Trend Slope OLS

Problem

Fit a linear trend x[t] = intercept + slope * t by ordinary least squares with t = 0, ..., n-1 and return the slope: sum((t - mean_t) * (x[t] - mean_x)) / sum((t - mean_t)^2).

Return 0.0 if the series has fewer than two points.

Hint

Precompute mean_t = (n - 1) / 2 to avoid building an index list.

Test cases

case 1

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

expected: 1

case 2

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

expected: -1

case 3

in: [[5,5,5]]

expected: 0

case 4

in: [[1,3,5]]

expected: 2

Starter codePython

def trend_slope(series):
    # Your code here
    pass

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