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Regression Analysis: A Primer - KDnuggest

Regression Analysis: A Primer - KDnuggest | The MarTech Digest | Scoop.it
Regression is arguably the workhorse of statistics. Despite its popularity, however, it may also be the most misunderstood. Why? The answer might surprise you: There is no such thing as Regression. Rather, there are a large number of statistical methods that are called Regression or grounded on its fundamental idea:

Dependent Variable = Constant + Slope*Independent Variable + Error
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A Refresher on Regression Analysis - HBR

A Refresher on Regression Analysis - HBR | The MarTech Digest | Scoop.it
You probably know by now that whenever possible you should be making data-driven decisions at work. 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. One of the most important types of data analysis is regression.

Regression analysis is a way of mathematically sorting out which of those variables does indeed have an impact. 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?

In regression analysis, those factors are called variables. You have your dependent variable — the main factor that you’re trying to understand or predict. In Redman’s example above, the dependent variable is monthly sales. And then you have your independent variables — the factors you suspect have an impact on your dependent variable.


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If you've forgotten or you just need a quicker refresher, here you go.

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