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 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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