Correction method and code of medical gender, age and years of Education

@ Founded in ：2022.07.17
@ Modified to ：2022.07.17

1、 Parameter Introduction

statsmodels.regression.linear_model.OLS(endog, exog=None, missing='none', hasconst=None, **kwargs)


Parameters

    endog: array_like
        A 1-d endogenous response variable. The dependent variable.
        
    exog: array_like
        A nobs x k array where nobs is the number of observations and k is the number of regressors. An intercept is not included by default and should be added by the user. See statsmodels.tools.add_constant.

    missing: str
        Available options are ‘none’, ‘drop’, and ‘raise’. If ‘none’, no nan checking is done. If ‘drop’, any observations with nans are dropped. If ‘raise’, an error is raised. Default is ‘none’.
    
    hasconst: None or bool
        Indicates whether the RHS includes a user-supplied constant. If True, a constant is not checked for and k_constant is set to 1 and all result statistics are calculated as if a constant is present. If False, a constant is not checked for and k_constant is set to 0.

 instructions  RHS  Whether to include constants provided by users .
 If  True, Then the constant is not checked and  k_constant  Set to  1, And all the result statistics are calculated in the way that there are constants .
 If  False, Then the constant is not checked and  k_constant  Set to  0.

statsmodels.regression.linear_model.OLS

2、 Official website code

>>> import statsmodels.api as sm
>>> import numpy as np
>>> duncan_prestige = sm.datasets.get_rdataset("Duncan", "carData")
>>> Y = duncan_prestige.data['income']
>>> X = duncan_prestige.data['education']
>>> X = sm.add_constant(X)
>>> model = sm.OLS(Y,X)
>>> results = model.fit()
>>> results.params
const        10.603498
education     0.594859
dtype: float64

>>> results.tvalues
const        2.039813
education    6.892802
dtype: float64

>>> print(results.t_test([1, 0]))
                             Test for Constraints
==============================================================================
                 coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------
c0            10.6035      5.198      2.040      0.048       0.120      21.087
==============================================================================

>>> print(results.f_test(np.identity(2)))
<F test: F=array([[159.63031026]]), p=1.2607168903696672e-20, df_denom=43, df_num=2>

3、 Reference material

python in statsmodels Modular API Interface

python statsmodel Regression result extraction （ Regression coefficient 、t value 、pvalue、R Fang ）

python statsmodel Regression result extraction （R Fang T value P-value)

stay scikit Study LinearRegression Find p value （ Importance ）

python Linear regression Statistical tests p value _Python New choice of Statistics ：pingouin