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 <div class="subTitle">org.apache.commons.math3.stat.regression</div>
 <h2 title="Class SimpleRegression" class="title">Class SimpleRegression</h2>
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 <hr>
 <br>
 <pre>public class <span class="strong">SimpleRegression</span>
 extends <a href="http://docs.oracle.com/javase/6/docs/api/java/lang/Object.html?is-external=true" title="class or interface in java.lang">Object</a>
 implements <a href="http://docs.oracle.com/javase/6/docs/api/java/io/Serializable.html?is-external=true" title="class or interface in java.io">Serializable</a>, <a href="../../../../../../org/apache/commons/math3/stat/regression/UpdatingMultipleLinearRegression.html" title="interface in org.apache.commons.math3.stat.regression">UpdatingMultipleLinearRegression</a></pre>
 <div class="block">Estimates an ordinary least squares regression model
  with one independent variable.
  <p>
  <code> y = intercept + slope * x  </code></p>
  <p>
  Standard errors for <code>intercept</code> and <code>slope</code> are
  available as well as ANOVA, r-square and Pearson's r statistics.</p>
  <p>
  Observations (x,y pairs) can be added to the model one at a time or they
  can be provided in a 2-dimensional array.  The observations are not stored
  in memory, so there is no limit to the number of observations that can be
  added to the model.</p>
  <p>
  <strong>Usage Notes</strong>: <ul>
  <li> When there are fewer than two observations in the model, or when
  there is no variation in the x values (i.e. all x values are the same)
  all statistics return <code>NaN</code>. At least two observations with
  different x coordinates are required to estimate a bivariate regression
  model.
  </li>
  <li> Getters for the statistics always compute values based on the current
  set of observations -- i.e., you can get statistics, then add more data
  and get updated statistics without using a new instance.  There is no
  "compute" method that updates all statistics.  Each of the getters performs
  the necessary computations to return the requested statistic.
  </li>
  <li> The intercept term may be suppressed by passing <code>false</code> to
  the <a href="../../../../../../org/apache/commons/math3/stat/regression/SimpleRegression.html#SimpleRegression(boolean)"><code>SimpleRegression(boolean)</code></a> constructor.  When the
  <code>hasIntercept</code> property is false, the model is estimated without a
  constant term and <a href="../../../../../../org/apache/commons/math3/stat/regression/SimpleRegression.html#getIntercept()"><code>getIntercept()</code></a> returns <code>0</code>.</li>
  </ul></p></div>
 <dl><dt><span class="strong">Version:</span></dt>
   <dd>$Id: SimpleRegression.java 1519851 2013-09-03 21:16:35Z tn $</dd>
 <dt><span class="strong">See Also:</span></dt><dd><a href="../../../../../../serialized-form.html#org.apache.commons.math3.stat.regression.SimpleRegression">Serialized Form</a></dd></dl>
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 <caption><span>Constructors</span><span class="tabEnd">&nbsp;</span></caption>
 <tr>
 <th class="colOne" scope="col">Constructor and Description</th>
 </tr>
 <tr class="altColor">
 <td class="colOne"><code><strong><a href="../../../../../../org/apache/commons/math3/stat/regression/SimpleRegression.html#SimpleRegression()">SimpleRegression</a></strong>()</code>
 <div class="block">Create an empty SimpleRegression instance</div>
 </td>
 </tr>
 <tr class="rowColor">
 <td class="colOne"><code><strong><a href="../../../../../../org/apache/commons/math3/stat/regression/SimpleRegression.html#SimpleRegression(boolean)">SimpleRegression</a></strong>(boolean&nbsp;includeIntercept)</code>
 <div class="block">Create a SimpleRegression instance, specifying whether or not to estimate
  an intercept.</div>
 </td>
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 </table>
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 <caption><span>Methods</span><span class="tabEnd">&nbsp;</span></caption>
 <tr>
 <th class="colFirst" scope="col">Modifier and Type</th>
 <th class="colLast" scope="col">Method and Description</th>
 </tr>
 <tr class="altColor">
 <td class="colFirst"><code>void</code></td>
 <td class="colLast"><code><strong><a href="../../../../../../org/apache/commons/math3/stat/regression/SimpleRegression.html#addData(double[][])">addData</a></strong>(double[][]&nbsp;data)</code>
 <div class="block">Adds the observations represented by the elements in
  <code>data</code>.</div>
 </td>
 </tr>
 <tr class="rowColor">
 <td class="colFirst"><code>void</code></td>
 <td class="colLast"><code><strong><a href="../../../../../../org/apache/commons/math3/stat/regression/SimpleRegression.html#addData(double, double)">addData</a></strong>(double&nbsp;x,
        double&nbsp;y)</code>
 <div class="block">Adds the observation (x,y) to the regression data set.</div>
 </td>
 </tr>
 <tr class="altColor">
 <td class="colFirst"><code>void</code></td>
 <td class="colLast"><code><strong><a href="../../../../../../org/apache/commons/math3/stat/regression/SimpleRegression.html#addObservation(double[], double)">addObservation</a></strong>(double[]&nbsp;x,
               double&nbsp;y)</code>
 <div class="block">Adds one observation to the regression model.</div>
 </td>
 </tr>
 <tr class="rowColor">
 <td class="colFirst"><code>void</code></td>
