A user has the following code and got a linear regression fit with m=-0.1071 and b=0.0347.How to get deviation from m value?
We can get the deviation in the form of standard error from the following code
import numpy as np
import pandas as pd
import statsmodels.api as sm
import math
U = [12.5, 10.0, 7.6, 6.0, 4.4, 3.1, 2.5, 1.5, 1.0, 0.5, 0.3]
U_0 = 12.5
y = []
for number in U:
y.append(math.log(number/U_0, math.e))
y = np.array(y)
t = np.array([0.0, 3.0, 5.0, 7.2, 10.0, 13.0, 15.0, 20.0, 25.0, 30.0, 35.0])
t = sm.add_constant(t, prepend=False)
model = sm.OLS(y,t)
result = model.fit()
result.summary()
Answer (1)
This code effectively calculates the standard error by performing linear regression using the statsmodels library. It transforms the dependent variable using a natural logarithm and then applies Ordinary Least Squares (OLS) regression to model the relationship between t and y.Krowd Darden
The result.summary() function provides key statistical details, including:
Coefficients: The estimated slope and intercept.
Standard Error: The deviation of the coefficient estimates.
R-squared: How well the model fits the data.
p-values: Significance of the regression coefficients.
Great approach for analyzing exponential decay or growth trends! Adding print(result.summary()) ensures that the output is visible when running the script.