Pearson Correlation in Haskell
Each function on its own

Let us have a look at each of your functions first in solitude. What does summation do?


summation :: (Integral a, Fractional b) =&gt; a -&gt; a -&gt; (a -&gt; b) -&gt; b
summation i n e = if (i &lt; n)
                  then (e i + summation (i+1) n e)
                  else (e i)



Well, for all numbers from x in i to n, sum the e x. We can write this a lot more clear with a list comprehension:

summation :: (Enum n, Num a) =&gt; n -&gt; n -&gt; a
summation i n e = sum [e x | x &lt;- [i..n]]


Note that this only holds if i is always lesser than n in the inital call.

Next, what does mean do?


mean :: (Real a, Fractional b) =&gt; [a] -&gt; b
mean x = (1 / (genericLength x)) *
         (summation 1 (length x) (\i -&gt; realToFrac (x !! (i-1))))



Uh, that looks complicated. First, let us rewrite it as proper fraction:

mean x = num / denom 
  where 
    num   = summation 1 (length x) (\i -&gt; realToFrac (x !! (i-1)))
    denom = genericLength x


Much easier to read. So, what do you actually do in num? You calculate the sum of all numbers in x. However, this can be done with sum already. We end up with

mean :: Fractional a =&gt; [a] -&gt; a
mean xs = sum xs / genericLength xs


Note that lists are usually named with a suffix s. One x, multiple xs.

We use the same approach on covariance. First, we rewrite it in simpler terms:

covariance x y = num / denom
  where
    num        = summation 1 (length x) elemwise
    denom      = genericLength x
    elemwise i = (realToFrac (x !! (i-1)) - mean x) * (realToFrac (y !! (i-1)) - mean x))


And now we immediately spot an error in elemwise. You wrote y !! (i-1) - mean x, but you meant y !! (i - 1) - mean y.

However, let us have a look at the mathematical definition again:

$$
cov(X,Y) = E\left[(X-E[X])*(Y-E[Y])\right]
$$

Allright. Let's write this exactly down as it stands there:

covariance x y = mean productXY
  where
   productXY = pairwiseProduct xSubMean ySubMean
   xSubMean  = subtractMean x
   ySubMean  = subtractMean y


This is obviously not ready yet. What are pairwiseProduct and subtractMean?

subtractMean :: Fractional a =&gt; [a] -&gt; [a]
subtractMean xs = [x - mx | x &lt;- xs]
  where
    mx = mean xs

pairwiseProduct :: Num a =&gt; [a] -&gt; [a] -&gt; [a]
pairwiseProduct xs ys = zipWith (*) xs ys


Now we're done. Note how the covariance almost looks like pseudo-code? It's clearly easy to read by a human. That's the most important point you should take from this review: make your code easy to read for yourself.

stddev xs is just sqrt (covariance xs xs), so you should probably use that:

-- sqrt needs Floating
stddev :: Floating a =&gt; [a] -&gt; a
stddev xs = sqrt (covariance xs xs)


Everything at once

After we've finished our rewrite, it turns out you don't need summation at all. So what do we end up with?

mean :: Fractional a =&gt; [a] -&gt; a
mean xs = sum xs / genericLength xs

covariance :: Fractional a =&gt; [a] -&gt; [a] -&gt; a
covariance xs ys = mean productXY
  where
   productXY = zipWith (*) [x - mx | x &lt;- xs] [y - my | y &lt;- ys]       
   mx        = mean xs
   my        = mean ys

stddev :: Floating a =&gt; [a] -&gt; a
stddev xs = sqrt (covariance xs xs)

pearson :: (Floating a) =&gt; [a] -&gt; [a] -&gt; a
pearson x y = covariance x y / (stddev x * stddev y)


Note that covariance got a lot easier to read due to bindings.

Please keep in mind that this implementation has some performance related issues. Especially the mean implementation is a poster child for a function that leaks memory, but for small lists you should be fine.

Working with lists

When you work with lists, avoid element-wise access. It's really slow. Instead, you want to transform the whole map into a single value (a fold, e.g. length or sum), or into another map (above with list comprehensions, e.g. [x - mx | x &lt;- xs]).