# Pastebin ziSfSGZD
import numpy as np
import pandas as pd
import statsmodels.api as sm

# Sample data
data = {
    'Year': [2010, 2011, 2012, 2013, 2014, 2015],
    'New_Home_Sales': [321, 361, 388, 442, 501, 550],
    'Durable_Goods_Orders': [125, 130, 135, 140, 145, 150],
    'Unemployment_Claims': [550, 520, 490, 460, 430, 400],
    'GDP': [14.87, 15.23, 15.6, 16.15, 16.91, 17.4]
}

df = pd.DataFrame(data)

# Define the features and target variable
X = df[['New_Home_Sales', 'Durable_Goods_Orders', 'Unemployment_Claims']]
y = df['GDP']

# Add a constant term to the features
X = sm.add_constant(X)

# Create and fit the multiple regression model
model = sm.OLS(y, X)
results = model.fit()

# Print the regression results
print(results.summary())

# Forecast GDP for a new set of data
new_data = {
    'New_Home_Sales': [590, 630, 670],
    'Durable_Goods_Orders': [155, 160, 165],
    'Unemployment_Claims': [370, 340, 320]
}

new_df = pd.DataFrame(new_data)

# Add a constant term to the new data
new_X = sm.add_constant(new_df)

# Make predictions using the fitted model
new_predictions = results.predict(new_X)

# Print the predicted GDP values
print("Predicted GDP:")
for i in range(len(new_predictions)):
    print(f"Input: {new_df.iloc[i]}, Predicted GDP: {new_predictions[i]}")