Statistical aggregation functions

Avg() finds the average value of the aggregated data in the expression over a number of records as defined by a group by clause.

Correl() returns the aggregated correlation coefficient for a series of coordinates represented by paired numbers in x-expression and y-expression iterated over a number of records as defined by a group by clause.

Fractile() finds the value that corresponds to the inclusive fractile (quantile) of the aggregated data in the expression over a number of records as defined by a group by clause.

FractileExc() finds the value that corresponds to the exclusive fractile (quantile) of the aggregated data in the expression over a number of records as defined by a group by clause.

Kurtosis() returns the kurtosis of the data in the expression over a number of records as defined by a group by clause.

LINEST_B() returns the aggregated b value (y-intercept) of a linear regression defined by the equation y=mx+b for a series of coordinates represented by paired numbers in x-expression and y-expression iterated over a number of records as defined by a group by clause.

LINEST_DF() returns the aggregated degrees of freedom of a linear regression defined by the equation y=mx+b for a series of coordinates represented by paired numbers in x-expression and y-expression iterated over a number of records as defined by a group by clause.

This script function returns the aggregated F statistic (r2/(1-r2)) of a linear regression defined by the equation y=mx+b for a series of coordinates represented by paired numbers in x-expression and y-expression iterated over a number of records as defined by a group by clause.

LINEST_M() returns the aggregated m value (slope) of a linear regression defined by the equation y=mx+b for a series of coordinates represented by paired numbers in x-expression and y-expression iterated over a number of records as defined by a group by clause.

LINEST_R2() returns the aggregated r2 value (coefficient of determination) of a linear regression defined by the equation y=mx+b for a series of coordinates represented by paired numbers in x-expression and y-expression iterated over a number of records as defined by a group by clause.

LINEST_SEB() returns the aggregated standard error of the b value of a linear regression defined by the equation y=mx+b for a series of coordinates represented by paired numbers in x-expression and y-expression iterated over a number of records as defined by a group by clause.

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 in x-expression and y-expression iterated over a number of records as defined by a group by clause.

LINEST_SEY() returns the aggregated standard error of the y estimate of a linear regression defined by the equation y=mx+b for a series of coordinates represented by paired numbers in x-expression and y-expression iterated over a number of records as defined by a group by clause.

LINEST_SSREG() returns the aggregated regression sum of squares of a linear regression defined by the equation y=mx+b for a series of coordinates represented by paired numbers in x-expression and y-expression iterated over a number of records as defined by a group by clause.

LINEST_SSRESID() returns the aggregated residual sum of squares of a linear regression defined by the equation y=mx+b for a series of coordinates represented by paired numbers in x-expression and y-expression iterated over a number of records as defined by a group by clause.

Median() returns the aggregated median of the values in the expression over a number of records as defined by a group by clause.

Skew() returns the skewness of expression over a number of records as defined by a group by clause.

Stdev() returns the standard deviation of the values given by the expression over a number of records as defined by a group by clause.

Sterr() returns the aggregated standard error (stdev/sqrt(n)) for a series of values represented by the expression iterated over a number of records as defined by a group by clause.

STEYX() returns the aggregated standard error of the predicted y-value for each x-value in the regression for a series of coordinates represented by paired numbers in x-expression and y-expression iterated over a number of records as defined by a group by clause.

Avg() returns the aggregated average of the expression or field iterated over the chart dimensions.

avg({[SetExpression] [DISTINCT] [TOTAL [<fld{, fld}>]]} expr)

Correl() returns the aggregated correlation coefficient for two data sets. The correlation function is a measure of the relationship between the data sets and is aggregated for (x,y) value pairs iterated over the chart dimensions.

correl({[SetExpression] [TOTAL [<fld {, fld}>]]} value1, value2 )

Fractile() finds the value that corresponds to the inclusive fractile (quantile) of the aggregated data in the range given by the expression iterated over the chart dimensions.

fractile({[SetExpression] [TOTAL [<fld {, fld}>]]} expr, fraction)

FractileExc() finds the value that corresponds to the exclusive fractile (quantile) of the aggregated data in the range given by the expression iterated over the chart dimensions.

fractileexc({[SetExpression] [TOTAL [<fld {, fld}>]]} expr, fraction)

