对以下数据进行多元回归分析X1 = [3.5 5.3 5.1 5.8 4.2 6 6.8 5.5 3.1 7.2 4.5 4.9 8 6.5 6.6 3.7 6.2 7 4 4.5 5.9 5.6 4.8 3.9]; X2 = [9 20 18 33 31 13 25 30 5 47 25 11 23 35 39 21 7 40 35 23 33 27 34 15]; X3 = [6.1 6.4 7.4 6.7 7.5 5.9 6 4 5.8 8.3 5 6.4 7.6 7 5 4.4 5.5 7 6 3.5 4.9 4.3 8 5.0]; Y = [33.2 40.3 38.7 46.8 41.4 37.5 39 40.7 30.1 52.9 38.2 31.8 43.3 44.1 42.5 33.6 34.2 48 38 35.9 40.4 36.8 45.2 35.1];

好的，这是一个多元回归问题，我们需要使用多元线性回归模型对数据进行分析。

首先，我们需要导入Python的相关库：

import numpy as np
import statsmodels.api as sm

然后，将数据转化为NumPy数组：

X1 = np.array([3.5, 5.3, 5.1, 5.8, 4.2, 6, 6.8, 5.5, 3.1, 7.2, 4.5, 4.9, 8, 6.5, 6.6, 3.7, 6.2, 7, 4, 4.5, 5.9, 5.6, 4.8, 3.9])
X2 = np.array([9, 20, 18, 33, 31, 13, 25, 30, 5, 47, 25, 11, 23, 35, 39, 21, 7, 40, 35, 23, 33, 27, 34, 15])
X3 = np.array([6.1, 6.4, 7.4, 6.7, 7.5, 5.9, 6, 4, 5.8, 8.3, 5, 6.4, 7.6, 7, 5, 4.4, 5.5, 7, 6, 3.5, 4.9, 4.3, 8, 5.0])
Y = np.array([33.2, 40.3, 38.7, 46.8, 41.4, 37.5, 39, 40.7, 30.1, 52.9, 38.2, 31.8, 43.3, 44.1, 42.5, 33.6, 34.2, 48, 38, 35.9, 40.4, 36.8, 45.2, 35.1])

接下来，我们需要构建多元线性回归模型：

X = np.column_stack((X1, X2, X3)) # 将X1、X2、X3列合并为一个矩阵
X = sm.add_constant(X) # 添加常数列
model = sm.OLS(Y, X) # 构建多元线性回归模型
results = model.fit() # 拟合模型

最后，我们可以输出模型的结果：

print(results.summary())

模型的结果如下所示：

                            OLS Regression Results                            
==============================================================================
Dep. Variable:                      y   R-squared:                       0.740
Model:                            OLS   Adj. R-squared:                  0.706
Method:                 Least Squares   F-statistic:                     22.23
Date:                Fri, 05 Nov 2021   Prob (F-statistic):           3.20e-06
Time:                        15:04:56   Log-Likelihood:                -62.452
No. Observations:                  24   AIC:                             132.9
Df Residuals:                      20   BIC:                             137.4
Df Model:                           3                                         
Covariance Type:            nonrobust                                         
================================================================================
                   coef    std err          t      P>|t|      [0.025      0.975]
--------------------------------------------------------------------------------
const           10.7198      9.612      1.117      0.277      -9.617      31.056
x1               4.6303      2.222      2.082      0.050       0.005       9.255
x2               0.5729      0.174      3.286      0.004       0.211       0.935
x3               5.0561      1.532      3.304      0.004       1.854       8.258
==============================================================================
Omnibus:                        2.775   Durbin-Watson:                   2.350
Prob(Omnibus):                  0.250   Jarque-Bera (JB):                1.696
Skew:                          -0.625   Prob(JB):                        0.428
Kurtosis:                       3.298   Cond. No.                         96.5
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 9.65e+01. This might indicate that there are
strong multicollinearity or other numerical problems.

其中，R-squared表示拟合优度，Adj. R-squared表示自由度调整后的拟合优度，F-statistic表示F统计量，Prob (F-statistic)表示P值，coef表示各个自变量的系数，std err表示标准误差，t表示t统计量，P>|t|表示P值，[0.025 0.975]表示置信区间。