线性回归在假设特证满⾜线性关系,根据给定的训练数据训练⼀个模型,并⽤此模型进⾏预测。⼆、代码: 1import numpy as np
2from matplotlib import pyplot as plt
3X=np.array([2,3,4,5,6])
4Y=2*X+al(1,2,5)
5plt.scatter(X,Y)
6x_an(X)
7y_an(Y)
8n=0.0
9d=0.0
10for x,y in zip(X,Y):
11 n+=(x-x_mean)*(y-y_mean)
12 d+=(x-x_mean)**2
13a=n/d偏振子
14b=y_mean-a*x_mean黄粉虫筛选机
便携式鱼缸15y_predict=[a*x+b for x in X]betal
16plt.scatter(X,Y)
17plt.plot(X,y_predict,color='r')
18ss_residual=sum((y_predict-Y)**2)
19ss_total=sum((Y-y_mean)**2)
20score=1-ss_residual/ss_total
21print(score)
22n=5
23betal_hat=a
24se_model=np.sqrt(ss_residual/(n-2))
25sss=np.sqrt(sum((X-x_mean)**2))
26t_val=betal_hat/(se_model/sss)
27from scipy.stats import t
28p_val=2*(1-t.cdf(t_val,n-2))
29print(p_val)
内外网切换器三、结果:
防撞钢梁
假设检验结果:
⼩于0.05,拒绝零假设,有线性关系。