Python Coding challenge - Day 792| What is the output of the following Python Code?

 

Code Explanation:

Import Libraries

import statsmodels.api as sm

import numpy as np

statsmodels.api → For statistical modeling (OLS regression, t-tests, etc.).

numpy → Provides numerical operations, arrays, etc.

Both are required to prepare data and fit the regression.

Create Feature (Independent Variable) with Intercept

X = sm.add_constant([1, 2, 3, 4, 5])

[1, 2, 3, 4, 5] → independent variable x.

sm.add_constant() → Adds a column of ones to account for the intercept in the regression.

After this line, X looks like:

[[1. 1.]

 [1. 2.]

 [1. 3.]

 [1. 4.]

 [1. 5.]]

First column = intercept

Second column = actual x values

Define Dependent Variable (y)

y = [2, 4, 5, 4, 5]

These are the target values we want to predict using x.

Notice: The data is not perfectly linear — the y-values fluctuate a little. This is why the slope is not exactly 1 or 0.8.

Fit the OLS Regression Model

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

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

.fit() → Finds the best-fit line that minimizes the sum of squared errors between predicted and actual y-values.

The model will estimate:

y=intercept+(slope×x)

Access and Round the Slope Coefficient

print(round(model.params[1], 2))

model.params → Array of coefficients: [intercept, slope]

params[1] → Slope of x

round(..., 2) → Rounds slope to 2 decimal places

Output 

0.6

500 Days Python Coding Challenges with Explanation