54c60195-f9ec-4932-97d6-47b451c8092d 5 4 5 false false 0 decimal

&[DATE] &[TIME] - &[FILENAME]

From Maple VRV help:

stats[fit, leastsquare] - Fit a curve to data using the least square method Calling Sequence: stats[fit, leastsquare[vars, eqn, parms]]( data) fit[leastsquare[vars, eqn, parms]]( data) Parameters: data - list of statistical lists vars - list of variables, corresponding, in order, to the lists in data eqn - The equation to fit (optional, default = a linear equation with the last variable in vars as dependent variable and with a constant term). parms - Set of parameters that will be replaced (optional, default = indets(eqn) minus op(vars)). Description: The function leastsquare of the subpackage stats[fit, ...] fits a curve to the given data using the method of least squares. The equation to fit must be linear in the unknown parameters. The equation itself need not be linear. For example, y=a*x^2+b*x+c with the parameters a, b, c is accepted. Note that some equations which have their parameters appearing nonlinearly can be transformed to linear ones. y=a*exp(b*x) can be transformed into log(y)=A+b*x, where A=log(a). Missing data and ranges cannot be handled. Weighted data are handled in the following fashion. The weight associated with the dependent variable is the weight given to the corresponding point. The weight specifications corresponding to the independent variables are ignored. The command with(stats[describe],leastsquare) allows the use of the abbreviated form of this command.

Basic example

dwBpAHQAaAAoAHMAdABhAHQAcwApADoADQAKAGEAbgBzACAAOgA9ACAAZgBpAHQAWwBsAGUAYQBzAHQAcwBxAHUAYQByAGUAWwBbAHgALAB5ACwAegBdAF0AXQANAAoAIAAgACgAWwBbADEALAAyACwAMwAsADUAXQAsAFsAMgAsADQALAA2ACwAOABdACwAWwAzACwANQAsADcALAAxADAAXQBdACkAOwA= #

ans z 2 y + 2 x * + 2 / =

Using Weight

dwBpAHQAaAAoAHMAdABhAHQAcwApADoADQAKAGEAbgBzACAAOgA9ACAAZgBpAHQAWwBsAGUAYQBzAHQAcwBxAHUAYQByAGUAWwBbAHgALAB5ACwAegBdAF0AXQANAAoAIAAgACgAWwBbADEALAAyACwAMwAsADUALAA1ACwANQBdACwAWwAyACwANAAsADYALAA4ACwAOAAsADgAXQAsAFsAMwAsADUALAA3ACwAMQAwACwAMQA1ACwAMQA1AF0AXQApADsA #

ans z 2313 x * + (* 7 y * - 6 / =

dwBpAHQAaAAoAHMAdABhAHQAcwApADoADQAKAGEAbgBzACAAOgA9ACAAZgBpAHQAWwBsAGUAYQBzAHQAcwBxAHUAYQByAGUAWwBbAHgALAB5ACwAegBdAF0AXQANAAoAIAAgACgAWwBbADEALAAyACwAMwAsADUALAA1AF0ALABbADIALAA0ACwANgAsADgALAA4AF0ALABbADMALAA1ACwANwAsADEAMAAsAA0ACgAgACAAIAAgAFcAZQBpAGcAaAB0ACgAMQA1ACwAMgApAF0AXQApADsA #

ans z 2313 x * + (* 7 y * - 6 / =

Fit a quadratic

dwBpAHQAaAAoAHMAdABhAHQAcwApADoADQAKAFgAdgBhAGwAdQBlAHMAOgA9AFsAMQAsADIALAAzACwANABdADsADQAKAFkAdgBhAGwAdQBlAHMAOgA9AFsAMAAsADYALAAxADQALAAyADQAXQA7AA0ACgBhAG4AcwAgADoAPQAgAGYAaQB0AFsAbABlAGEAcwB0AHMAcQB1AGEAcgBlAFsAWwB4ACwAeQBdACwAIAANAAoAIAAgAHkAPQBhACoAeABeADIAKwBiACoAeAArAGMALAAgAA0ACgAgACAAewBhACwAYgAsAGMAfQBdAF0AKABbAFgAdgBhAGwAdQBlAHMALAAgAFkAdgBhAGwAdQBlAHMAXQApADsA #

ans y 4 - x 3 x + (* + =

Calling maple function

IsMat(x) try to check if x is a matrix. x\032E\ IsMat x\032E\ num2str mat(findstr 1 - ≠ eval 11 line :

Converts a Matrix into a string with specific delimiters. M\032E\ D\032E\\005B\ D\032E\\005D\ D\032E\\002C\ Mat2Str M\032E\ IsMat s\0324\ D\032E\\005B\ : r\0324\ 1 M\032E\ rows range s\0324\ s\0324\ D\032E\\005B\ concat : c\0324\ 1 M\032E\ cols range s\0324\ s\0324\ M\032E\ r\0324\ c\0324\ el D\032E\\005B\ D\032E\\005D\ D\032E\\002C\ Mat2Str D\032E\\002C\ concat : for s\0324\ s\0324\ 1 s\0324\ strlen 1 - substr D\032E\\005D\ D\032E\\002C\ concat : 31 line for s\0324\ s\0324\ 1 s\0324\ strlen 1 - substr D\032E\\005D\ concat : 31 line M\032E\ num2str if 11 line :

M# Mat2Str M# [], Mat2Str 11 line :

X 123514 mat :

Y 246814 mat :

Z 3571014 mat :

Values from the first example

str with(stats):fit[leastsquare[[x,y,z]]](X Y Z stack Mat2Str) concat eval :

str with(stats):fit[leastsquare[[x,y,z]]]([[1,2,3,5],[2,4,6,8],[3,5,7,10]])

Unfortunatelly this don't work

str maple str

You must to copy and paste the string

with(stats):fit[leastsquare[[x,y,z]]]([[1,2,3,5],[2,4,6,8],[3,5,7,10]]) maple z 2 y + 2 x * + 2 / =

There are also another fit function: leastmediansquare for Fit a Curve to data using the least median of squares method

Alvaro