AI & MACHINE LEARNING PROGRAM • LEVEL 06 — LINEAR REGRESSION
Fit a Simple Regression Line with Python
Learn fit a simple regression line with python with a short, executable Python example.
PROBLEM UNDERSTANDING
Input and expected output
No input required
2.0 0.0
COMPLETE PYTHON PROGRAM
Complete Python implementation
x = [1, 2, 3] y = [2, 4, 6] x_mean, y_mean = sum(x)/len(x), sum(y)/len(y) slope = sum((a-x_mean)*(b-y_mean) for a,b in zip(x,y)) / sum((a-x_mean)**2 for a in x) intercept = y_mean - slope*x_mean print(slope, intercept)
CURRENT STEP
Select Start to walk through the important lines.
SELECTED LINE
No line selected
EXPECTED OUTPUT FOR THE SAMPLE
2.0 0.0
PROGRAM EXPLANATION
Algorithm and explanation
- Initialize the sample values used to fit a simple regression line.
- Process the data step by step using Least squares and Slope.
- Display the result for fit a simple regression line and compare it with the documented sample output.
This example of fit a simple regression line processes the sample values in a controlled iteration. It demonstrates Least squares, Slope, and Intercept and prints a deterministic result that can be checked against the sample output.
EFFICIENCY
Time and space complexity
O(n)
O(1)
DEBUGGING CHECKLIST
Common mistakes
For fit a simple regression line, keep the data shape and value types consistent with Least squares.
Keep every dependent statement inside the correct indented Python block.
Check denominators, numeric ranges and rounding before comparing the calculated value.