But it’s much easier with the Data Analysis Tool Pack, which you can enable from the Developer Tab -> Excel Add-ins.

There are ways to calculate all the relevant statistics in Excel using formulas. Looking at the development over the periods, we can assume that GDP increases together with Education Spend and Employee Compensation. X2 - Unemployment Rate as % of the Labor Force Įven before we run our regression model, we notice some dependencies in our data.As a massive fan of Agatha Christie’s Hercule Poirot, let’s direct our attention to Belgium.Īs you can see in the table below, we have nineteen observations of our target variable (GDP), as well as our three predictor variables: The EU dataset gives us information for all member states of the union.

I have also kept the links to the source tables to explore further if you want. All the relevant source data is within the model file for your convenience, which you can download below. We will obtain public data from Eurostat, the statistics database for the European Commission for this exercise. Now that we have this out of the way and expectations are set, let’s open Excel and get started! Sourcing our data The article aims to show you how to run multiple Regression in Excel and interpret the output, not to teach about setting up our model assumptions and choosing the most appropriate variables. I am not a statistician, and I do not claim that the selected dependent and independent variables are the right analysis choices. This article will take a practical look at modeling a Multiple Regression model for the Gross Domestic Product (GDP) of a country.īefore I start, let me add a short disclaimer. You can read our Regression Analysis in Financial Modeling article to gain more insight into the statistical concepts employed in the method and where it finds application within finance. In a previous article, we explored Linear Regression Analysis and its application in financial analysis and modeling.

Regression data analysis tool in excel