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. In regression analysis, those factors are called “variables.” You have your dependent variable - the main factor that you’re trying to understand or predict. It answers the questions: Which factors matter most? Which can we ignore? How do those factors interact with one another? And, perhaps most important, how certain are we about all these factors? Regression analysis is a way of mathematically sorting out which of those variables does indeed have an impact. The more rain we have, the more we sell.” “Six weeks after the competitor’s promotion, sales jump.” Perhaps people in your organization even have a theory about what will have the biggest effect on sales. 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 numbers. Redman offers this example scenario: Suppose you’re a sales manager trying to predict next month’s numbers. He also advises organizations on their data and data-quality programs. To better understand this method and how companies use it, I talked with Thomas Redman, author of Data Driven: Profiting from Your Most Important Business Asset. One of the most important types of data analysis is called regression analysis. But do you know how to parse through all the data available to you? The good news is that you probably don’t need 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.
0 kommentar(er)