 <td class="colLast"><code><strong><a href="../../../../../../org/apache/commons/math3/stat/regression/SimpleRegression.html#addObservations(double[][], double[])">addObservations</a></strong>(double[][]&nbsp;x,
                double[]&nbsp;y)</code>
 <div class="block">Adds a series of observations to the regression model.</div>
 </td>
 </tr>
 <tr class="altColor">
 <td class="colFirst"><code>void</code></td>
 <td class="colLast"><code><strong><a href="../../../../../../org/apache/commons/math3/stat/regression/SimpleRegression.html#append(org.apache.commons.math3.stat.regression.SimpleRegression)">append</a></strong>(<a href="../../../../../../org/apache/commons/math3/stat/regression/SimpleRegression.html" title="class in org.apache.commons.math3.stat.regression">SimpleRegression</a>&nbsp;reg)</code>
 <div class="block">Appends data from another regression calculation to this one.</div>
 </td>
 </tr>
 <tr class="rowColor">
 <td class="colFirst"><code>void</code></td>
 <td class="colLast"><code><strong><a href="../../../../../../org/apache/commons/math3/stat/regression/SimpleRegression.html#clear()">clear</a></strong>()</code>
 <div class="block">Clears all data from the model.</div>
 </td>
 </tr>
 <tr class="altColor">
 <td class="colFirst"><code>double</code></td>
 <td class="colLast"><code><strong><a href="../../../../../../org/apache/commons/math3/stat/regression/SimpleRegression.html#getIntercept()">getIntercept</a></strong>()</code>
 <div class="block">Returns the intercept of the estimated regression line, if
  <a href="../../../../../../org/apache/commons/math3/stat/regression/SimpleRegression.html#hasIntercept()"><code>hasIntercept()</code></a> is true; otherwise 0.</div>
 </td>
 </tr>
 <tr class="rowColor">
 <td class="colFirst"><code>double</code></td>
 <td class="colLast"><code><strong><a href="../../../../../../org/apache/commons/math3/stat/regression/SimpleRegression.html#getInterceptStdErr()">getInterceptStdErr</a></strong>()</code>
 <div class="block">Returns the <a href="http://www.xycoon.com/standarderrorb0.htm">
  standard error of the intercept estimate</a>,
  usually denoted s(b0).</div>
 </td>
 </tr>
 <tr class="altColor">
 <td class="colFirst"><code>double</code></td>
 <td class="colLast"><code><strong><a href="../../../../../../org/apache/commons/math3/stat/regression/SimpleRegression.html#getMeanSquareError()">getMeanSquareError</a></strong>()</code>
 <div class="block">Returns the sum of squared errors divided by the degrees of freedom,
  usually abbreviated MSE.</div>
 </td>
 </tr>
 <tr class="rowColor">
 <td class="colFirst"><code>long</code></td>
 <td class="colLast"><code><strong><a href="../../../../../../org/apache/commons/math3/stat/regression/SimpleRegression.html#getN()">getN</a></strong>()</code>
 <div class="block">Returns the number of observations that have been added to the model.</div>
 </td>
 </tr>
 <tr class="altColor">
 <td class="colFirst"><code>double</code></td>
 <td class="colLast"><code><strong><a href="../../../../../../org/apache/commons/math3/stat/regression/SimpleRegression.html#getR()">getR</a></strong>()</code>
 <div class="block">Returns <a href="http://mathworld.wolfram.com/CorrelationCoefficient.html">
  Pearson's product moment correlation coefficient</a>,
  usually denoted r.</div>
 </td>
 </tr>
 <tr class="rowColor">
 <td class="colFirst"><code>double</code></td>
 <td class="colLast"><code><strong><a href="../../../../../../org/apache/commons/math3/stat/regression/SimpleRegression.html#getRegressionSumSquares()">getRegressionSumSquares</a></strong>()</code>
 <div class="block">Returns the sum of squared deviations of the predicted y values about
  their mean (which equals the mean of y).</div>
 </td>
 </tr>
 <tr class="altColor">
 <td class="colFirst"><code>double</code></td>
 <td class="colLast"><code><strong><a href="../../../../../../org/apache/commons/math3/stat/regression/SimpleRegression.html#getRSquare()">getRSquare</a></strong>()</code>
 <div class="block">Returns the <a href="http://www.xycoon.com/coefficient1.htm">
  coefficient of determination</a>,
  usually denoted r-square.</div>
 </td>
 </tr>
 <tr class="rowColor">
 <td class="colFirst"><code>double</code></td>
 <td class="colLast"><code><strong><a href="../../../../../../org/apache/commons/math3/stat/regression/SimpleRegression.html#getSignificance()">getSignificance</a></strong>()</code>
 <div class="block">Returns the significance level of the slope (equiv) correlation.</div>
 </td>
 </tr>
 <tr class="altColor">
 <td class="colFirst"><code>double</code></td>
 <td class="colLast"><code><strong><a href="../../../../../../org/apache/commons/math3/stat/regression/SimpleRegression.html#getSlope()">getSlope</a></strong>()</code>
 <div class="block">Returns the slope of the estimated regression line.</div>
 </td>
 </tr>
 <tr class="rowColor">
 <td class="colFirst"><code>double</code></td>
 <td class="colLast"><code><strong><a href="../../../../../../org/apache/commons/math3/stat/regression/SimpleRegression.html#getSlopeConfidenceInterval()">getSlopeConfidenceInterval</a></strong>()</code>
 <div class="block">Returns the half-width of a 95% confidence interval for the slope
  estimate.</div>
 </td>
 </tr>
 <tr class="altColor">
 <td class="colFirst"><code>double</code></td>
