covariance_dev: following @ivan-pi s comments

Author: jvdp1

src/stdlib_experimental_stats.md

@@ -18,7 +18,7 @@ Per default, the covariance is defined as:
  cov(array) = 1/(n-1) sum_i (array(i) - mean(array) * (array(i) - mean(array)))
 ```
 
-where n is the number of elements.
+where `n` is the number of elements.
 
 The scaling can be changed with the logical argument `corrected`. If `corrected` is `.false.`, then the sum is scaled with `n`, otherwise with `n-1`.
 
@@ -31,9 +31,9 @@ The scaling can be changed with the logical argument `corrected`. If `corrected`
 
 `array`: Shall be a rank-1 or a rank-2 array of type `integer`, `real`, or `complex`.
 
-`dim`: Shall be a scalar of type `integer` with a value in the range from 1 to n, where n is the rank of `array`.
+`dim`: Shall be a scalar of type `integer` with a value in the range from 1 to `n`, where `n` is the rank of `array`.
 
-`mask` (optional): Shall be of type `logical` and either by a scalar or an array of the same shape as `array`.
+`mask` (optional): Shall be of type `logical` and either a scalar or an array of the same shape as `array`.
 
 `corrected` (optional): Shall be a scalar of type `logical`. If `corrected` is `.true.` (default value), the sum is scaled with `n-1`. If `corrected` is `.false.`, then the sum is scaled with `n`.
 
@@ -78,16 +78,16 @@ Returns the mean of all the elements of `array`, or of the elements of `array` a
 
 `array`: Shall be an array of type `integer`, `real`, or `complex`.
 
-`dim`: Shall be a scalar of type `integer` with a value in the range from 1 to n, where n is the rank of `array`.
+`dim`: Shall be a scalar of type `integer` with a value in the range from 1 to `n`, where `n` is the rank of `array`.
 
-`mask` (optional): Shall be of type `logical` and either by a scalar or an array of the same shape as `array`.
+`mask` (optional): Shall be of type `logical` and either a scalar or an array of the same shape as `array`.
 
 ### Return value
 
 If `array` is of type `real` or `complex`, the result is of the same type as `array`.
 If `array` is of type `integer`, the result is of type `real(dp)`.
 
-If `dim` is absent, a scalar with the mean of all elements in `array` is returned. Otherwise, an array of rank n-1, where n equals the rank of `array`, and a shape similar to that of `array` with dimension `dim` dropped is returned.
+If `dim` is absent, a scalar with the mean of all elements in `array` is returned. Otherwise, an array of rank `n-1`, where `n` equals the rank of `array`, and a shape similar to that of `array` with dimension `dim` dropped is returned.
 
 If `mask` is specified, the result is the mean of all elements of `array` corresponding to `true` elements of `mask`. If every element of `mask` is `false`, the result is IEEE `NaN`.
 
@@ -121,7 +121,7 @@ The _k_-th order central moment is defined as :
  moment(array) = 1/n sum_i (array(i) - mean(array))^k
 ```
 
-where n is the number of elements.
+where `n` is the number of elements.
 
 The _k_-th order moment about `center` is defined as :
 
@@ -141,18 +141,18 @@ The _k_-th order moment about `center` is defined as :
 
 `order`: Shall be an scalar of type `integer`.
 
-`dim`: Shall be a scalar of type `integer` with a value in the range from 1 to n, where n is the rank of `array`.
+`dim`: Shall be a scalar of type `integer` with a value in the range from 1 to `n`, where `n` is the rank of `array`.
 
 `center` (optional): Shall be a scalar of the same type of `result` if `dim` is not provided. If `dim` is provided, `center` shall be a scalar or an array (with a shape similar to that of `array` with dimension `dim` dropped) of the same type of `result`.
 
-`mask` (optional): Shall be of type `logical` and either by a scalar or an array of the same shape as `array`.
+`mask` (optional): Shall be of type `logical` and either a scalar or an array of the same shape as `array`.
 
 ### Return value
 
 If `array` is of type `real` or `complex`, the result is of the same type as `array`.
 If `array` is of type `integer`, the result is of type `real(dp)`.
 
-If `dim` is absent, a scalar with the _k_-th (central) moment of all elements in `array` is returned. Otherwise, an array of rank n-1, where n equals the rank of `array`, and a shape similar to that of `array` with dimension `dim` dropped is returned.
+If `dim` is absent, a scalar with the _k_-th (central) moment of all elements in `array` is returned. Otherwise, an array of rank `n-1`, where `n` equals the rank of `array`, and a shape similar to that of `array` with dimension `dim` dropped is returned.
 
 If `mask` is specified, the result is the _k_-th  (central) moment of all elements of `array` corresponding to `true` elements of `mask`. If every element of `mask` is `false`, the result is IEEE `NaN`.
 
@@ -185,7 +185,7 @@ Per default, the variance is defined as the best unbiased estimator and is compu
  var(array) = 1/(n-1) sum_i (array(i) - mean(array))^2
 ```
 
-where n is the number of elements.
+where `n` is the number of elements.
 
 The use of the term `n-1` for scaling is called Bessel 's correction. The scaling can be changed with the logical argument `corrected`. If `corrected` is `.false.`, then the sum is scaled with `n`, otherwise with `n-1`.
 
@@ -200,9 +200,9 @@ The use of the term `n-1` for scaling is called Bessel 's correction. The scalin
 
 `array`: Shall be an array of type `integer`, `real`, or `complex`.
 
-`dim`: Shall be a scalar of type `integer` with a value in the range from 1 to n, where n is the rank of `array`.
+`dim`: Shall be a scalar of type `integer` with a value in the range from 1 to `n`, where `n` is the rank of `array`.
 
-`mask` (optional): Shall be of type `logical` and either by a scalar or an array of the same shape as `array`.
+`mask` (optional): Shall be of type `logical` and either a scalar or an array of the same shape as `array`.
 
 `corrected` (optional): Shall be a scalar of type `logical`. If `corrected` is `.true.` (default value), the sum is scaled with `n-1`. If `corrected` is `.false.`, then the sum is scaled with `n`.
 
@@ -211,7 +211,7 @@ The use of the term `n-1` for scaling is called Bessel 's correction. The scalin
 If `array` is of type `real` or `complex`, the result is of type `real` corresponding to the type of `array`.
 If `array` is of type `integer`, the result is of type `real(dp)`.
 
-If `dim` is absent, a scalar with the variance of all elements in `array` is returned. Otherwise, an array of rank n-1, where n equals the rank of `array`, and a shape similar to that of `array` with dimension `dim` dropped is returned.
+If `dim` is absent, a scalar with the variance of all elements in `array` is returned. Otherwise, an array of rank `n-1`, where `n` equals the rank of `array`, and a shape similar to that of `array` with dimension `dim` dropped is returned.
 
 If `mask` is specified, the result is the variance of all elements of `array` corresponding to `true` elements of `mask`. If every element of `mask` is `false`, the result is IEEE `NaN`.