# NDK_RMS

 int __stdcall NDK_RMS ( double * X, size_t N, WORD reserved, double * retVal )

Returns the sample root mean square (RMS).

Returns
status code of the operation
Return values
 NDK_SUCCESS Operation successful NDK_FAILED Operation unsuccessful. See Macros for full list.
Parameters
 [in] X is the input data sample (a one/two dimensional array). [in] N is the number of observations in X. [in] reserved This parameter is reserved and must be 1. [out] retVal is the calculated value of this function.
Remarks
1. The input time series data may include missing values (NaN), but they will not be included in the calculations.
2. The root mean square (RMS) is defined as follows for a set of $$n$$ values $${x_1,x_2,...,x_n}$$:
$\mathrm{RMS}=\sqrt{\frac{x_1^2+x_2^2+\cdots +x_N^2}{N}} =\sqrt{\frac{\sum_{i=1}^N {x_i^2}}{N}}$
Where:
• $$x_i$$ is the value of the i-th non-missing observation.
• $$N$$ is the number of non-missing observations in the input sample data.
4. The root mean square (RMS) is a statistical measure of the magnitude of a varying quantity.
5. The root mean square (RMS) has an interesting relationship to the mean ( $$\bar{x}$$) and the population standard deviation ( $$\sigma$$), such that:
$\mathrm{RMS}^2=\bar{x}^2+\sigma^2$
Requirements
Header SFSDK.H SFSDK.LIB SFSDK.DLL
Examples



 Namespace: NumXLAPI Class: SFSDK Scope: Public Lifetime: Static
 int NDK_RMS ( double[] pData, UIntPtr nSize, short argMenthod, ref double retVal )

Returns the sample root mean square (RMS).

Return Value

a value from NDK_RETCODE enumeration for the status of the call.

 NDK_SUCCESS operation successful Error Error Code
Parameters
 [in] pData is the input data sample (a one/two dimensional array). [in] nSize is the number of observations in pData. [in] argMenthod This parameter is reserved and must be 1. [out] retVal is the calculated value of this function.
Remarks
1. The input time series data may include missing values (NaN), but they will not be included in the calculations.
2. The root mean square (RMS) is defined as follows for a set of $$n$$ values $${x_1,x_2,...,x_n}$$:
$\mathrm{RMS}=\sqrt{\frac{x_1^2+x_2^2+\cdots +x_N^2}{N}} =\sqrt{\frac{\sum_{i=1}^N {x_i^2}}{N}}$
Where:
• $$x_i$$ is the value of the i-th non-missing observation.
• $$N$$ is the number of non-missing observations in the input sample data.
4. The root mean square (RMS) is a statistical measure of the magnitude of a varying quantity.
5. The root mean square (RMS) has an interesting relationship to the mean ( $$\bar{x}$$) and the population standard deviation ( $$\sigma$$), such that:
$\mathrm{RMS}^2=\bar{x}^2+\sigma^2$
Exceptions
Exception Type Condition
None N/A
Requirements
Namespace NumXLAPI SFSDK Public Static NumXLAPI.DLL
Examples

References
Hull, John C.; Options, Futures and Other DerivativesFinancial Times/ Prentice Hall (2011), ISBN 978-0132777421