DOCAPI-6887: Description of skewPop, skewSamp, kurtPop, kurtSamp functions. (#5544)

docs/en/query_language/agg_functions/reference.md

-
 # Function reference
 
 ## count() {#agg_function-count}
@@ -301,6 +300,98 @@ GROUP BY timeslot
 └─────────────────────┴──────────────────────────────────────────────┘
 ```
 
+## skewPop
+
+Computes the [skewness](https://en.wikipedia.org/wiki/Skewness) for sequence.
+
+```
+skewPop(expr)
+```
+
+**Parameters**
+
+`expr` — [Expression](../syntax.md#syntax-expressions) returning a number.
+
+**Returned value**
+
+The skewness of given distribution. Type — [Float64](../../data_types/float.md)
+
+**Example of Use**
+
+```sql
+SELECT skewPop(value) FROM series_with_value_column
+```
+
+## skewSamp
+
+Computes the [sample skewness](https://en.wikipedia.org/wiki/Skewness) for sequence.
+
+It represents an unbiased estimate of the skewness of a random variable, if passed values form it's sample.
+
+```
+skewSamp(expr)
+```
+
+**Parameters**
+
+`expr` — [Expression](../syntax.md#syntax-expressions) returning a number.
+
+**Returned value**
+
+The skewness of given distribution. Type — [Float64](../../data_types/float.md). If `n <= 1` (`n` is a size of the sample), then the function returns `nan`.
+
+**Example of Use**
+
+```sql
+SELECT skewSamp(value) FROM series_with_value_column
+```
+
+## kurtPop
+
+Computes the [kurtosis](https://en.wikipedia.org/wiki/Kurtosis) for sequence.
+
+```
+kurtPop(expr)
+```
+
+**Parameters**
+
+`expr` — [Expression](../syntax.md#syntax-expressions) returning a number.
+
+**Returned value**
+
+The kurtosis of given distribution. Type — [Float64](../../data_types/float.md)
+
+**Example of Use**
+
+```sql
+SELECT kurtPop(value) FROM series_with_value_column
+```
+
+## kurtSamp
+
+Computes the [sample kurtosis](https://en.wikipedia.org/wiki/Kurtosis) for sequence.
+
+It represents an unbiased estimate of the kurtosis of a random variable, if passed values form it's sample.
+
+```
+kurtSamp(expr)
+```
+
+**Parameters**
+
+`expr` — [Expression](../syntax.md#syntax-expressions) returning a number.
+
+**Returned value**
+
+The kurtosis of given distribution. Type — [Float64](../../data_types/float.md). If `n <= 1` (`n` is a size of the sample), then the function returns `nan`.
+
+**Example of Use**
+
+```sql
+SELECT kurtSamp(value) FROM series_with_value_column
+```
+
 ## timeSeriesGroupSum(uid, timestamp, value) {#agg_function-timeseriesgroupsum}
 timeSeriesGroupSum can aggregate different time series that sample timestamp not alignment.
 It will use linear interpolation between two sample timestamp and then sum time-series together.
@@ -514,7 +605,7 @@ All the quantile functions also have corresponding quantiles functions: `quantil
 
 Calculates the amount `Σ((x - x̅)^2) / (n - 1)`, where `n` is the sample size and `x̅`is the average value of `x`.
 
-It represents an unbiased estimate of the variance of a random variable, if the values passed to the function are a sample of this random amount.
+It represents an unbiased estimate of the variance of a random variable, if passed values form it's sample.
 
 Returns `Float64`. When `n <= 1`, returns `+∞`.
 
@@ -595,7 +686,7 @@ Parameters:
 
 Returned values:
 
-Parameters `(a, b)` of the resulting line `x = a*y + b`.
+Parameters `(a, b)` of the resulting line `y = a*x + b`.
 
 **Examples**
 
@@ -654,11 +745,11 @@ To predict we use function `evalMLMethod`, which takes a state as an argument as
         param2 Float64,
         target Float64
     ) ENGINE = Memory;
-    
+
     CREATE TABLE your_model ENGINE = Memory AS SELECT
     stochasticLinearRegressionState(0.1, 0.0, 5, 'SGD')(target, param1, param2)
     AS state FROM train_data;
-    
+
     ```
     Here we also need to insert data into `train_data` table. The number of parameters is not fixed, it depends only on number of arguments, passed into `linearRegressionState`. They all must be numeric values.
     Note that the column with target value(which we would like to learn to predict) is inserted as the first argument.
@@ -671,7 +762,7 @@ To predict we use function `evalMLMethod`, which takes a state as an argument as
     evalMLMethod(model, param1, param2) FROM test_data
     ```
     The query will return a column of predicted values. Note that first argument of `evalMLMethod` is `AggregateFunctionState` object, next are columns of features.
-    
+
     `test_data` is a table like `train_data` but may not contain target value.
 
 **Some notes**
@@ -681,12 +772,12 @@ To predict we use function `evalMLMethod`, which takes a state as an argument as
     SELECT state1 + state2 FROM your_models
     ```
     where `your_models` table contains both models. This query will return new `AggregateFunctionState` object.
-    
+
 2. User may fetch weights of the created model for its own purposes without saving the model if no `-State` combinator is used.
     ```sql
     SELECT stochasticLinearRegression(0.01)(target, param1, param2) FROM train_data
     ```
-    Such query will fit the model and return its weights - first are weights, which correspond to the parameters of the model, the last one is bias. So in the example above the query will return a column with 3 values. 
+    Such query will fit the model and return its weights - first are weights, which correspond to the parameters of the model, the last one is bias. So in the example above the query will return a column with 3 values.
 
 
 ## logisticRegression
@@ -696,8 +787,8 @@ This function implements stochastic logistic regression. It can be used for bina
 
 #### Parameters
 
-Parameters are exactly the same as in stochasticLinearRegression: 
-`learning rate`, `l2 regularization coefficient`, `mini-batch size`, `method for updating weights`. 
+Parameters are exactly the same as in stochasticLinearRegression:
+`learning rate`, `l2 regularization coefficient`, `mini-batch size`, `method for updating weights`.
 For more information see [parameters](#parameters).
 ```text
 stochasticLogisticRegression(1.0, 1.0, 10, 'SGD')
@@ -706,9 +797,9 @@ stochasticLogisticRegression(1.0, 1.0, 10, 'SGD')
 1. *Fitting*
 
     See *stochasticLinearRegression.Fitting*
-    
+
     Predicted labels have to be in {-1, 1}.
-    
+
 2. *Predicting*
 
     Using saved state we can predict probability of object having label *1*.