Linear Regression in Economics Using R: A Complete Guide with Interpretation and Visualization

This article demonstrates how linear regression in R can be used to analyze, interpret, and visualize economic relationships, helping economists draw meaningful conclusions from data.

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1. Introduction

Linear regression is a foundational statistical method in economics, used to quantify relationships between economic variables such as income and consumption, inflation and interest rates, or GDP and public spending. R, with its built-in statistical capabilities and visualization tools, is an excellent platform for economic modeling and analysis. This guide walks through a complete linear regression example in R using simulated economic data.

2. Understanding Linear Regression in Economic Context

The linear regression model seeks to explain a dependent variable (Y) using one or more independent variables (X). The simple linear regression equation is:

Y = β0 + β1 * X + ε

Where:

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

• X: Independent variable (e.g., GDP)

• β0: Intercept (baseline value of Y when X is zero)

• β1: Slope (change in Y per unit change in X)

• ε: Random error term

This model is commonly used in economics to test hypotheses, forecast trends, and understand causal relationships.

3. Setting Up the R Environment

Before proceeding, ensure you have the necessary packages installed and loaded:

install.packages(c("tidyverse", "broom"))
library(tidyverse)
library(broom)

These packages will help us handle data, run regressions, and produce clean visualizations.