And in the past, for every additional inch of rain, you made an average of five more sales. So, historically, when it didn’t rain at all, you made an average of 200 sales and you can expect to do the same going forward assuming other variables stay the same. What this formula is telling you is that if there is no x then y = 200. It refers to the fact that regression isn’t perfectly precise. In addition to drawing the line, your statistics program also outputs a formula that explains the slope of the line and looks something like this: In other words, explains Redman, “The red line is the best explanation of the relationship between the independent variable and dependent variable.” This is called the regression line and it’s drawn (using a statistics program like SPSS or STATA or even Excel) to show the line that best fits the data. Then you plot all of that information on a chart that looks like this: So, in this case, let’s say you find out the average monthly rainfall for the past three years as well. (Reminder: you likely don’t have to do this yourself, but it’s helpful for you to understand the process your data analyst colleague uses.) You take all of your monthly sales numbers for, say, the past three years and any data on the independent variables you’re interested in. In order to conduct a regression analysis, you gather the data on the variables in question. And then you have your independent variables - the factors you suspect have an impact on your dependent variable. In Redman’s example above, the dependent variable is monthly sales. You have your dependent variable - the main factor that you’re trying to understand or predict. In regression analysis, those factors are called variables. It answers the questions: Which factors matter most? Which can we ignore? How do those factors interact with each other? And, perhaps most importantly, how certain are we about all of these factors? Regression analysis is a way of mathematically sorting out which of those variables does indeed have an impact. “Trust me. The more rain we have, the more we sell.” “Six weeks after the competitor’s promotion, sales jump.” Redman offers this example scenario: Suppose you’re a sales manager trying to predict next month’s numbers. You know that dozens, perhaps even hundreds of factors from the weather to a competitor’s promotion to the rumor of a new and improved model can impact the number. Perhaps people in your organization even have a theory about what will have the biggest effect on sales. To better understand this method and how companies use it, I talked with Tom Redman, author of Data Driven: Profiting from Your Most Important Business Asset. He also advises organizations on their data and data quality programs. One of the most important types of data analysis is regression analysis.
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But do you know how to parse through all of the data available to you? The good news is that you likely don’t have to do the number crunching yourself (hallelujah!) but you do need to correctly understand and interpret the analysis created by your colleagues. You probably know by now that whenever possible you should be making data-driven decisions at work.
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