import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
 
# Datos
data = {
    'Tiempo_entrega': [9.95, 24.45, 31.75, 35.00, 25.02, 16.8, 14.38, 9.60, 24.35, 27.50, 
                       17.08, 37.00, 41.95, 11.66, 21.65, 17.89, 69.00, 10.30, 34.93, 46.59, 
                       44.88, 54.12, 56.63, 22.13, 21.15],
    'Numero_latas': [2, 8, 11, 10, 8, 4, 2, 2, 9, 8, 4, 11, 12, 2, 4, 4, 20, 1, 10, 15, 
                     15, 16, 17, 6, 5],
    'Distancias_pies': [50, 110, 120, 550, 295, 200, 375, 52, 100, 300, 412, 400, 500, 
                        360, 205, 400, 600, 585, 540, 250, 290, 510, 590, 100, 400]
}
 
# Convertir a DataFrame
df = pd.DataFrame(data)
 
# Variables predictoras (X) y variable de respuesta (y)
X = df[['Numero_latas', 'Distancias_pies']]
y = df['Tiempo_entrega']
 
# Modelo de regresión lineal
regression_model = LinearRegression()
 
# Ajustar el modelo
regression_model.fit(X, y)
 
# Coeficientes de correlación y determinación múltiple
R = np.corrcoef(df.values.T)[0,1]
R_squared = r2_score(y, regression_model.predict(X))
 
# Error estándar de la estimación múltiple
n = len(df)
p = 2  # número de variables predictoras
y_pred = regression_model.predict(X)
residuals = y - y_pred
MSE = np.sum(residuals**2) / (n - p - 1)
SEM = np.sqrt(MSE)
 
# Coeficientes de la regresión
coeficientes = regression_model.coef_
intercepto = regression_model.intercept_
 
# Mostrar resultados
print("Coeficiente de correlación (R):", R)
print("Coeficiente de determinación (R^2):", R_squared)
print("Error estándar de la estimación múltiple (SEM):", SEM)
print("Coeficientes de la regresión:")
print("Intercepto:", intercepto)
print("Coeficientes de las variables predictoras:", coeficientes)
 
 

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