背景
計(jì)算線性回歸a、b值后，希望計(jì)算r-squared，對(duì)比是否為最佳擬合
Excel擬合
數(shù)據(jù)源
擬合結(jié)果
Python擬合
import math
import numpy as np
from scipy import stats
testX = [174.5,171.2,172.9,161.6,123.6,112.1,107.1,98.6,98.7,97.5,95.8,93.5,91.1,85.2,75.6,72.7,68.6,69.1,63.8,60.1,65.2,71,75.8,77.8]
testY = [88.3,87.1,88.7,85.8,89.4,88,83.7,73.2,71.6,71,71.2,70.5,69.2,65.1,54.8,56.7,62,68.2,71.1,76.1,79.8,80.9,83.7,85.8]
def computeCorrelation(X, Y):
xBar = np.mean(X)
yBar = np.mean(Y)
SSR = 0
varX = 0
varY = 0
for i in range(0 , len(X)):
diffXXBar = X[i] - xBar
diffYYBar = Y[i] - yBar
SSR += (diffXXBar * diffYYBar)
varX += diffXXBar**2
varY += diffYYBar**2
SST = math.sqrt(varX * varY)
print ("使用math庫(kù)：r：", SSR / SST,"r-squared：", (SSR / SST)**2)
return
computeCorrelation(testX, testY)
x = np.array(testX)
y = np.array(testY)
#擬合 y = ax + b
poly = np.polyfit(x, y, deg=1)
print("使用numpy庫(kù)：a：" + str(poly[0]) + "，b：" + str(poly[1]))
def rsquared(x, y):
slope, intercept, r_value, p_value, std_err = stats.linregress(x, y)
#a、b、r
print("使用scipy庫(kù)：a：",slope,"b：", intercept,"r：", r_value,"r-squared：", r_value**2)
rsquared(testX, testY)
Python擬合結(jié)果
