Last Update: February 21, 2022
Linear Regression: Residual Standard Error in Python can be estimated using statsmodels package ols function, mse_resid property found within statsmodels.formula.api module and numpy package sqrt function for evaluating linear regression goodness of fit. Main parameters within ols function are formula with “y ~ x1 + … + xp” model description string and data with data frame object including model variables. Main parameter within sqrt function is x with value for square root calculation.
As example, we can estimate residual standard error from multiple linear regression of house price explained by its lot size and number of bedrooms using data included within AER R package HousePrices object [1].
First, we import packages numpy for square root calculation and statsmodels for data downloading, model fitting [2].
In [1]:
import numpy as np
import statsmodels.api as sm
import statsmodels.formula.api as smf
Second, we create houseprices data object using get_rdataset function and display first five rows and three columns of data using print function and head data frame method to view its structure.
In [2]:
houseprices = sm.datasets.get_rdataset(dataname="HousePrices", package="AER", cache=True).data
print(houseprices.iloc[:, 0:3].head())
Out [2]:
price lotsize bedrooms
0 42000.0 5850 3
1 38500.0 4000 2
2 49500.0 3060 3
3 60500.0 6650 3
4 61000.0 6360 2
Third, we fit model with ols function using variables within houseprices data object and store outcome within mlr object. Within ols function, parameter formula = “price ~ lotsize + bedrooms” fits model where house price is explained by its lot size and number of bedrooms.
In [3]:
mlr = smf.ols(formula="price ~ lotsize + bedrooms", data=houseprices).fit()
Fourth, we can print mlr model estimated residual standard error using sqrt function and its mse_resid property.
In [4]:
print(np.sqrt(mlr.mse_resid))
Out [4]:
21229.04501315886
Fifth, we can also print mlr model estimated residual standard error using sqrt function and its resid, df_resid properties.
In [5]:
print(np.sqrt(sum(mlr.resid ** 2) / mlr.df_resid))
Out [5]:
21229.045013158862
Courses
My online courses are hosted at Teachable website.
For more details on this concept, you can view my Linear Regression in Python Course.
References
[1] Data Description: Sales prices of houses sold in the city of Windsor, Canada, during July, August and September, 1987.
Original Source: Anglin, P., and Gencay, R. (1996). Semiparametric Estimation of a Hedonic Price Function. Journal of Applied Econometrics, 11, 633–648.
[2] numpy Python package: Travis E. Oliphant, et al. (2020). Array programming with NumPy. Nature, 585, 357–362.
statsmodels Python package: Seabold, Skipper, and Josef Perktold. (2010). “statsmodels: Econometric and statistical modeling with python.” Proceedings of the 9th Python in Science Conference.