Hello, I am just loving plotly/dash. I was just wondering how I can just show a raw table as I get it in Python as output. Below an example. My wish would be just to show the print(res.summary()).
# Load modules and data
In [1]: import numpy as np
In [2]: import statsmodels.api as sm
In [3]: spector_data = sm.datasets.spector.load(as_pandas=False)
In [4]: spector_data.exog = sm.add_constant(spector_data.exog, prepend=False)
# Fit and summarize OLS model
In [5]: mod = sm.OLS(spector_data.endog, spector_data.exog)
In [6]: res = mod.fit()
In [7]: print(res.summary())
OLS Regression Results
==============================================================================
Dep. Variable: y R-squared: 0.416
Model: OLS Adj. R-squared: 0.353
Method: Least Squares F-statistic: 6.646
Date: Fri, 21 Feb 2020 Prob (F-statistic): 0.00157
Time: 13:59:19 Log-Likelihood: -12.978
No. Observations: 32 AIC: 33.96
Df Residuals: 28 BIC: 39.82
Df Model: 3
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
x1 0.4639 0.162 2.864 0.008 0.132 0.796
x2 0.0105 0.019 0.539 0.594 -0.029 0.050
x3 0.3786 0.139 2.720 0.011 0.093 0.664
const -1.4980 0.524 -2.859 0.008 -2.571 -0.425
==============================================================================
Omnibus: 0.176 Durbin-Watson: 2.346
Prob(Omnibus): 0.916 Jarque-Bera (JB): 0.167
Skew: 0.141 Prob(JB): 0.920
Kurtosis: 2.786 Cond. No. 176.
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.