Dundas BI lets you apply mathematical formulas to data by entering small script-like expressions in a toolbar, similar to a desktop spreadsheet program. This article lists the math functions that are available for use in formula expressions.
This walkthrough shows you how to use a Formula Visualization and display the result in a Data Label.
The Rank Value function returns the value found at the specified position, either starting from the highest or the lowest value.
The Clustering function uses the K-Means algorithm to split data points into clusters based on similarity of the measures provided.
The AVG function computes the mean (or simple average) of a set of input values.
The Bollinger Bands function was developed by John Bollinger. It computes a pair of data bands that envelops a simple moving average of the input value series.
The Cumulative Total function computes the cumulative total (or running sum) of a set of input values.
The Exponential Moving Average function computes the average of a set of input values over a specified number of time periods.
The Historical Volatility function calculates the volatility of a set of input data values, such as stock prices over a period of time.
The Median function computes the median (e.g. middle) value from a set of input values.
The Mode function finds the value (or values) that is repeated more often than any other in a value series.
The Moving Average function computes the average of a set of input values over a specified number of time periods. The smaller the number of time periods, the faster the moving average responds to changes in the input values.
The Moving Average Envelopes function computes a pair of data bands that envelops a moving average of the input data values.
The Percent of Total function returns the percentage of each value in the input series out of the total sum of the values in the input series.
The Sum function calculates the total sum for any number of input value series.
The Trend and Forecasting function applies a regression function to historical data in order to forecast future values based on the best fit.
The Weighted Moving Average function computes the average of a set of input values over a specified number of time periods.
The Exponential Smoothing functions apply an exponentially-decreasing weight to historical data in order to forecast future values based on emerging trends.
The Correlation Matrix calculates the strength of the relationships between possible pairings of the specified data series.
The Correlation function calculates the correlation between two input data series.