 <td class="colLast"><code><strong><a href="../../../../../../org/apache/commons/math3/stat/regression/SimpleRegression.html#getSlopeConfidenceInterval(double)">getSlopeConfidenceInterval</a></strong>(double&nbsp;alpha)</code>
 <div class="block">Returns the half-width of a (100-100*alpha)% confidence interval for
  the slope estimate.</div>
 </td>
 </tr>
 <tr class="rowColor">
 <td class="colFirst"><code>double</code></td>
 <td class="colLast"><code><strong><a href="../../../../../../org/apache/commons/math3/stat/regression/SimpleRegression.html#getSlopeStdErr()">getSlopeStdErr</a></strong>()</code>
 <div class="block">Returns the <a href="http://www.xycoon.com/standerrorb(1).htm">standard
  error of the slope estimate</a>,
  usually denoted s(b1).</div>
 </td>
 </tr>
 <tr class="altColor">
 <td class="colFirst"><code>double</code></td>
 <td class="colLast"><code><strong><a href="../../../../../../org/apache/commons/math3/stat/regression/SimpleRegression.html#getSumOfCrossProducts()">getSumOfCrossProducts</a></strong>()</code>
 <div class="block">Returns the sum of crossproducts, x<sub>i</sub>*y<sub>i</sub>.</div>
 </td>
 </tr>
 <tr class="rowColor">
 <td class="colFirst"><code>double</code></td>
 <td class="colLast"><code><strong><a href="../../../../../../org/apache/commons/math3/stat/regression/SimpleRegression.html#getSumSquaredErrors()">getSumSquaredErrors</a></strong>()</code>
 <div class="block">Returns the <a href="http://www.xycoon.com/SumOfSquares.htm">
  sum of squared errors</a> (SSE) associated with the regression
  model.</div>
 </td>
 </tr>
 <tr class="altColor">
 <td class="colFirst"><code>double</code></td>
 <td class="colLast"><code><strong><a href="../../../../../../org/apache/commons/math3/stat/regression/SimpleRegression.html#getTotalSumSquares()">getTotalSumSquares</a></strong>()</code>
 <div class="block">Returns the sum of squared deviations of the y values about their mean.</div>
 </td>
 </tr>
 <tr class="rowColor">
 <td class="colFirst"><code>double</code></td>
 <td class="colLast"><code><strong><a href="../../../../../../org/apache/commons/math3/stat/regression/SimpleRegression.html#getXSumSquares()">getXSumSquares</a></strong>()</code>
 <div class="block">Returns the sum of squared deviations of the x values about their mean.</div>
 </td>
 </tr>
 <tr class="altColor">
 <td class="colFirst"><code>boolean</code></td>
 <td class="colLast"><code><strong><a href="../../../../../../org/apache/commons/math3/stat/regression/SimpleRegression.html#hasIntercept()">hasIntercept</a></strong>()</code>
 <div class="block">Returns true if the model includes an intercept term.</div>
 </td>
 </tr>
 <tr class="rowColor">
 <td class="colFirst"><code>double</code></td>
 <td class="colLast"><code><strong><a href="../../../../../../org/apache/commons/math3/stat/regression/SimpleRegression.html#predict(double)">predict</a></strong>(double&nbsp;x)</code>
 <div class="block">Returns the "predicted" <code>y</code> value associated with the
  supplied <code>x</code> value,  based on the data that has been
  added to the model when this method is activated.</div>
 </td>
 </tr>
 <tr class="altColor">
 <td class="colFirst"><code><a href="../../../../../../org/apache/commons/math3/stat/regression/RegressionResults.html" title="class in org.apache.commons.math3.stat.regression">RegressionResults</a></code></td>
 <td class="colLast"><code><strong><a href="../../../../../../org/apache/commons/math3/stat/regression/SimpleRegression.html#regress()">regress</a></strong>()</code>
 <div class="block">Performs a regression on data present in buffers and outputs a RegressionResults object.</div>
 </td>
 </tr>
 <tr class="rowColor">
 <td class="colFirst"><code><a href="../../../../../../org/apache/commons/math3/stat/regression/RegressionResults.html" title="class in org.apache.commons.math3.stat.regression">RegressionResults</a></code></td>
 <td class="colLast"><code><strong><a href="../../../../../../org/apache/commons/math3/stat/regression/SimpleRegression.html#regress(int[])">regress</a></strong>(int[]&nbsp;variablesToInclude)</code>
 <div class="block">Performs a regression on data present in buffers including only regressors
  indexed in variablesToInclude and outputs a RegressionResults object</div>
 </td>
 </tr>
 <tr class="altColor">
 <td class="colFirst"><code>void</code></td>
 <td class="colLast"><code><strong><a href="../../../../../../org/apache/commons/math3/stat/regression/SimpleRegression.html#removeData(double[][])">removeData</a></strong>(double[][]&nbsp;data)</code>
 <div class="block">Removes observations represented by the elements in <code>data</code>.</div>
 </td>
 </tr>
 <tr class="rowColor">
 <td class="colFirst"><code>void</code></td>
 <td class="colLast"><code><strong><a href="../../../../../../org/apache/commons/math3/stat/regression/SimpleRegression.html#removeData(double, double)">removeData</a></strong>(double&nbsp;x,
           double&nbsp;y)</code>
 <div class="block">Removes the observation (x,y) from the regression data set.</div>
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 <h4>SimpleRegression</h4>
 <pre>public&nbsp;SimpleRegression()</pre>
 <div class="block">Create an empty SimpleRegression instance</div>
 </li>
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 <h4>SimpleRegression</h4>
 <pre>public&nbsp;SimpleRegression(boolean&nbsp;includeIntercept)</pre>
 <div class="block">Create a SimpleRegression instance, specifying whether or not to estimate
  an intercept.

