前言

使用python计算出每一组安斯库姆四重奏数据

步骤

1.引入库

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pandas import Series
from pandas import DataFrame
import sklearn
from sklearn import linear_model

2.读入数据

df = pd.read_csv('dataset.csv')

3.求出均值、方差、相关系数

x1=df.x1
y1=df.y1
x2=df.x2
y2=df.y2
x3=df.x3
y3=df.y3
x4=df.x4
y4=df.y4
print("x组的平均数为%.2f"%np.mean(x1),"%.2f"%np.mean(x2),"%.2f"%np.mean(x3),"%.2f"%np.mean(x4))
print("x组的方差为%.2f"%np.var(x1),"%.2f"%np.var(x2),"%.2f"%np.var(x3),"%.2f"%np.var(x4))
print("y组的平均数为%.2f"%np.mean(y1),"%.2f"%np.mean(y2),"%.2f"%np.mean(y3),"%.2f"%np.mean(y4))
print("y组的方差为%.2f"%np.var(y1),"%.2f"%np.var(y2),"%.2f"%np.var(y3),"%.2f"%np.var(y4))
print("xy的相关系数分别为%.3f"%x1.corr(y1),"%.3f"%x2.corr(y2),"%.3f"%x3.corr(y3),"%.3f"%x4.corr(y4))

4.利用斜率、截距画出线性回归线

reg = linear_model.LinearRegression()
reg.fit (np.array(x1).reshape(-1,1),np.array(y1).reshape(-1,1))
#斜率为
print("xy1直线的斜率为%.3f"%reg.coef_)
#截距为
print("xy1直线的截距为%.3f"%reg.intercept_)
plt.scatter(x1,y1)
plt.plot(x1,0.500*x1+3.00)
plt.show()

reg = linear_model.LinearRegression()
reg.fit (np.array(x2).reshape(-1,1),np.array(y2).reshape(-1,1))
 
#斜率为
print("xy2直线的斜率为%.3f"%reg.coef_)
#截距为
print("xy2直线的截距为%.3f"%reg.intercept_)
plt.scatter(x2,y2)
plt.plot(x2,0.500*x2+3.01)

reg = linear_model.LinearRegression()
reg.fit (np.array(x3).reshape(-1,1),np.array(y3).reshape(-1,1))
#斜率为
print("xy3直线的斜率为%.3f"%reg.coef_)
#截距为
print("xy3直线的截距为%.3f"%reg.intercept_)
plt.scatter(x3,y3)
plt.plot(x3,0.500*x3+3.01)

reg = linear_model.LinearRegression()
reg.fit (np.array(x4).reshape(-1,1),np.array(y4).reshape(-1,1))
 
#斜率为
print("xy4直线的斜率为%.3f"%reg.coef_)
#截距为
print("xy4直线的截距为%.3f"%reg.intercept_)
plt.scatter(x4,y4)
plt.plot(x4,0.500*x4+3.02)

最后

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