By syuoni (https://github.com/syuoni)
This is an implementation of statistic methods with Python (numpy, pandas, scipy, etc.).
It currently supports:
-
OLS, WLS, 2SLS
-
MLE (e.g., binary and duration models)
-
Non- and semi-parametric models
- Kernel density estimation
- Local polynomial regression
- Robinson regression
# -*- coding: utf-8 -*-
import pandas as pd
from statspy.ols import OrdinaryLeastSquare
df = pd.read_stata('example-data/womenwk.dta')
ols_md = OrdinaryLeastSquare(df, 'work', ['age', 'married', 'children', 'education'])
ols_md.fit() ======================================================================
method OLS
robust False
obs 2000
RMSE 0.41992
SSE 89.3922
SSR 351.783
SST 441.175
R-sq 0.202623
adj-R-sq 0.201024
F-stat 126.738
Prob(F) 1.11022e-16
dtype: object
======================================================================
Coef Std.Err t p CI.lower CI.upper
age 0.010255 0.001227 8.358393 0.000000e+00 0.007849 0.012661
married 0.111112 0.021948 5.062567 4.516572e-07 0.068069 0.154154
children 0.115308 0.006772 17.028476 0.000000e+00 0.102028 0.128588
education 0.018601 0.003250 5.723597 1.200935e-08 0.012228 0.024975
_const -0.207323 0.054111 -3.831436 1.313376e-04 -0.313443 -0.101203
======================================================================