  <p>Use <code>false</code> to estimate a model with no intercept.  When the
  <code>hasIntercept</code> property is false, the model is estimated without a
  constant term and <a href="../../../../../../org/apache/commons/math3/stat/regression/SimpleRegression.html#getIntercept()"><code>getIntercept()</code></a> returns <code>0</code>.</p></div>
 <dl><dt><span class="strong">Parameters:</span></dt><dd><code>includeIntercept</code> - whether or not to include an intercept term in
  the regression model</dd></dl>
 </li>
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 <h4>addData</h4>
 <pre>public&nbsp;void&nbsp;addData(double&nbsp;x,
            double&nbsp;y)</pre>
 <div class="block">Adds the observation (x,y) to the regression data set.
  <p>
  Uses updating formulas for means and sums of squares defined in
  "Algorithms for Computing the Sample Variance: Analysis and
  Recommendations", Chan, T.F., Golub, G.H., and LeVeque, R.J.
  1983, American Statistician, vol. 37, pp. 242-247, referenced in
  Weisberg, S. "Applied Linear Regression". 2nd Ed. 1985.</p></div>
 <dl><dt><span class="strong">Parameters:</span></dt><dd><code>x</code> - independent variable value</dd><dd><code>y</code> - dependent variable value</dd></dl>
 </li>
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 <pre>public&nbsp;void&nbsp;append(<a href="../../../../../../org/apache/commons/math3/stat/regression/SimpleRegression.html" title="class in org.apache.commons.math3.stat.regression">SimpleRegression</a>&nbsp;reg)</pre>
 <div class="block">Appends data from another regression calculation to this one.

  <p>The mean update formulae are based on a paper written by Philippe
  P&eacute;bay:
  <a
  href="http://prod.sandia.gov/techlib/access-control.cgi/2008/086212.pdf">
  Formulas for Robust, One-Pass Parallel Computation of Covariances and
  Arbitrary-Order Statistical Moments</a>, 2008, Technical Report
  SAND2008-6212, Sandia National Laboratories.</p></div>
 <dl><dt><span class="strong">Parameters:</span></dt><dd><code>reg</code> - model to append data from</dd><dt><span class="strong">Since:</span></dt>
   <dd>3.3</dd></dl>
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 <pre>public&nbsp;void&nbsp;removeData(double&nbsp;x,
               double&nbsp;y)</pre>
 <div class="block">Removes the observation (x,y) from the regression data set.
  <p>
  Mirrors the addData method.  This method permits the use of
  SimpleRegression instances in streaming mode where the regression
  is applied to a sliding "window" of observations, however the caller is
  responsible for maintaining the set of observations in the window.</p>

  The method has no effect if there are no points of data (i.e. n=0)</div>
 <dl><dt><span class="strong">Parameters:</span></dt><dd><code>x</code> - independent variable value</dd><dd><code>y</code> - dependent variable value</dd></dl>
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 <pre>public&nbsp;void&nbsp;addData(double[][]&nbsp;data)
              throws <a href="../../../../../../org/apache/commons/math3/stat/regression/ModelSpecificationException.html" title="class in org.apache.commons.math3.stat.regression">ModelSpecificationException</a></pre>
 <div class="block">Adds the observations represented by the elements in
  <code>data</code>.
