用python实现一元线性回归

以下是使用Python实现一元线性回归的示例代码：

import numpy as np
import matplotlib.pyplot as plt

# 输入数据
x = np.array([1, 2, 3, 4, 5])
y = np.array([3, 5, 7, 9, 11])

# 计算均值
x_mean = np.mean(x)
y_mean = np.mean(y)

# 计算斜率和截距
num = 0
denom = 0
for i in range(len(x)):
    num += (x[i] - x_mean) * (y[i] - y_mean)
    denom += (x[i] - x_mean) ** 2
slope = num / denom
intercept = y_mean - slope * x_mean

# 输出结果
print("斜率：", slope)
print("截距：", intercept)

# 绘制数据和拟合直线
plt.scatter(x, y)
plt.plot(x, slope * x + intercept, 'r')
plt.show()

输出：

斜率： 2.0
截距： 1.0

绘制的图形如下所示：