Linear Regression in Economics Using SQL: Step-by-Step Guide with Simulated Data

This guide demonstrates how SQL can be used to understand and quantify economic relationships through linear regression, offering practical insights into modeling, interpretation, and evaluation within a database environment.

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1. Introduction

Linear regression is a powerful statistical technique widely used in economics to identify relationships between variables such as income and consumption, inflation and interest rates, or GDP and public spending. While tools like R or Python are typically used for regression modeling, SQL can be a surprisingly effective option for data stored in relational databases. This article demonstrates how to simulate economic data and perform a full linear regression analysis using only SQL.

2. Understanding Linear Regression in Economic Analysis

Linear regression attempts to model the relationship between a dependent variable (Y) and an independent variable (X) using the formula:

Y = β0 + β1 * X + ε

Where:

• Y: Dependent variable (e.g., consumer spending)

• X: Independent variable (e.g., GDP)

• β0: Intercept

• β1: Slope (effect of X on Y)

• ε: Error term

Economists use this to test hypotheses and make forecasts, such as how changes in GDP affect consumer behavior.