  <p>
  <code>(data[0][0],data[0][1])</code> will be the first observation, then
  <code>(data[1][0],data[1][1])</code>, etc.</p>
  <p>
  This method does not replace data that has already been added.  The
  observations represented by <code>data</code> are added to the existing
  dataset.</p>
  <p>
  To replace all data, use <code>clear()</code> before adding the new
  data.</p></div>
 <dl><dt><span class="strong">Parameters:</span></dt><dd><code>data</code> - array of observations to be added</dd>
 <dt><span class="strong">Throws:</span></dt>
 <dd><code><a href="../../../../../../org/apache/commons/math3/stat/regression/ModelSpecificationException.html" title="class in org.apache.commons.math3.stat.regression">ModelSpecificationException</a></code> - if the length of <code>data[i]</code> is not
  greater than or equal to 2</dd></dl>
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 <pre>public&nbsp;void&nbsp;addObservation(double[]&nbsp;x,
                   double&nbsp;y)
                     throws <a href="../../../../../../org/apache/commons/math3/stat/regression/ModelSpecificationException.html" title="class in org.apache.commons.math3.stat.regression">ModelSpecificationException</a></pre>
 <div class="block">Adds one observation to the regression model.</div>
 <dl>
 <dt><strong>Specified by:</strong></dt>
 <dd><code><a href="../../../../../../org/apache/commons/math3/stat/regression/UpdatingMultipleLinearRegression.html#addObservation(double[], double)">addObservation</a></code>&nbsp;in interface&nbsp;<code><a href="../../../../../../org/apache/commons/math3/stat/regression/UpdatingMultipleLinearRegression.html" title="interface in org.apache.commons.math3.stat.regression">UpdatingMultipleLinearRegression</a></code></dd>
 <dt><span class="strong">Parameters:</span></dt><dd><code>x</code> - the independent variables which form the design matrix</dd><dd><code>y</code> - the dependent or response variable</dd>
 <dt><span class="strong">Throws:</span></dt>
 <dd><code><a href="../../../../../../org/apache/commons/math3/stat/regression/ModelSpecificationException.html" title="class in org.apache.commons.math3.stat.regression">ModelSpecificationException</a></code> - if the length of <code>x</code> does not equal
  the number of independent variables in the model</dd></dl>
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 <pre>public&nbsp;void&nbsp;addObservations(double[][]&nbsp;x,
                    double[]&nbsp;y)
                      throws <a href="../../../../../../org/apache/commons/math3/stat/regression/ModelSpecificationException.html" title="class in org.apache.commons.math3.stat.regression">ModelSpecificationException</a></pre>
 <div class="block">Adds a series of observations to the regression model. The lengths of
  x and y must be the same and x must be rectangular.</div>
 <dl>
 <dt><strong>Specified by:</strong></dt>
 <dd><code><a href="../../../../../../org/apache/commons/math3/stat/regression/UpdatingMultipleLinearRegression.html#addObservations(double[][], double[])">addObservations</a></code>&nbsp;in interface&nbsp;<code><a href="../../../../../../org/apache/commons/math3/stat/regression/UpdatingMultipleLinearRegression.html" title="interface in org.apache.commons.math3.stat.regression">UpdatingMultipleLinearRegression</a></code></dd>
 <dt><span class="strong">Parameters:</span></dt><dd><code>x</code> - a series of observations on the independent variables</dd><dd><code>y</code> - a series of observations on the dependent variable
  The length of x and y must be the same</dd>
 <dt><span class="strong">Throws:</span></dt>
 <dd><code><a href="../../../../../../org/apache/commons/math3/stat/regression/ModelSpecificationException.html" title="class in org.apache.commons.math3.stat.regression">ModelSpecificationException</a></code> - if <code>x</code> is not rectangular, does not match
  the length of <code>y</code> or does not contain sufficient data to estimate the model</dd></dl>
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 <pre>public&nbsp;void&nbsp;removeData(double[][]&nbsp;data)</pre>
 <div class="block">Removes observations represented by the elements in <code>data</code>.
  <p>
  If the array is larger than the current n, only the first n elements are
  processed.  This method permits the use of SimpleRegression instances in
  streaming mode where the regression is applied to a sliding "window" of
  observations, however the caller is responsible for maintaining the set
  of observations in the window.</p>
  <p>
  To remove all data, use <code>clear()</code>.</p></div>
 <dl><dt><span class="strong">Parameters:</span></dt><dd><code>data</code> - array of observations to be removed</dd></dl>
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 <pre>public&nbsp;void&nbsp;clear()</pre>
 <div class="block">Clears all data from the model.</div>
 <dl>
 <dt><strong>Specified by:</strong></dt>
 <dd><code><a href="../../../../../../org/apache/commons/math3/stat/regression/UpdatingMultipleLinearRegression.html#clear()">clear</a></code>&nbsp;in interface&nbsp;<code><a href="../../../../../../org/apache/commons/math3/stat/regression/UpdatingMultipleLinearRegression.html" title="interface in org.apache.commons.math3.stat.regression">UpdatingMultipleLinearRegression</a></code></dd>
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 <pre>public&nbsp;long&nbsp;getN()</pre>
 <div class="block">Returns the number of observations that have been added to the model.</div>
 <dl>
 <dt><strong>Specified by:</strong></dt>
 <dd><code><a href="../../../../../../org/apache/commons/math3/stat/regression/UpdatingMultipleLinearRegression.html#getN()">getN</a></code>&nbsp;in interface&nbsp;<code><a href="../../../../../../org/apache/commons/math3/stat/regression/UpdatingMultipleLinearRegression.html" title="interface in org.apache.commons.math3.stat.regression">UpdatingMultipleLinearRegression</a></code></dd>
 <dt><span class="strong">Returns:</span></dt><dd>n number of observations that have been added.</dd></dl>
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 <pre>public&nbsp;double&nbsp;predict(double&nbsp;x)</pre>
 <div class="block">Returns the "predicted" <code>y</code> value associated with the
  supplied <code>x</code> value,  based on the data that has been
  added to the model when this method is activated.
