Linear Regression

Problem

Fit a simple linear regression y = a + b*x using least squares.

b = sum((x_i - mean_x) * (y_i - mean_y)) / sum((x_i - mean_x)^2) a = mean_y - b * mean_x

Return [a, b] = [intercept, slope]. If the denominator is 0, return [mean_y, 0.0].

Hint

b is the slope (cov(x,y) / var(x)); a is the intercept that makes the line pass through (mean_x, mean_y).

case 1

in: [[1,2,3,4,5],[3,5,7,9,11]]

expected: [1,2]

case 2

in: [[0,1,2,3],[1,3,5,7]]

expected: [1,2]

case 3

in: [[1,2,3],[2,4,6]]

expected: [0,2]

case 4

in: [[5,5,5],[3,4,5]]

expected: [4,0]

Starter codePython

def linear_regression_fit(X, y):
    # Returns [intercept, slope]
    # Your code here
    pass

4 test cases · run your code against them in the editor