Standard Error (of the Mean) is a measure of how well sample mean approximates the population mean.

The standard error is the standard deviation of the error in the sample mean relative to the true mean. It is usually estimated by the sample estimate of the population Standard Deviation divided by the square root of the sample size.



Input Data*

Defines the time series data fed into the function. This can be a sensor ID or another function.


Number of data intervals considered in the function

*Input data is optional in most cases. If Info360 detects that the first input is time series data, it will be applied to the function. Otherwise, the current active sensor's data will be used, which is often the case in Reference Charts.

Example Usage as an Expression:

StdErr(12) - Outputs the Standard Error of the previous 12 data points for the current data stream.

StdErr(Sensor('Tank_A'),24) - Outputs the Standard Error of the previous 24 data points for the Tank_A sensor.

Examples Reference Chart:

The following example shows measured discharge pressure from a station along with calculations of the StdErr and StdDev. Both statistics follow the same trend and characteristics, since StdErr is equal to the StdDev divided by the square root of the sample size (12 in this case).

The StdErr spikes upwards when their are spikes or jumps in the measured data that deviate from the mean of the sample.

For information on setting up custom equations and syntax, please refer to Analytical Functions.