import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt

# Sample data
data = {
    'neg_reviews': [4, 2, 3, 1, 5],
    'risk_perception': [2, 3, 1, 4, 2],
    'consumption_behavior': [5, 4, 3, 2, 1]
}

df = pd.DataFrame(data)

# Prepare the data
X = df[['neg_reviews', 'risk_perception']]
X = sm.add_constant(X)  # Add a constant term for the intercept
y = df['consumption_behavior']

# Fit the linear regression model
model = sm.OLS(y, X)
results = model.fit()

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

# Visualize the regression results
fig, ax = plt.subplots(figsize=(8, 6))
ax.scatter(df['neg_reviews'], y, label='Actual Consumption Behavior')
ax.plot(df['neg_reviews'], results.fittedvalues, 'r', label='Fitted Regression Line')
ax.set_xlabel('Negative Reviews')
ax.set_ylabel('Consumption Behavior')
ax.set_title('Stimuli-Organism-Response Model')
ax.legend()
plt.show()

# Interpretation of the results
print("Intercept:", results.params['const'])
print("Coefficient for 'neg_reviews':", results.params['neg_reviews'])
print("Coefficient for 'risk_perception':", results.params['risk_perception'])
print("R-squared:", results.rsquared)