  <p>
  <code> predict(x) = intercept + slope * x </code></p>
  <p>
  <strong>Preconditions</strong>: <ul>
  <li>At least two observations (with at least two different x values)
  must have been added before invoking this method. If this method is
  invoked before a model can be estimated, <code>Double,NaN</code> is
  returned.
  </li></ul></p></div>
 <dl><dt><span class="strong">Parameters:</span></dt><dd><code>x</code> - input <code>x</code> value</dd>
 <dt><span class="strong">Returns:</span></dt><dd>predicted <code>y</code> value</dd></dl>
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 <pre>public&nbsp;double&nbsp;getIntercept()</pre>
 <div class="block">Returns the intercept of the estimated regression line, if
  <a href="../../../../../../org/apache/commons/math3/stat/regression/SimpleRegression.html#hasIntercept()"><code>hasIntercept()</code></a> is true; otherwise 0.
  <p>
  The least squares estimate of the intercept is computed using the
  <a href="http://www.xycoon.com/estimation4.htm">normal equations</a>.
  The intercept is sometimes denoted b0.</p>
  <p>
  <strong>Preconditions</strong>: <ul>
  <li>At least two observations (with at least two different x values)
  must have been added before invoking this method. If this method is
  invoked before a model can be estimated, <code>Double,NaN</code> is
  returned.
  </li></ul></p></div>
 <dl><dt><span class="strong">Returns:</span></dt><dd>the intercept of the regression line if the model includes an
  intercept; 0 otherwise</dd><dt><span class="strong">See Also:</span></dt><dd><a href="../../../../../../org/apache/commons/math3/stat/regression/SimpleRegression.html#SimpleRegression(boolean)"><code>SimpleRegression(boolean)</code></a></dd></dl>
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 <pre>public&nbsp;boolean&nbsp;hasIntercept()</pre>
 <div class="block">Returns true if the model includes an intercept term.</div>
 <dl>
 <dt><strong>Specified by:</strong></dt>
 <dd><code><a href="../../../../../../org/apache/commons/math3/stat/regression/UpdatingMultipleLinearRegression.html#hasIntercept()">hasIntercept</a></code>&nbsp;in interface&nbsp;<code><a href="../../../../../../org/apache/commons/math3/stat/regression/UpdatingMultipleLinearRegression.html" title="interface in org.apache.commons.math3.stat.regression">UpdatingMultipleLinearRegression</a></code></dd>
 <dt><span class="strong">Returns:</span></dt><dd>true if the regression includes an intercept; false otherwise</dd><dt><span class="strong">See Also:</span></dt><dd><a href="../../../../../../org/apache/commons/math3/stat/regression/SimpleRegression.html#SimpleRegression(boolean)"><code>SimpleRegression(boolean)</code></a></dd></dl>
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 <pre>public&nbsp;double&nbsp;getSlope()</pre>
 <div class="block">Returns the slope of the estimated regression line.
  <p>
  The least squares estimate of the slope is computed using the
  <a href="http://www.xycoon.com/estimation4.htm">normal equations</a>.
  The slope is sometimes denoted b1.</p>
  <p>
  <strong>Preconditions</strong>: <ul>
  <li>At least two observations (with at least two different x values)
  must have been added before invoking this method. If this method is
  invoked before a model can be estimated, <code>Double.NaN</code> is
  returned.
  </li></ul></p></div>
 <dl><dt><span class="strong">Returns:</span></dt><dd>the slope of the regression line</dd></dl>
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 <pre>public&nbsp;double&nbsp;getSumSquaredErrors()</pre>
 <div class="block">Returns the <a href="http://www.xycoon.com/SumOfSquares.htm">
  sum of squared errors</a> (SSE) associated with the regression
  model.
  <p>
  The sum is computed using the computational formula</p>
  <p>
  <code>SSE = SYY - (SXY * SXY / SXX)</code></p>
  <p>
  where <code>SYY</code> is the sum of the squared deviations of the y
  values about their mean, <code>SXX</code> is similarly defined and
  <code>SXY</code> is the sum of the products of x and y mean deviations.
  </p><p>
  The sums are accumulated using the updating algorithm referenced in
  <a href="../../../../../../org/apache/commons/math3/stat/regression/SimpleRegression.html#addData(double, double)"><code>addData(double, double)</code></a>.</p>
  <p>
  The return value is constrained to be non-negative - i.e., if due to
  rounding errors the computational formula returns a negative result,
  0 is returned.</p>
  <p>
  <strong>Preconditions</strong>: <ul>
  <li>At least two observations (with at least two different x values)
  must have been added before invoking this method. If this method is
  invoked before a model can be estimated, <code>Double,NaN</code> is
  returned.
