LINEST_SEM() returns the aggregated standard error of the m value of a linear regression defined by the equation y=mx+b for a series of coordinates represented by paired numbers given by the expressions x_value and y_value, iterated over the chart dimensions.
Syntax:
LINEST_SEM([{SetExpression}] [DISTINCT] [TOTAL [<fld{, fld}>]] y_value, x_value[, y0_const[, x0_const]])
Return data type: numeric
Arguments:
- y_value: The expression or field containing the range of y-values to be measured.
- x_value: The expression or field containing the range of x-values to be measured.
-
y0,x0: An optional
value y0 may be stated forcing
the regression line to pass through the y-axis at a given point. By stating
both y0 and x0 it is possible to force the regression line to pass through a single fixed coordinate. Information noteUnless both y0 and x0 are stated, the function requires at least two valid data-pairs to calculate. If y0 and x0 are stated, a single data pair will do.
- SetExpression: By default, the aggregation function will aggregate over the set of possible records defined by the selection. An alternative set of records can be defined by a set analysis expression.
- DISTINCT: If the word DISTINCT occurs before the function arguments, duplicates resulting from the evaluation of the function arguments are disregarded.
- TOTAL: If the word TOTAL occurs before the function arguments, the calculation is made over all possible values given the current selections, and not just those that pertain to the current dimensional value, that is, it disregards the chart dimensions. The TOTAL qualifier may be followed by a list of one or more field names within angle brackets <fld>. These field names should be a subset of the chart dimension variables.
Limitations:
The parameter of the aggregation function must not contain other aggregation functions, unless these inner aggregations contain the TOTAL qualifier. For more advanced nested aggregations, use the advanced function Aggr, in combination with a specified dimension.
Text values, NULL values and missing values in any or both pieces of a data-pair result in the entire data-pair being disregarded.
