Python - regresion polinomial

import numpy as np

import math

x = np.array([0, 1, 2, 3, 4, 5])

y = np.array([5, 5, 13, 24, 36, 65])

xala2 = x**2

xala3 = x**3

xala4 = x**4

xpory = np.multiply(x, y)

xala2pory = (x**2)*y

sumax = np.sum(x)

sumay = np.sum(y)

suma_xala2 = np.sum(xala2)

suma_xala3 = np.sum(xala3)

suma_xala4 = np.sum(xala4)

suma_xpory = np.sum(xpory)

suma_xala2pory = np.sum(xala2pory)

n = len(x)

print(x)

print(y)

print(xala2)

print(xala3)

print(xala4)

print(xpory)

print(xala2pory)

print('')

print("sumatoria x: ",sumax)

print("sumatoria y: ",sumay)

print("sumatoria x^2: ",suma_xala2)

print("sumatoria x^3: ",suma_xala3)

print("sumatoria x^4: ",suma_xala4)

print("sumatoria x*y: ",suma_xpory)

print("sumatoria x^2*y: ",suma_xala2pory)

print('')

print("ecuacion 1 => ",n,"a0 +",sumax,"a1 +",suma_xala2,"a2 =",sumay)

print("ecuacion 2 => ",sumax,"a0 +",suma_xala2,"a1 +",suma_xala3,"a2 =",suma_xpory)

print("ecuacion 3 => ",suma_xala2,"a0 +",suma_xala3,"a1 +",suma_xala4,"a2 =",suma_xala2pory)

a = np.array([[n,sumax,suma_xala2],[sumax,suma_xala2,suma_xala3],[suma_xala2,suma_xala3,suma_xala4]])

b = np.array([sumay,suma_xpory,suma_xala2pory])

z = np.linalg.solve(a,b)

print('')

print("el sistema de ecuaciones lineales resuelto: ")

print(z)

print('')

print("ingrese los valores de a0, a1 y a2: ")

a0 = float(input("valor de a0 = "))

a1 = float(input("valor de a1 = "))

a2 = float(input("valor de a2 = "))

print('')

print("ecuacion: ")

print("y = ",a0,"+",a1,"x +",a2,"x^2")

promediox = np.mean(x)

promedioy = np.mean(y)

st = (y-promedioy)**2

suma_st = np.sum(st)

sr = (y-a0-(a1*x)-(a2*(x**2)))**2

suma_sr = np.sum(sr)

sy = math.sqrt((suma_st/(n-1)))

sydivx = math.sqrt((suma_sr/(n-2)))

print('')

print("promedio de x = ",promediox)

print("promedio de y = ",promedioy)

print('')

print("st:")

print(st)

print("sr:")

print(sr)

print('')

print("sumatoria st = ",suma_st)

print("sumatoria sr = ",suma_sr)

print('')

print("sy = ",sy)

print("sy/x = ",sydivx)

if (sydivx < sy):

    print("el modelo es adecuado")

    r = math.sqrt(((suma_st-suma_sr)/suma_st))

    rfinal = r*100

    print('coeficiente de correlacion(r) = ',rfinal)

else:

    print('El modelo no es adecuado')

import numpy as np

x = np.array([0,1,2,3,4,5])

y = np.array([5,5,13,24,36,65])

def potencia(lista,elevado):

    return pow(lista,elevado)

def suma(lista):

    return np.sum(lista)

def multiplicar(uno,dos):

    return np.multiply(uno,dos)

print(f"""vector x : {x}

vector y : {y}

vector x elevado a 2 : {potencia(x,2)}

vector x elevado a 3 : {potencia(x,3)}

vector x elevado a 4 : {potencia(x,4)}

vector x multiplicado por vector y: {multiplicar(x,y)}

""")