Python Coding challenge - Day 796| What is the output of the following Python Code?

 Code Explanation:

import statsmodels.api as sm

Purpose: Imports the Statsmodels library under the alias sm.

Why: Statsmodels is used for running statistical tests and regression models (like OLS — Ordinary Least Squares).

X = sm.add_constant([1,2,3,4])

Purpose: Adds a constant (intercept) column to your predictor data.

Result:

[[1., 1.],

 [1., 2.],

 [1., 3.],

 [1., 4.]]

The first column (all 1s) represents the intercept.

The second column is your predictor variable [1,2,3,4].

Why: Statsmodels does not automatically include an intercept in OLS models, so you must add it manually with add_constant.

y = [2,4,6,8]

Purpose: Defines your dependent variable (target/output).

Meaning: The relationship here is perfectly linear:

y=2x

So the true intercept should be 0 and the slope should be 2.

model = sm.OLS(y, X).fit()

Purpose:

Creates an Ordinary Least Squares regression model (sm.OLS(y, X)).

Then calls .fit() to estimate the coefficients (parameters) using least squares.

What happens internally:

Result: Returns a RegressionResultsWrapper object containing all regression results and statistics.

print(model.params.tolist())

Purpose: Prints the estimated parameters (intercept and slope) as a Python list.

Output :

[0.0005000000000001118, 1.9990000000000003]

Final Output:

[0.0, 1.999]

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