Verify distribution uniformity/Chi-squared test: Difference between revisions

=={{header|Julia}}==

<lang julia># v0.6

using Distributions

function eqdist(data::Vector{T}, α::Float64=0.05)::Bool where T <: Real

if ! (0 ≤ α ≤ 1); error("α must be in [0, 1]") end

exp = mean(data)

chisqval = sum((x - exp) ^ 2 for x in data) / exp

pval = ccdf(Chisq(2), chisqval)

return pval > α

end

data1 = [199809, 200665, 199607, 200270, 199649]

data2 = [522573, 244456, 139979, 71531, 21461]

for data in (data1, data2)

println("Data:\n$data")

println("Hypothesis test: the original population is ", (eqdist(data) ? "" : "not "), "uniform.\n")

end</lang>

{{out}}

<pre>Data:

[199809, 200665, 199607, 200270, 199649]

Hypothesis test: the original population is uniform.

Data:

[522573, 244456, 139979, 71531, 21461]

Hypothesis test: the original population is not uniform.

</pre>