Python Coding challenge - Day 407| What is the output of the following Python Code?

 

Step-by-Step Code Explanation

Imports necessary libraries:

import statsmodels.api as sm  

import numpy as np  

statsmodels.api is used for statistical modeling.

numpy is used for numerical computations and array handling.

x = np.array([1, 2, 3, 4, 5])  

y = np.array([2, 4, 6, 8, 10])  

Defines input variables:

x = [1, 2, 3, 4, 5] (independent variable)

y = [2, 4, 6, 8, 10] (dependent variable)

The relationship is perfectly linear with y = 2x.

model = sm.OLS(y, sm.add_constant(x)).fit()  

Creates and fits an Ordinary Least Squares (OLS) regression model:

sm.add_constant(x) adds a bias (intercept) term to x.

sm.OLS(y, X).fit() computes the best-fit line for y = 2x.

print(model.params[1])

Extracts and prints the slope (coefficient) of x

The slope represents how much y changes for each unit change in x.

Ideally, the output should be 2.0, but the actual printed value is:

1.9999999999999998

Why is the Output 1.9999999999999998 Instead of 2.0?

This happens due to floating-point precision errors in Python. Here’s why:

Floating-Point Representation

Computers store decimal numbers in binary floating-point format, which sometimes cannot exactly represent simple decimals.

Tiny rounding errors occur during calculations, leading to results like 1.9999999999999998 instead of 2.0.

Final Answer (Exact Value with Floating-Point Precision)

1.999