Class: Statistics::StatisticalTest::TTest

Inherits:
Object
  • Object
show all
Defined in:
lib/statistics/statistical_test/t_test.rb

Class Method Summary collapse

Class Method Details

.paired_test(alpha, tails, left_group, right_group) ⇒ Object 



45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
 
# File 'lib/statistics/statistical_test/t_test.rb', line 45

def self.paired_test(alpha, tails, left_group, right_group)
  # Handy snippet grabbed from https://stackoverflow.com/questions/2682411/ruby-sum-corresponding-members-of-two-or-more-arrays
  differences = [left_group, right_group].transpose.map { |value| value.reduce(:-) }

  degrees_of_freedom = differences.size - 1
  down = differences.standard_deviation/Math.sqrt(differences.size)

  t_score = (differences.mean - 0)/down.to_f

  probability = Distribution::TStudent.new(degrees_of_freedom).cumulative_function(t_score)

  p_value = 1 - probability
  p_value *= 2 if tails == :two_tail

  { probability: probability,
    p_value: p_value,
    alpha: alpha,
    null: alpha < p_value,
    alternative: p_value <= alpha,
    confidence_level: 1 - alpha }
end

.perform(alpha, tails, *args) ⇒ Object

Perform a T-Test for one or two samples. For the tails param, we need a symbol: :one_tail or :two_tail



6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
 
# File 'lib/statistics/statistical_test/t_test.rb', line 6

def self.perform(alpha, tails, *args)
  return if args.size < 2

  degrees_of_freedom = 0

  t_score = if args[0].is_a? Numeric
              data_mean = args[1].mean
              data_std = args[1].standard_deviation
              comparison_mean = args[0]
              degrees_of_freedom = args[1].size

              (data_mean - comparison_mean)/(data_std / Math.sqrt(args[1].size).to_f).to_f
            else
              sample_left_mean = args[0].mean
              sample_left_variance = args[0].variance
              sample_right_variance = args[1].variance
              sample_right_mean = args[1].mean
              degrees_of_freedom = args.flatten.size - 2

              left_root = sample_left_variance/args[0].size.to_f
              right_root = sample_right_variance/args[1].size.to_f

              standard_error = Math.sqrt(left_root + right_root)

              (sample_left_mean - sample_right_mean)/standard_error.to_f
            end

  probability = Distribution::TStudent.new(degrees_of_freedom).cumulative_function(t_score)
  p_value = 1 - probability
  p_value *= 2 if tails == :two_tail

  { probability: probability,
    p_value: p_value,
    alpha: alpha,
    null: alpha < p_value,
    alternative: p_value <= alpha,
    confidence_level: 1 - alpha }
end