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let
    EWMA = Function.From(
        type function(alpha as number, values as list, index as number) as number,
        (params) =>
            let
                alpha = params{0},
                values = params{1},
                index = params{2},
                // Accumulate EWMA values up to the specified index
                resultList = List.Accumulate(

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Cpl = 
/*
=============EXAMPLE DATASET===================
CallID	Duration_Minutes	Agent	Date
C1001	4.2	Agent_A	2024-01-15
C1002	3.5	Agent_B	2024-01-15
C1003	2.8	Agent_C	2024-01-15
C1004	4.6	Agent_A	2024-01-15
C1005	3.9	Agent_B	2024-01-16
C1006	3.2	Agent_C	2024-01-16

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Cpu = 

/*
====================EXAMPLE DATASET===========================================
LoanID	Processing_Days	Application_Date
L1001	18	2024-01-05
L1002	22	2024-01-08
L1003	16	2024-01-10
L1004	25	2024-01-12
L1005	19	2024-01-15

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Cpk = 
/*
===============EXAMPLE DATASET====================
MeasurementID	Thickness_mm	Date
1	10.12	2024-01-15
2	10.08	2024-01-15
3	10.25	2024-01-15
4	9.95	2024-01-15
5	10.15	2024-01-16
6	10.22	2024-01-16

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3 Day Rolling Average 3 = 

VAR __MaxOffset = MAX('Calendar'[CurrDayOffset])-1
VAR __MinOffset = MAX('Calendar'[CurrDayOffset])-3
VAR __PrevMaxOffset = MAX('Calendar'[CurrDayOffset])-2
VAR __PrevMinOffset = MAX('Calendar'[CurrDayOffset])-4
VAR __STDev = CALCULATE(STDEV.P(TimeSeriesData[Value]), ALL(TimeSeriesData))
VAR __Mean = CALCULATE(AVERAGE(TimeSeriesData[Value]), ALL(TimeSeriesData))
VAR __T1 = SUMMARIZE(FILTER(ALL('Calendar'),'Calendar'[CurrDayOffset] >= __MinOffset && 'Calendar'[CurrDayOffset] <= __MaxOffset), "__Value", SUM(TimeSeriesData[Value]))
VAR __T2 = ADDCOLUMNS(__T1,"__Previous_Value", CALCULATE(SUM(TimeSeriesData[Value]), ALL('Calendar'),'Calendar'[CurrDayOffset] >= __PrevMinOffset && 'Calendar'[CurrDayOffset] <= __PrevMaxOffset))

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// Make Table 1

T1 = DATATABLE("EntityID",INTEGER,"Feature",STRING,{
        { 1,"A" },{ 1,"B" },{ 1,"C" },{ 2,"B" },{ 2,"C" },{ 2,"D" },{ 3,"A" },{ 3,"E" },{ 3,"F" },{ 4,"C" },{ 4,"D" },{ 4,"F" },{ 5,"A" },{ 5,"B" },{ 5,"F" }
    })

// Make Table 2
T2 = DATATABLE("EntityID",INTEGER,"Feature",STRING,{
        { 101,"B" },{ 101,"C" },{ 101,"E" },{ 102,"A" },{ 102,"D" },{ 102,"F" },{ 103,"C" },{ 103,"D" },{ 103,"G" },{ 104,"A" },{ 104,"B" },{ 104,"G" },{ 105,"E" },{ 105,"F" },{ 105,"G" }
    }) 

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DEFINE 
    -- Sample data table for demonstration purposes
    -- Replace with your actual table in production use
    TABLE SampleData = DATATABLE(
        "ProductID", INT64,
        "Sales", CURRENCY,
        {
            {1, 100},
            {2, 150},
            {3, 200},

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Median of Means = 

VAR __Partition = AddColumns(SampleData, "__Group", MOD(RANKX(ALL(SampleData),SampleData[ID],,ASC),5))

VAR __Means = SUMMARIZE(__Partition,[__Group], "__GroupMean", AVERAGE(SampleData[Value1]))

VAR __Result = MEDIANX(__Means,[__GroupMean])

RETURN __Result

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PERT ESTIMATION = 
VAR __Optimistic = 100
VAR __Likely = 70
VAR __Pessimistic = 60
VAR __Result = (__Optimistic+ (4*__Likely)+__Pessimistic)/6
RETURN __Result

cbaragao

RPEM = 
/*RPEM(Risk-Adjusted Point Estimate Method)
📌 Purpose:
Estimate a risk-adjusted cost(or value) using the Rosenblueth Point Estimate Method(PEM),accounting for correlation between two uncertain variables and a user-defined risk tolerance.

🛠️ How to Use:
-Set low,moderate,and high values for two variables(A and B).
-Set the correlation between them(-1 to+1).
-Set a risk tolerance factor(e.g.,1.0=1 standard deviation).
-Set __IsCostIncreasing=TRUE()if higher values are worse(e.g.,cost).