Programmer Guide/Command Reference/EVAL/rpolyreg: Difference between revisions
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:;<var>x</var>: x data vector | :;<var>x</var>: x data vector | ||
:;<var>y</var>: y data vector: <code>''y''[i] = f(''x''[i], ''x''[i]^2, .., ''x''[i]^''m'')</code> | :;<var>y</var>: y data vector: <code>''y''[i] = f(''x''[i], ''x''[i]^2, .., ''x''[i]^''m'')</code> | ||
:;<var>m</var>: the regression (polynom) order; m>1 | |||
;Result 1: A vector ''r'' with the two linear regression coefficients. | ;Result 1: A vector ''r'' with the two linear regression coefficients. | ||
:<code>''y''<sub>REG</sub>[i] = ''r''[0] + ''r''[1] * ''x''[i] | :<code>''y''<sub>REG</sub>[i] = ''r''[0] + ''r''[1] * ''x''[i] | ||
---- | ---- | ||
Revision as of 13:46, 11 April 2011
Linear, multivariant and polynominal regression.
- Usage 1
rpolyreg(xvector, yvector):- x
- x data vector
- y
- y data vector:
y[i] = f(x[i])
- Result 1
- A vector r with the two linear regression coefficients.
yREG[i] = r[0] + r[1] * x[i]
- Usage 1
rpolyreg(xvector, yvector, m):
- x
- x data vector
- y
- y data vector:
y[i] = f(x[i], x[i]^2, .., x[i]^m)
- m
- the regression (polynom) order; m>1
- Result 1
- A vector r with the two linear regression coefficients.
yREG[i] = r[0] + r[1] * x[i]
