首先,我们需要导入所需的库,包括pandas用于数据处理和分析,以及statsmodels库用于建立回归模型:
import pandas as pd
import statsmodels.api as sm
接下来,我们将给出数据并创建一个DataFrame对象:
data = {'Y': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
'X1': [-62.8, 3.3, -120.8, -18.1, -3.8, -61.2, -20.3, -194.5, 20.8, -106.1, -39.4, -164.1, -308.9, 7.2, -118.3, -185.9, -34.6, -27.9, -48.2, -49.2, -19.2, -18.1, -98.0, -129.0, -4.0, -8.7, -59.2, -13.1, -38.0, -57.9, -8.8, -64.7, -11.4, 43.0, 47.0, -3.3, 35.0, 46.7, 20.8, 33.0, 26.1, 68.6, 37.3, 59.0, 49.6, 12.5, 37.3, 35.3, 49.5, 18.1, 31.4, 21.5, 8.5, 40.6, 34.6, 19.9, 17.4, 54.7, 53.5, 35.9, 39.4, 53.1, 39.8, 59.5, 16.3, 21.7],
'X2': [-89.5, -3.5, -103.2, -28.8, -50.6, -56.2, -17.4, -25.8, -4.3, -22.9, -35.7, -17.7, -65.8, -22.6, -34.2, -280.0, -19.4, 6.3, 6.8, -17.2, -36.7, -6.5, -20.8, -14.2, -15.8, -36.3, -12.8, -17.6, 1.6, 0.7, -9.1, -4.0, 4.8, 16.4, 16.0, 4.0, 20.8, 12.6, 12.5, 23.6, 10.4, 13.8, 33.4, 23.1, 23.8, 7.0, 34.1, 4.2, 25.1, 13.5, 15.7, -14.4, 5.8, 5.8, 26.4, 26.7, 12.6, 14.6, 20.6, 26.4, 30.5, 7.1, 13.8, 7.0, 20.4, -7.8],
'X3': [1.7, 1.1, 2.5, 1.1, 0.9, 1.7, 1.0, 0.5, 1.0, 1.5, 1.2, 1.3, 0.8, 2.0, 1.5, 6.7, 3.4, 1.3, 1.6, 0.3, 0.8, 0.9, 1.7, 1.3, 2.1, 2.8, 2.1, 0.9, 1.2, 0.8, 0.9, 0.1, 0.9, 1.3, 1.3, 1.9, 2.7, 1.9, 0.9, 2.4, 1.5, 2.1, 1.6, 3.5, 5.5, 1.9, 1.8, 1.5, 2.6, 4.0, 1.9, 1.0, 1.5, 1.8, 1.8, 2.3, 1.3, 1.7, 1.1, 2.0, 1.9, 1.9, 1.2, 2.0, 1.0, 1.6]}
df = pd.DataFrame(data)
接下来,我们需要进行回归分析。首先,我们定义自变量X和因变量y:
X = df[['X1', 'X2', 'X3']]
y = df['Y']
然后,我们向X添加常数列(截距项):
接下来,我们使用最小二乘法来建立模型,并进行拟合:
model = sm.OLS(y, X).fit()
最后,我们可以打印模型的摘要及系数:
完整的代码如下:
```python
import pandas as pd
import statsmodels.api as sm
data = {'Y': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1
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