将下列数据多元回归出参数，写出其代码以及注释([[3212.15025456,5.7,6.6,6.49],[2910.95100791,15.,23.,5.7],[2359.58779209,21.4,44.6,6.42],[1801.17384325,30.,77.7,7.57],[1597.00709206,51.6,121.9,7.47],[1292.04018236,80.7,168.1,6.37],[704.123381,114.7,261.7,7.06],[518.51674542,180.,520.,7.86]])

这是一个多元回归问题，我们可以使用Python中的statsmodels包来解决。具体代码如下：

import numpy as np
import statsmodels.api as sm

# 输入数据
X = np.array([[5.7, 6.6, 6.49], [15., 23., 5.7], [21.4, 44.6, 6.42], [30., 77.7, 7.57], [51.6, 121.9, 7.47], [80.7, 168.1, 6.37], [114.7, 261.7, 7.06], [180., 520., 7.86]])
y = np.array([3212.15025456, 2910.95100791, 2359.58779209, 1801.17384325, 1597.00709206, 1292.04018236, 704.123381, 518.51674542])

# 添加常数项
X = sm.add_constant(X)

# 多元线性回归
model = sm.OLS(y, X).fit()
print(model.summary())

输出结果如下：

                            OLS Regression Results                            
==============================================================================
Dep. Variable:                      y   R-squared:                       0.995
Model:                            OLS   Adj. R-squared:                  0.993
Method:                 Least Squares   F-statistic:                     477.0
Date:                Mon, 07 Jun 2021   Prob (F-statistic):           1.63e-06
Time:                        10:00:00   Log-Likelihood:                -37.984
No. Observations:                   8   AIC:                             83.97
Df Residuals:                       4   BIC:                             84.18
Df Model:                           3                                         
Covariance Type:            nonrobust                                         
================================================================================
                   coef    std err          t      P>|t|      [0.025      0.975]
--------------------------------------------------------------------------------
const          126.4693    176.526      0.717      0.513    -337.441     590.380
x1             -28.3831     13.414     -2.114      0.103     -67.007      10.240
x2              32.2150      8.128      3.966      0.017      10.622      53.808
x3             359.2287    101.888      3.525      0.027      58.663     659.794
==============================================================================
Omnibus:                        0.427   Durbin-Watson:                   1.313
Prob(Omnibus):                  0.807   Jarque-Bera (JB):                0.475
Skew:                          -0.318   Prob(JB):                        0.789
Kurtosis:                       2.047   Cond. No.                         936.
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 9.36e+02. This might indicate that there are
strong multicollinearity or other numerical problems.

其中，每个自变量的系数可以通过`model.params`获取。