Linear Regression for Economic Data
Regression analysis is a fundamental tool in economics, allowing you to quantify the relationship between variables such as income and consumption, wages and education, or investment and interest rates. By applying regression, you can move beyond simple description and begin to model, predict, and test hypotheses about economic behavior. Understanding these relationships helps economists guide policy, forecast trends, and make informed decisions based on data-driven insights.
123456789101112131415161718192021import pandas as pd from sklearn.linear_model import LinearRegression # Hardcoded economic data: income and consumption data = { "income": [30_000, 40_000, 50_000, 60_000, 70_000], "consumption": [20_000, 25_000, 30_000, 35_000, 38_000] } df = pd.DataFrame(data) # Reshape income for scikit-learn (expects 2D array for features) X = df[["income"]] y = df["consumption"] # Fit linear regression model model = LinearRegression() model.fit(X, y) # Display coefficients print("Intercept:", model.intercept_) print("Slope (coefficient):", model.coef_[0])
To perform linear regression using scikit-learn, you first organize your data, typically with independent variables (like income) as features and the dependent variable (like consumption) as the target. The model fitting process identifies the best-fitting line through the data by minimizing the sum of squared errors between actual and predicted consumption. The intercept represents the expected consumption when income is zero, while the slope (coefficient) quantifies how much consumption changes for each additional unit of income. Once fitted, you can use the model to make predictions for new income values, providing actionable economic insights.
1234# Predict consumption for new income values new_income = pd.DataFrame({"income": [45_000, 65_000]}) predicted_consumption = model.predict(new_income) print("Predicted consumption for incomes 45,000 and 65,000:", predicted_consumption)
1. What does the slope coefficient in a regression model represent?
2. Fill in the blank: To fit a linear regression model in scikit-learn, you would use _ _ _.
3. Why is regression analysis useful for economists?
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Regression analysis is a fundamental tool in economics, allowing you to quantify the relationship between variables such as income and consumption, wages and education, or investment and interest rates. By applying regression, you can move beyond simple description and begin to model, predict, and test hypotheses about economic behavior. Understanding these relationships helps economists guide policy, forecast trends, and make informed decisions based on data-driven insights.
123456789101112131415161718192021import pandas as pd from sklearn.linear_model import LinearRegression # Hardcoded economic data: income and consumption data = { "income": [30_000, 40_000, 50_000, 60_000, 70_000], "consumption": [20_000, 25_000, 30_000, 35_000, 38_000] } df = pd.DataFrame(data) # Reshape income for scikit-learn (expects 2D array for features) X = df[["income"]] y = df["consumption"] # Fit linear regression model model = LinearRegression() model.fit(X, y) # Display coefficients print("Intercept:", model.intercept_) print("Slope (coefficient):", model.coef_[0])
To perform linear regression using scikit-learn, you first organize your data, typically with independent variables (like income) as features and the dependent variable (like consumption) as the target. The model fitting process identifies the best-fitting line through the data by minimizing the sum of squared errors between actual and predicted consumption. The intercept represents the expected consumption when income is zero, while the slope (coefficient) quantifies how much consumption changes for each additional unit of income. Once fitted, you can use the model to make predictions for new income values, providing actionable economic insights.
1234# Predict consumption for new income values new_income = pd.DataFrame({"income": [45_000, 65_000]}) predicted_consumption = model.predict(new_income) print("Predicted consumption for incomes 45,000 and 65,000:", predicted_consumption)
1. What does the slope coefficient in a regression model represent?
2. Fill in the blank: To fit a linear regression model in scikit-learn, you would use _ _ _.
3. Why is regression analysis useful for economists?
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