Python Coding challenge - Day 424| What is the output of the following Python Code?

 

Code Explanation:

1. Importing Libraries

import statsmodels.api as sm  

import numpy as np  

numpy is used for numerical operations and creating arrays.

statsmodels.api is a Python package used for statistical modeling — especially regression.

2. Defining Data

x = np.array([1, 2, 3, 4, 5])  

y = np.array([3, 6, 9, 12, 15])  

x is the independent variable.

y is the dependent variable.

From a quick look: y = 3 * x — it’s a perfect linear relationship.

 3. Adding Intercept to X

X = sm.add_constant(x)

This adds a column of ones to x so the model can estimate an intercept term (bias).

Now X becomes a 2D array:

[[1. 1.]

 [1. 2.]

 [1. 3.]

 [1. 4.]

 [1. 5.]]

First column = constant (intercept)

Second column = original x values

4. Fitting the OLS Model

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

This runs Ordinary Least Squares (OLS) regression using:

y as the dependent variable

X (with constant) as the independent variable(s)

.fit() estimates the parameters (intercept and slope)

5. Accessing the Slope

print(model.params[1])

model.params returns the regression coefficients:

[intercept, slope]

model.params[1] extracts the slope of the line.

Since the data is perfectly linear (y = 3x), the slope should be exactly 3.0.

Final Output

>>> 3.0

This confirms the slope is 3, matching the relationship between x and y.