नोट
इस पृष्ठ तक पहुंच के लिए प्राधिकरण की आवश्यकता होती है। आप साइन इन करने या निर्देशिकाएँ बदलने का प्रयास कर सकते हैं।
इस पृष्ठ तक पहुंच के लिए प्राधिकरण की आवश्यकता होती है। आप निर्देशिकाएँ बदलने का प्रयास कर सकते हैं।
Applies to: 
Calculated column
Calculated table
Measure
Visual calculation
Uses the Least Squares method to calculate a straight line that best fits the given data, then returns a table describing the line. The equation for the line is of the form: y = Slope1*x1 + Slope2*x2 + ... + Intercept.
Syntax
LINEST ( <columnY>, <columnX>[, …][, <const>] )
Parameters
| Term | Definition |
|---|---|
columnY |
The column of known y-values. Must have scalar type. |
columnX |
The columns of known x-values. Must have scalar type. At least one must be provided. |
const |
(Optional) A constant TRUE/FALSE value specifying whether to force the constant Intercept to equal 0.If TRUE or omitted, the Intercept value is calculated normally; If FALSE, the Intercept value is set to zero. |
Return value
A single-row table describing the line, plus additional statistics. These are the available columns:
- Slope1, Slope2, ..., SlopeN: the coefficients corresponding to each x-value;
- Intercept: intercept value;
- StandardErrorSlope1, StandardErrorSlope2, ..., StandardErrorSlopeN: the standard error values for the coefficients Slope1, Slope2, ..., SlopeN;
- StandardErrorIntercept: the standard error value for the constant Intercept;
- CoefficientOfDetermination: the coefficient of determination (r²). Compares estimated and actual y-values, and ranges in value from 0 to 1: the higher the value, the higher the correlation in the sample;
- StandardError: the standard error for the y estimate;
- FStatistic: the F statistic, or the F-observed value. Use the F statistic to determine whether the observed relationship between the dependent and independent variables occurs by chance;
- DegreesOfFreedom: the degrees of freedom. Use this value to help you find F-critical values in a statistical table, and determine a confidence level for the model;
- RegressionSumOfSquares: the regression sum of squares;
- ResidualSumOfSquares: the residual sum of squares.
Remarks
columnY and the columnX’s must all belong to the same table.
Example 1
The following DAX query:
EVALUATE LINEST(
'FactInternetSales'[SalesAmount],
'FactInternetSales'[TotalProductCost]
)
Returns a single-row table with ten columns:
| Slope1 | Intercept | StandardErrorSlope1 | StandardErrorIntercept | CoefficientOfDetermination |
|---|---|---|---|---|
| 1.67703250456677 | 6.34550460373026 | 0.000448675725548806 | 0.279131821917317 | 0.995695557281456 |
| StandardError | FStatistic | DegreesOfFreedom | RegressionSumOfSquares | ResidualSumOfSquares |
|---|---|---|---|---|
| 60.9171030357485 | 13970688.6139993 | 60396 | 51843736761.658 | 224123120.339218 |
- Slope1 and Intercept: the coefficients of the calculated linear model;
- StandardErrorSlope1 and StandardErrorIntercept: the standard error values for the coefficients above;
- CoefficientOfDetermination, StandardError, FStatistic, DegreesOfFreedom, RegressionSumOfSquares and ResidualSumOfSquares: regression statistics about the model.
For a given internet sale, this model predicts the sale amount by the following formula:
SalesAmount = Slope1 * TotalProductCost + Intercept
Example 2
The following DAX query:
EVALUATE LINEST(
'DimCustomer'[TotalSalesAmount],
'DimCustomer'[YearlyIncome],
'DimCustomer'[TotalChildren],
'DimCustomer'[BirthDate]
)
Returns a single-row table with fourteen columns:
- Slope1
- Slope2
- Slope3
- Intercept
- StandardErrorSlope1
- StandardErrorSlope2
- StandardErrorSlope3
- StandardErrorIntercept
- CoefficientOfDetermination
- StandardError
- FStatistic
- DegreesOfFreedom
- RegressionSumOfSquares
- ResidualSumOfSquares
For a given customer, this model predicts total sales by the following formula (the birth date is automatically converted to a number):
TotalSalesAmount = Slope1 * YearlyIncome + Slope2 * TotalChildren + Slope3 * BirthDate + Intercept