Linear Regression in Finance and Macroeconomics Using Power BI with DAX and M: A Complete Step-by-Step Guide

Article Outline:

1. Introduction

   • The growing importance of data visualization and analytics in finance and macroeconomics

   • How linear regression supports economic forecasting and financial decision-making

   • Why Power BI, with its DAX and M languages, is a powerful platform for regression-based analysis

2. Understanding Linear Regression in Finance and Economics

   • Definition and basic components: intercept, slope, residual, and R²

   • Typical use cases:

     • Analysing the impact of interest rates on inflation

     • Estimating GDP growth from investment rates

     • Modeling stock price returns relative to market indices

3. Setting Up the Problem: Investment vs GDP Growth

   • Description of the economic question: how does investment (% of GDP) affect GDP growth?

   • Data structure overview:

     • Columns: Year, Investment (%), GDP Growth Rate (%)

   • Outcome: create a regression model in Power BI that dynamically estimates this relationship

4. Loading and Transforming Data Using Power Query (M Language)

   • Importing data via Excel or manual entry in Power BI Desktop

   • Using M to clean and transform the dataset

   • Ensuring appropriate data types and column formatting

5. Building Linear Regression Calculations Using DAX

   • Creating DAX measures for:

     • Average X (investment) and Y (GDP growth)

     • Covariance, variance, slope, and intercept

     • Predicted values and residuals

     • R² score for model evaluation

   • Best practices for writing efficient and readable DAX expressions

6. Visualising Regression Results in Power BI

   • Creating a scatter plot with investment vs GDP growth

   • Overlaying the regression line using calculated columns or measures

   • Displaying key statistics (slope, intercept, R²) in dynamic cards

7. Interpreting the Results in Economic Context

   • What the slope and intercept reveal about economic behavior

   • Using R² to assess model fit

   • How the visual and numeric insights can inform macroeconomic policy or financial investment decisions

8. Using the Regression Model for Forecasting and Scenario Analysis

   • Enabling users to input hypothetical investment values

   • Predicting GDP growth outcomes using DAX measures

   • Creating an interactive what-if analysis with slicers or parameter tables

9. Conclusion

   • Recap of how DAX and M in Power BI can be used to perform end-to-end regression analysis

   • The benefit of combining statistical modeling with dynamic visuals for financial and economic analytics

   • Encouragement to adapt the model for broader use cases in corporate finance, development economics, or capital markets

This article teaches how to build, visualize, and interpret a linear regression model in Power BI using DAX and M, empowering users to analyse financial and macroeconomic relationships such as the impact of investment on GDP growth with dynamic, interactive tools.

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