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Coefficients are basically the weights assigned to the features, based on their importance. Here, we have Y as our dependent variable, X’s are the independent variables and all C’s are the coefficients. Linear regression gives us an equation like this: It is a linear approach to statistically model the relationship between the dependent variable (the variable you want to predict) and the independent variables (the factors used for predicting). It’s also the most commonly used supervised learning technique in the industry. Linear Regression is the first machine learning technique most of us learn. Interpreting the Results of our Predictive Model.Implementing Linear Regression in Excel.Getting the All-Important Add Analytics ToolPak in Excel.Microsoft Excel: Formulas and Functions (Free Course!).I encourage you to check out the below resources if you’re a beginner in Excel and Business Analytics: 3 Classic Excel Tricks to Become an Efficient Analyst.5 Handy Excel Tricks for Conditional Formatting Every Analyst Should Know.5 Excel Tricks You’ll Love Working with as an Analyst.5 Useful Excel Tricks to Become an Efficient Analyst.5 Powerful Excel Dashboards for Analytics Professionals.3 Advanced Excel Charts Every Analytics Professional Should Try.I highly recommend going through the previous articles to become a more efficient analyst: This is the seventh article in my Excel for Analysts series. In this article, I am going to explain how to build a linear regression model in Excel and how to analyze the result so that you can become a superstar analyst! And we don’t need to be a master in Excel or Statistics to perform predictive modeling! We can easily build a simple model like linear regression in MS Excel that can help us perform analysis in a few simple steps. Microsoft Excel offers us the ability to conjure up predictive models without having to write complex code that flies over most people’s heads. Here’s the good news – they don’t need to.

Now you must be wondering how in the world will they build a complex statistical model that can predict these things? And learning analytics or hiring an analyst might be beyond their scope. Let me ask you a question – if the shops around you started collecting customer data, could they adopt a data-based strategy to sell their goods? Can they forecast their sales or estimate the number of products that might be sold?

This is followed by an incredulous look when I demonstrate how we can leverage the flexible nature of Excel to build predictive models for our data science and analytics projects.

That’s typically the first reaction I get when I bring up the subject. Here’s a step-by-step tutorial on how to build a linear regression model in Excel and how to interpret the results.You can perform predictive modeling in Excel in just a few steps.