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
Library SFSDK.LIB
DLL 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
Class SFSDK
Scope Public
Lifetime Static
Package NumXLAPI.DLL
Examples

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