Kurtosis() finds the kurtosis of the range of data aggregated in the expression or field iterated over the chart dimensions.

kurtosis({[SetExpression] [DISTINCT] [TOTAL [<fld{, fld}>]]} expr)

LINEST_B() returns the aggregated b value (y-intercept) of a linear regression defined by the equation y=mx+b for a series of coordinates represented by paired numbers in the expressions given by the expressions x_value and y_value, iterated over the chart dimensions.

linest_r2({[SetExpression] [TOTAL [<fld{ ,fld}>]] }y_value, x_value[, y0_const[, x0_const]])

LINEST_DF() returns the aggregated degrees of freedom of a linear regression defined by the equation y=mx+b for a series of coordinates represented by paired numbers in the expressions given by x_value and y_value, iterated over the chart dimensions.

linest_df({[SetExpression] [TOTAL [<fld{, fld}>]]} y_value, x_value [, y0_const [, x0_const]])

LINEST_F() returns the aggregated F statistic (r2/(1-r2)) of a linear regression defined by the equation y=mx+b for a series of coordinates represented by paired numbers in the expressions given by x_value and the y_value, iterated over the chart dimensions.

linest_f({[SetExpression] [TOTAL[<fld{, fld}>]]} y_value, x_value [, y0_const [, x0_const]])

LINEST_M() returns the aggregated m value (slope) 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.

linest_m({[SetExpression] [TOTAL[<fld{, fld}>]]} y_value, x_value [, y0_const [, x0_const]])

LINEST_R2() returns the aggregated r2 value (coefficient of determination) 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.

linest_r2({[SetExpression] [TOTAL [<fld{ ,fld}>]] }y_value, x_value[, y0_const[, x0_const]])

LINEST_SEB() returns the aggregated standard error of the b 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.

linest_seb({[SetExpression] [TOTAL [<fld{ ,fld}>]] }y_value, x_value[, y0_const[, x0_const]])

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.

linest_sem([{set_expression}][ distinct ] [total [<fld {,fld}>] ] y-expression, x-expression [, y0 [, x0 ]] )

LINEST_SEY() returns the aggregated standard error of the y estimate 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.

linest_sey({[SetExpression] [TOTAL [<fld{ ,fld}>]] }y_value, x_value[, y0_const[, x0_const]])

LINEST_SSREG() returns the aggregated regression sum of squares 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.

linest_ssreg({[SetExpression] [TOTAL [<fld{ ,fld}>]] }y_value, x_value[, y0_const[, x0_const]])

LINEST_SSRESID() returns the aggregated residual sum of squares of a linear regression defined by the equation y=mx+b for a series of coordinates represented by paired numbers in the expressions given by x_value and y_value, iterated over the chart dimensions.

linest_ssresid({[SetExpression] [TOTAL [<fld{ ,fld}>]] }y_value, x_value[, y0_const[, x0_const]])

Median() returns the median value of the range of values aggregated in the expression iterated over the chart dimensions.

median({[SetExpression] [TOTAL [<fld{, fld}>]]} expr)

MutualInfo calculates the mutual information (MI) between two fields or between aggregated values in Aggr().

MutualInfo{[SetExpression] [DISTINCT] [TOTAL target, driver [, datatype [, breakdownbyvalue [, samplesize ]]])

Skew() returns the aggregated skewness of the expression or field iterated over the chart dimensions.

skew{[SetExpression] [DISTINCT] [TOTAL [<fld{ ,fld}>]]} expr)

Stdev() finds the standard deviation of the range of data aggregated in the expression or field iterated over the chart dimensions.

stdev({[SetExpression] [DISTINCT] [TOTAL [<fld{, fld}>]]} expr)

Sterr() finds the value of the standard error of the mean, (stdev/sqrt(n)), for the series of values aggregated in the expression iterated over the chart dimensions.

sterr({[SetExpression] [DISTINCT] [TOTAL[<fld{, fld}>]]} expr)

STEYX() returns the aggregated standard error when predicting y-values for each x-value in a linear regression given by a series of coordinates represented by paired numbers in the expressions given by y_value and x_value.

steyx{[SetExpression] [TOTAL [<fld{, fld}>]]} y_value, x_value)