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?

Question

Ranjana Admin · Accepted Answer

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()

Krowd Darden · Answer

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.

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?

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