Module: PROIEL::Statistics

Defined in:
lib/proiel/statistics.rb

Class Method Summary collapse

Class Method Details

.least_squares(x, y) ⇒ Array(Float, Float)

Computes the line of best fit using the least-squares method.

Examples:

x = [8, 2, 11, 6, 5, 4, 12, 9, 6, 1]
y = [3, 10, 3, 6, 8, 12, 1, 4, 9, 14]
a, b = PROIEL::Statistics.least_squares(x, y)
a # => 14.081081081081088
b # => -1.1064189189189197

Parameters:

  • x (Array<Number>) —

    x-values

  • y (Array<Number>) —

    y-values

Returns:

  • (Array(Float, Float)) —

    y-intercept and slope

Raises:

  • (ArgumentError) 


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# File 'lib/proiel/statistics.rb', line 22

def self.least_squares(x, y)
  raise ArgumentError unless x.is_a?(Array)
  raise ArgumentError unless y.is_a?(Array)
  raise ArgumentError, 'array lengths differ' unless x.size == y.size

  x_mean = x.reduce(&:+).to_f / x.size
  y_mean = y.reduce(&:+).to_f / y.size
  x_sqsum = x.reduce(0.0) { |sum, n| sum + n**2 }
  xy_sum = x.zip(y).reduce(0.0) { |sum, (m, n)| sum + m * n }

  sxy = xy_sum - x.length * x_mean * y_mean
  sx2 = x_sqsum - x.length * (x_mean**2)

  beta = sxy / sx2
  alfa = y_mean - beta * x_mean

  [alfa, beta]
end