7 Replies Latest reply: Sep 25, 2013 2:42 PM by Clever Anjos RSS

    Looking for integrated statistical functions

    Philippe Grenier

      Good morning,

       

      I am currenlty trying to get a firm grasp on QlikView's capabilities to formulate statistical expressions, and have found out a few interesing functions under the Statistical Aggregation Functions in Charts and Statistical Distribution Functions

      sections in the inline help. Up to now, I am basically quite confident that the following needs are answered (my question is at the end):

       

      Kurtosis

      kurtosis([{set_expression}][ distinct ] [ total [<fld { , fld } >] ] expression)

      Returns the aggregated kurtosis of expression or field iterated over the chart dimension(s).

      This function has the same limitations for nested aggregation as the avg([{set_expression}] [ distinct ] [ total [<fld { , fld } >]] expression) function. The kurtosis function supports Set Analysis and the total qualifier in the same way as the avg([{set_expression}] [ distinct ] [ total [<fld { , fld } >]] expression) function.

      Examples:

      kurtosis(Sales)

      kurtosis(X'Y/3)

      kurtosis(distinct Price)

      kurtosis(total Sales)

      kurtosis({1} total Sales)

      Median

      median ([{set_expression}] [ distinct ] [ total [<fld {,fld}>] ] expression )

      Returns the aggregated median of expression iterated over the chart dimension(s).

      This function has the same limitations for nested aggregation as the avg([{set_expression}] [ distinct ] [ total [<fld { , fld } >]] expression) function. The median function supports Set Analysis and the total qualifier in the same way as the avg([{set_expression}] [ distinct ] [ total [<fld { , fld } >]] expression) function.

      Examples:

      median( X )    

      median( X*Y/3 )    

      median( total X )    

      median( total <Group> Price )

      Standard Deviation

      stdev([{set_expression}][ distinct ] [ total [<fld { , fld } >] ] expression)

      Returns the aggregated standard deviation of expression or field iterated over the chart dimension(s).

      This function has the same limitations for nested aggregation as the avg([{set_expression}] [ distinct ] [ total [<fld { , fld } >]] expression) function. The stdev function supports Set Analysis and the total qualifier in the same way as the avg([{set_expression}] [ distinct ] [ total [<fld { , fld } >]] expression) function.

      Examples:

      stdev(Sales)

      stdev(X'Y/3)

      stdev(distinct Price)

      stdev(total Sales)

      stdev({1} total Sales)

      Mean

      avg([{set_expression}] [ distinct ] [ total [<fld { , fld } >]] expression)

      Returns the aggregated average of expression or field iterated over the chart dimension(s). [...]

      If the word distinct occurs before the function arguments, duplicates resulting from the evaluation of the function arguments will be disregarded.

      If the word total occurs before the function arguments the calculation will be made over all possible values given the current selections but disregarding the chart dimension variables.

      The total qualifier may be followed by a list of one or more field names within angle brackets. These field names should be a subset of the chart dimension variables. In this case the calculation will be made disregarding all chart dimension variables except those listed, i.e. one value will be returned for each combination of field values in the listed dimension fields. Also fields which are not currently a dimension in a chart may be included in the list. This may be useful in the case of group dimensions, where the dimension fields are not fixed. Listing all of the variables in the group causes the function to work when the cycle or drill-down level changes.

      Examples:

      avg(Sales)

      avg(X'Y/3)

      avg(distinct Price)

      avg(total Sales)

      avg({1} total Sales)

      Distribution

      normdist (value, mean, standard_dev)

      returns the cumulative normal distribution for the specified mean and standard deviation. Value is the value at which you want to evaluate the distribution. Mean is a value stating the arithmetic mean for the distribution. Standard_dev is a positive value stating the standard deviation of the distribution. All arguments must be numeric, else null will be returned. If mean = 0 and standard_dev = 1, the function returns the standard normal distribution. This function is related to the norminv (prob, mean, standard_dev) function in the following way:

      If prob = normdist(value, m, sd), then norminv(prob, m, sd) = value.

      Example:

      normdist( 0.5, 0, 1 ) returns 0.6914625

       

      At this point though, I haven't found any information or functions related to Symmetry analysis, or for central tendency and dispersion measures, Mode and Variance.

       

      Does anyone have any hints or pointers on this subject matter?

       

      Thanks in advance for your time, regards,

       

      Philippe