Aggregations for Measures

The aggregation that is assigned to a measure determines how its values are summarized in a visualization or report object.
For example, in a bar chart of Sales by Quarter, each bar represents the aggregated values of the Sales measure for a specific quarter. If the aggregation for Sales is Sum, then the bars represent the sum (total) of sales for each quarter. If the aggregation for Sales is Average, then the bars represent the average sales for each quarter.
Note: Some aggregation types can override the data format that is used to display values in a visualization or report object. For example, if a measure has the Currency format with zero decimal places of precision, and you apply the Variance aggregation, then the values are displayed using the Comma format with two decimal places of precision instead.
You can specify the following aggregations for your measures:
Sum
calculates the sum (total) of the values of a measure.
Average
calculates the average (mean) value of a measure.
Standard Deviation
calculates the standard deviation of a measure.
Standard Error
calculates the standard error of the mean of a measure.
Variance
calculates the variance of a measure.
Count
calculates the total number of nonmissing values of a measure.
Number Missing
calculates the number of missing values in a measure.
Minimum
calculates the smallest value of a measure.
First Quartile
calculates the first quartile of a measure.
Median
calculates the median value of a measure.
Third Quartile
calculates the third quartile of a measure.
Maximum
calculates the largest value of a measure.
Skewness
calculates the skewness of a measure. Skewness indicates the distribution of values. A positive value indicates that the distribution is heavier for values greater than the mean. A negative value indicates that the distribution is heavier for values less than the mean.
Kurtosis
calculates the kurtosis of a measure. The kurtosis value indicates how peaked the distribution is. A larger value indicates a more sharply peaked distribution. A smaller value indicates a flatter distribution.
Coefficient of Variation
calculates the coefficient of variation of a measure. The coefficient of variation is the ratio of the standard deviation to the mean.
Uncorrected Sum of Squares
calculates the uncorrected sum of squares of a measure. The uncorrected sum of squares is the sum of the squared values.
Corrected Sum of Squares
calculates the corrected sum of squares of a measure. The corrected sum of squares is the sum of the squared deviations from the mean.
T-statistic (for Average = 0)
calculates the Student’s t statistic for a measure, assuming a mean value of zero.
P-value (for T-statistic)
calculates the probability of observing the t statistic value or a more extreme value. A small value indicates that the mean is likely not equal to zero.