# PEL statistical functions

The following links provide detailed reference information about PerformancePoint Expression Language (PEL) functions that perform statistical calculations.

# Reference

• Avg (PEL)
Returns the average value of measures or the average value of an optional numeric expression, evaluated over a specified set.
• Correlation (PEL)
Returns the correlation coefficient of two series evaluated over a set.
• Covariance (PEL)
Returns the population covariance of two series evaluated over a set, by using the biased population formula.
• CovarianceN (PEL)
Returns the sample covariance of two series evaluated over a set, by using the unbiased population formula.
• LinRegIntercept (PEL)
Calculates the linear regression of a set and returns the value of the intercept in the regression line, y = ax + b.
• LinRegPoint (PEL)
Calculates the linear regression of a set and returns the value of y in the regression line, y = ax + b.
• LinRegR2 (PEL)
Calculates the linear regression of a set and returns the coefficient of determination, R2.
• LinRegSlope (PEL)
Calculates the linear regression of a set and returns the value of the slope in the regression line, y = ax + b.
• Max (PEL)
Returns the maximum value of a numeric expression that is evaluated over a set.
• Median (PEL)
Returns the median value of a numeric expression that is evaluated over a set.
• Min (PEL)
Returns the minimum value of a numeric expression that is evaluated over a set.
• StdDev (PEL)
Returns the sample standard deviation of a numeric expression evaluated over a set, by using the unbiased population formula.
• StdDevP (PEL)
Returns the population standard deviation of a numeric expression evaluated over a set, by using the biased population formula.
• Sum (PEL)
Returns the sum of a numeric expression evaluated over a set.
• Variance (PEL)
Returns the sample variance of a numeric expression evaluated over a set, by using the unbiased population formula.
• VarianceP (PEL)
Returns the population variance of a numeric expression evaluated over a set, by using the biased population formula.