使用Python的statsmodels模块进行多元线性回归分析

喜欢ヅ旅行 2023-02-18 01:42 1阅读 0赞

**运行环境：** `win7`、`python3.6`  
**实现功能：** 对多个参数进行回归分析，得出回归方程，回归统计量P值等

--------------------

## 代码： ##

创建`statsmodels_test.py`  
将下面代码复制到该py文件

from pandas import DataFrame
    import statsmodels.api as sm
    #import statsmodels.regression.linear_model as sm
    import pandas as pd
    
    '''
    # 测试集
    Stock_Market = {'Year': [2017,2017,2017,2017,2017,2017,2017,2017,2017,2017,2017,2017,2016,2016,2016,2016,2016,2016,2016,2016,2016,2016,2016,2016],
                    'Month': [12, 11,10,9,8,7,6,5,4,3,2,1,12,11,10,9,8,7,6,5,4,3,2,1],
                    'Interest_Rate': [2.75,2.5,2.5,2.5,2.5,2.5,2.5,2.25,2.25,2.25,2,2,2,1.75,1.75,1.75,1.75,1.75,1.75,1.75,1.75,1.75,1.75,1.75],
                    'Unemployment_Rate': [5.3,5.3,5.3,5.3,5.4,5.6,5.5,5.5,5.5,5.6,5.7,5.9,6,5.9,5.8,6.1,6.2,6.1,6.1,6.1,5.9,6.2,6.2,6.1],
                    'Stock_Index_Price': [1464,1394,1357,1293,1256,1254,1234,1195,1159,1167,1130,1075,1047,965,943,958,971,949,884,866,876,822,704,719]        
                    }
    
    df = DataFrame(Stock_Market,columns=['Year','Month','Interest_Rate','Unemployment_Rate','Stock_Index_Price']) 
    
    X = df[['Interest_Rate','Unemployment_Rate']] # here we have 2 variables for multiple regression. If you just want to use one variable for simple linear regression, then use X = df['Interest_Rate'] for example.Alternatively, you may add additional variables within the brackets
    Y = df['Stock_Index_Price']
    
    X = sm.add_constant(X) # adding a constant
    
    model = sm.OLS(Y, X).fit()
    predictions = model.predict(X) 
    
    print_model = model.summary()
    print(print_model)
    '''
    
    
    #读取文件
    datafile = u'cig_data.xlsx'#文件所在位置，u为防止路径中有中文名称，此处没有，可以省略
    data = pd.read_excel(datafile)#datafile是excel文件，所以用read_excel,如果是csv文件则用read_csv
    examDf = DataFrame(data)
    print("GOOD")
    new_examDf = examDf.ix[1:, 1:]
    X = new_examDf.ix[:,:4]
    Y = new_examDf.ix[:,4]
    
    
    X = sm.add_constant(X) # adding a constant
    
    model = sm.OLS(Y, X).fit()
    predictions = model.predict(X) 
    
    print_model = model.summary()
    print(print_model)

读取的`data.xlsx`文件：[传送门][Link 1]

# 运行结果： #

OLS Regression Results
    ==============================================================================
    Dep. Variable:                Day_abs   R-squared:                       0.056
    Model:                            OLS   Adj. R-squared:                  0.039
    Method:                 Least Squares   F-statistic:                     3.238
    Date:                Mon, 15 Jun 2020   Prob (F-statistic):             0.0132
    Time:                        00:54:57   Log-Likelihood:                -1392.7
    No. Observations:                 223   AIC:                             2795.
    Df Residuals:                     218   BIC:                             2812.
    Df Model:                           4
    Covariance Type:            nonrobust
    ==============================================================================
                     coef    std err          t      P>|t|      [0.025      0.975]
    ------------------------------------------------------------------------------
    const         62.1170     85.299      0.728      0.467    -105.999     230.233
    Age            0.1967      0.692      0.284      0.777      -1.168       1.561
    Cig_Day        1.3202      0.705      1.873      0.062      -0.069       2.710
    CO            -0.2645      0.103     -2.566      0.011      -0.468      -0.061
    LogCOadj       0.0313      0.069      0.458      0.648      -0.104       0.166
    ==============================================================================
    Omnibus:                       54.065   Durbin-Watson:                   1.813
    Prob(Omnibus):                  0.000   Jarque-Bera (JB):               86.116
    Skew:                           1.475   Prob(JB):                     2.00e-19
    Kurtosis:                       3.756   Cond. No.                     1.45e+04
    ==============================================================================

[Link 1]: https://mp.csdn.net/console/upDetailed