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[英]How can I plot best fit line in multiple Linear Regression if I have two independent variables and one dependent variable
[英]how to get equation for nonlinear mutivariate regression in which one variable is dependent on other two independent variables in python
我為like_so_(x,y,z)設置了5000個數據點,例如(0,1,50),其中x = 1,y = 2,z = 120.在這5000個腸道的幫助下,我必須得到一個方程式
給定x和y,方程應該能夠得到z的值
您可以使用statsmodels.ols
。 一些示例數據 - 假設您可以從(x, y, z)
數據創建pd.DataFrame
:
import pandas as pd
df = pd.DataFrame(np.random.randint(100, size=(150, 3)), columns=list('XYZ'))
df.info()
RangeIndex: 150 entries, 0 to 149
Data columns (total 3 columns):
X 150 non-null int64
Y 150 non-null int64
Z 150 non-null int64
現在估計線性回歸參數:
import numpy as np
import statsmodels.api as sm
model = sm.OLS(df['Z'], df[['X', 'Y']])
results = model.fit()
要得到:
results.summary())
OLS Regression Results
==============================================================================
Dep. Variable: Z R-squared: 0.652
Model: OLS Adj. R-squared: 0.647
Method: Least Squares F-statistic: 138.6
Date: Fri, 17 Jun 2016 Prob (F-statistic): 1.21e-34
Time: 13:48:38 Log-Likelihood: -741.94
No. Observations: 150 AIC: 1488.
Df Residuals: 148 BIC: 1494.
Df Model: 2
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [95.0% Conf. Int.]
------------------------------------------------------------------------------
X 0.5224 0.076 6.874 0.000 0.372 0.673
Y 0.3531 0.076 4.667 0.000 0.204 0.503
==============================================================================
Omnibus: 5.869 Durbin-Watson: 1.921
Prob(Omnibus): 0.053 Jarque-Bera (JB): 2.990
Skew: -0.000 Prob(JB): 0.224
Kurtosis: 2.308 Cond. No. 2.70
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
預測,使用:
params = results.params
params = results.params
df['predictions'] = model.predict(params)
產量:
X Y Z predictions
0 31 85 75 54.701830
1 36 46 43 34.828605
2 77 42 8 43.795386
3 78 84 65 66.932761
4 27 54 50 36.737606
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