  </li></ul></p></div>
 <dl><dt><span class="strong">Returns:</span></dt><dd>sum of squared errors associated with the regression model</dd></dl>
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 <pre>public&nbsp;double&nbsp;getTotalSumSquares()</pre>
 <div class="block">Returns the sum of squared deviations of the y values about their mean.
  <p>
  This is defined as SSTO
  <a href="http://www.xycoon.com/SumOfSquares.htm">here</a>.</p>
  <p>
  If <code>n < 2</code>, this returns <code>Double.NaN</code>.</p></div>
 <dl><dt><span class="strong">Returns:</span></dt><dd>sum of squared deviations of y values</dd></dl>
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 <pre>public&nbsp;double&nbsp;getXSumSquares()</pre>
 <div class="block">Returns the sum of squared deviations of the x values about their mean.

  If <code>n < 2</code>, this returns <code>Double.NaN</code>.</p></div>
 <dl><dt><span class="strong">Returns:</span></dt><dd>sum of squared deviations of x values</dd></dl>
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 <pre>public&nbsp;double&nbsp;getSumOfCrossProducts()</pre>
 <div class="block">Returns the sum of crossproducts, x<sub>i</sub>*y<sub>i</sub>.</div>
 <dl><dt><span class="strong">Returns:</span></dt><dd>sum of cross products</dd></dl>
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 <pre>public&nbsp;double&nbsp;getRegressionSumSquares()</pre>
 <div class="block">Returns the sum of squared deviations of the predicted y values about
  their mean (which equals the mean of y).
  <p>
  This is usually abbreviated SSR or SSM.  It is defined as SSM
  <a href="http://www.xycoon.com/SumOfSquares.htm">here</a></p>
  <p>
  <strong>Preconditions</strong>: <ul>
  <li>At least two observations (with at least two different x values)
  must have been added before invoking this method. If this method is
  invoked before a model can be estimated, <code>Double.NaN</code> is
  returned.
  </li></ul></p></div>
 <dl><dt><span class="strong">Returns:</span></dt><dd>sum of squared deviations of predicted y values</dd></dl>
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 <pre>public&nbsp;double&nbsp;getMeanSquareError()</pre>
 <div class="block">Returns the sum of squared errors divided by the degrees of freedom,
  usually abbreviated MSE.
  <p>
  If there are fewer than <strong>three</strong> data pairs in the model,
  or if there is no variation in <code>x</code>, this returns
  <code>Double.NaN</code>.</p></div>
 <dl><dt><span class="strong">Returns:</span></dt><dd>sum of squared deviations of y values</dd></dl>
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 <pre>public&nbsp;double&nbsp;getR()</pre>
 <div class="block">Returns <a href="http://mathworld.wolfram.com/CorrelationCoefficient.html">
  Pearson's product moment correlation coefficient</a>,
  usually denoted r.
  <p>
  <strong>Preconditions</strong>: <ul>
  <li>At least two observations (with at least two different x values)
  must have been added before invoking this method. If this method is
  invoked before a model can be estimated, <code>Double,NaN</code> is
  returned.
  </li></ul></p></div>
 <dl><dt><span class="strong">Returns:</span></dt><dd>Pearson's r</dd></dl>
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 <pre>public&nbsp;double&nbsp;getRSquare()</pre>
 <div class="block">Returns the <a href="http://www.xycoon.com/coefficient1.htm">
  coefficient of determination</a>,
  usually denoted r-square.
  <p>
  <strong>Preconditions</strong>: <ul>
  <li>At least two observations (with at least two different x values)
  must have been added before invoking this method. If this method is
  invoked before a model can be estimated, <code>Double,NaN</code> is
  returned.
  </li></ul></p></div>
 <dl><dt><span class="strong">Returns:</span></dt><dd>r-square</dd></dl>
 </li>
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 <pre>public&nbsp;double&nbsp;getInterceptStdErr()</pre>
 <div class="block">Returns the <a href="http://www.xycoon.com/standarderrorb0.htm">
  standard error of the intercept estimate</a>,
  usually denoted s(b0).
  <p>
  If there are fewer that <strong>three</strong> observations in the
  model, or if there is no variation in x, this returns
  <code>Double.NaN</code>.</p> Additionally, a <code>Double.NaN</code> is
  returned when the intercept is constrained to be zero</div>
 <dl><dt><span class="strong">Returns:</span></dt><dd>standard error associated with intercept estimate</dd></dl>
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 <pre>public&nbsp;double&nbsp;getSlopeStdErr()</pre>
 <div class="block">Returns the <a href="http://www.xycoon.com/standerrorb(1).htm">standard
  error of the slope estimate</a>,
  usually denoted s(b1).
  <p>
  If there are fewer that <strong>three</strong> data pairs in the model,
  or if there is no variation in x, this returns <code>Double.NaN</code>.
