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<a name="LinearRegression"></a>
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<p>
Next: <a href="wsolve.html#wsolve" accesskey="n" rel="next">wsolve</a>, Previous: <a href="polyconf.html#polyconf" accesskey="p" rel="prev">polyconf</a>, Up: <a href="Residual-optimization.html#Residual-optimization" accesskey="u" rel="up">Residual optimization</a> &nbsp; [<a href="Function-index.html#Function-index" title="Index" rel="index">Index</a>]</p>
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<hr>
<a name="Function-LinearRegression"></a>
<h3 class="section">2.13 Function LinearRegression</h3>
<a name="index-LinearRegression-3"></a>

<a name="XREFLinearRegression"></a><dl>
<dt><a name="index-LinearRegression"></a>Function File: <em></em> <strong>LinearRegression</strong> <em>(<var>F</var>, <var>y</var>)</em></dt>
<dt><a name="index-LinearRegression-1"></a>Function File: <em></em> <strong>LinearRegression</strong> <em>(<var>F</var>, <var>y</var>, <var>w</var>)</em></dt>
<dt><a name="index-LinearRegression-2"></a>Function File: <em>[<var>p</var>, <var>e_var</var>, <var>r</var>, <var>p_var</var>, <var>fit_var</var>] =</em> <strong>LinearRegression</strong> <em>(&hellip;)</em></dt>
<dd>

<p>general linear regression
</p>
<p>determine the parameters p_j  (j=1,2,...,m) such that the function
f(x) = sum_(j=1,...,m) p_j*f_j(x) is the best fit to the given values
y_i by f(x_i) for i=1,...,n, i.e. minimize
sum_(i=1,...,n)(y_i-sum_(j=1,...,m) p_j*f_j(x_i))^2 with respect to p_j
</p>
<p>parameters:  
</p><ul>
<li> <var>F</var> is an n*m matrix with the values of the basis functions at
the support points. In column j give the values of f_j at the points
x_i  (i=1,2,...,n)
</li><li> <var>y</var> is a column vector of length n with the given values
</li><li> <var>w</var> is a column vector of length n with the weights of
the data points. 1/w_i is expected to be proportional to the
estimated uncertainty in the y values. Then the weighted expression
sum_(i=1,...,n)(w_i^2*(y_i-f(x_i))^2) is minimized.
</li></ul>

<p>return values:
</p><ul>
<li> <var>p</var> is the vector of length m with the estimated values
of the parameters
</li><li> <var>e_var</var> is the vector of estimated variances of the
provided y values. If weights are provided, then the product
e_var_i * w^2_i is assumed to be constant.
</li><li> <var>r</var> is the weighted norm of the residual
</li><li> <var>p_var</var> is the vector of estimated variances of the parameters p_j
</li><li> <var>fit_var</var> is the vector of the estimated variances of the
fitted function values f(x_i)
</li></ul>

<p>To estimate the variance of the difference between future y values
and fitted y values use the sum of <var>e_var</var> and <var>fit_var</var>
</p>
<p>Caution:  
do NOT request <var>fit_var</var> for large data sets, as a n by n matrix is
generated
</p>
</dd></dl>


<p>See also <a href="https://www.gnu.org/software/octave/doc/interpreter/XREFols.html#XREFols">(octave)ols</a>, <a href="https://www.gnu.org/software/octave/doc/interpreter/XREFgls.html#XREFgls">(octave)gls</a>,
<a href="https://www.gnu.org/software/octave/doc/interpreter/XREFregress.html#XREFregress">(octave)regress</a>, <a href="leasqr.html#XREFleasqr">leasqr</a>,
<a href="nonlin_005fcurvefit.html#XREFnonlin_005fcurvefit">nonlin_curvefit</a>,
<a href="https://www.gnu.org/software/octave/doc/interpreter/XREFpolyfit.html#XREFpolyfit">(octave)polyfit</a>, <a href="wpolyfit.html#XREFwpolyfit">wpolyfit</a>,
<a href="pronyfit.html#XREFpronyfit">pronyfit</a>.
</p>

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Next: <a href="wsolve.html#wsolve" accesskey="n" rel="next">wsolve</a>, Previous: <a href="polyconf.html#polyconf" accesskey="p" rel="prev">polyconf</a>, Up: <a href="Residual-optimization.html#Residual-optimization" accesskey="u" rel="up">Residual optimization</a> &nbsp; [<a href="Function-index.html#Function-index" title="Index" rel="index">Index</a>]</p>
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