In statistics, to smooth a data set is to create an approximating function that attempts to capture important patterns in the data, while leaving out noise or other fine-scale structures/rapid phenomena. In smoothing, the data points of a signal are modified so individual points (presumably because of noise) are reduced, and points that are lower than the adjacent points are increased leading to a smoother signal. Smoothing may be used in two important ways that can aid in data analysis (1) by being able to extract more information from the data as long as the assumption of smoothing is reasonable and (2) by being able to provide analyses that are both flexible and robust.[1] Many different algorithms are used in smoothing.
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NDK_DESMTH
C/C++
.Net
int __stdcall NDK_DESMTH
(
double *
pData,
size_t
nSize,
BOOL
bAscending,
...
NDK_LESMTH
C/C++
.Net
int __stdcall NDK_LESMTH
(
double *
pData,
size_t
nSize,
BOOL
bAscending,
...
NDK_SESMTH
C/C++
.Net
int __stdcall NDK_SESMTH
(
double *
pData,
size_t
nSize,
BOOL
bAscending,
...
NDK_TESMTH
C/C++
.Net
int __stdcall NDK_TESMTH
(
double *
pData,
size_t
nSize,
BOOL
bAscending,
...
NDK_TREND
C/C++
.Net
int __stdcall NDK_TREND
(
double *
pData,
size_t
nSize,
BOOL
bAscending,
...
NDK_WMA
C/C++
.Net
int __stdcall NDK_WMA
(
double *
pData,
size_t
nSize,
BOOL
bAscending,
...