more data you’ve collected from previous prospects, the more accurately you’ll be able to use Logistic Regression in Excel to calculate your new prospect’s probability of purchasing. Marketers use Logistic Regression to rank their prospects with a quality score which indicates that prospect’s likelihood to buy. The tool that makes this possible is called Logistic Regression and can be easily implemented in Excel.Ĭustomer Quality Scores Are Created With Logistic Regression Wouldn’t it be great if there was a more accurate way to predict whether your prospect will buy rather than just taking an educated guess? Well, there is…if you have enough data on your previous prospects. Logistic Regression in Excel Logistic Regression Analysis in Excel