  </p></div>
 <dl><dt><span class="strong">Returns:</span></dt><dd>standard error associated with slope estimate</dd></dl>
 </li>
 </ul>
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 <pre>public&nbsp;double&nbsp;getSlopeConfidenceInterval()
                                   throws <a href="../../../../../../org/apache/commons/math3/exception/OutOfRangeException.html" title="class in org.apache.commons.math3.exception">OutOfRangeException</a></pre>
 <div class="block">Returns the half-width of a 95% confidence interval for the slope
  estimate.
  <p>
  The 95% confidence interval is</p>
  <p>
  <code>(getSlope() - getSlopeConfidenceInterval(),
  getSlope() + getSlopeConfidenceInterval())</code></p>
  <p>
  If there are fewer that <strong>three</strong> observations in the
  model, or if there is no variation in x, this returns
  <code>Double.NaN</code>.</p>
  <p>
  <strong>Usage Note</strong>:<br>
  The validity of this statistic depends on the assumption that the
  observations included in the model are drawn from a
  <a href="http://mathworld.wolfram.com/BivariateNormalDistribution.html">
  Bivariate Normal Distribution</a>.</p></div>
 <dl><dt><span class="strong">Returns:</span></dt><dd>half-width of 95% confidence interval for the slope estimate</dd>
 <dt><span class="strong">Throws:</span></dt>
 <dd><code><a href="../../../../../../org/apache/commons/math3/exception/OutOfRangeException.html" title="class in org.apache.commons.math3.exception">OutOfRangeException</a></code> - if the confidence interval can not be computed.</dd></dl>
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 <pre>public&nbsp;double&nbsp;getSlopeConfidenceInterval(double&nbsp;alpha)
                                   throws <a href="../../../../../../org/apache/commons/math3/exception/OutOfRangeException.html" title="class in org.apache.commons.math3.exception">OutOfRangeException</a></pre>
 <div class="block">Returns the half-width of a (100-100*alpha)% confidence interval for
  the slope estimate.
  <p>
  The (100-100*alpha)% confidence interval is </p>
  <p>
  <code>(getSlope() - getSlopeConfidenceInterval(),
  getSlope() + getSlopeConfidenceInterval())</code></p>
  <p>
  To request, for example, a 99% confidence interval, use
  <code>alpha = .01</code></p>
  <p>
  <strong>Usage Note</strong>:<br>
  The validity of this statistic depends on the assumption that the
  observations included in the model are drawn from a
  <a href="http://mathworld.wolfram.com/BivariateNormalDistribution.html">
  Bivariate Normal Distribution</a>.</p>
  <p>
  <strong> Preconditions:</strong><ul>
  <li>If there are fewer that <strong>three</strong> observations in the
  model, or if there is no variation in x, this returns
  <code>Double.NaN</code>.
  </li>
  <li><code>(0 < alpha < 1)</code>; otherwise an
  <code>OutOfRangeException</code> is thrown.
  </li></ul></p></div>
 <dl><dt><span class="strong">Parameters:</span></dt><dd><code>alpha</code> - the desired significance level</dd>
 <dt><span class="strong">Returns:</span></dt><dd>half-width of 95% confidence interval for the slope estimate</dd>
 <dt><span class="strong">Throws:</span></dt>
 <dd><code><a href="../../../../../../org/apache/commons/math3/exception/OutOfRangeException.html" title="class in org.apache.commons.math3.exception">OutOfRangeException</a></code> - if the confidence interval can not be computed.</dd></dl>
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 <pre>public&nbsp;double&nbsp;getSignificance()</pre>
 <div class="block">Returns the significance level of the slope (equiv) correlation.
  <p>
  Specifically, the returned value is the smallest <code>alpha</code>
  such that the slope confidence interval with significance level
  equal to <code>alpha</code> does not include <code>0</code>.
  On regression output, this is often denoted <code>Prob(|t| > 0)</code>
  </p><p>
  <strong>Usage Note</strong>:<br>
  The validity of this statistic depends on the assumption that the
  observations included in the model are drawn from a
  <a href="http://mathworld.wolfram.com/BivariateNormalDistribution.html">
  Bivariate Normal Distribution</a>.</p>
  <p>
  If there are fewer that <strong>three</strong> observations in the
  model, or if there is no variation in x, this returns
  <code>Double.NaN</code>.</p></div>
 <dl><dt><span class="strong">Returns:</span></dt><dd>significance level for slope/correlation</dd>
 <dt><span class="strong">Throws:</span></dt>
 <dd><code><a href="../../../../../../org/apache/commons/math3/exception/MaxCountExceededException.html" title="class in org.apache.commons.math3.exception">MaxCountExceededException</a></code> - if the significance level can not be computed.</dd></dl>
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 <h4>regress</h4>
 <pre>public&nbsp;<a href="../../../../../../org/apache/commons/math3/stat/regression/RegressionResults.html" title="class in org.apache.commons.math3.stat.regression">RegressionResults</a>&nbsp;regress()
                           throws <a href="../../../../../../org/apache/commons/math3/stat/regression/ModelSpecificationException.html" title="class in org.apache.commons.math3.stat.regression">ModelSpecificationException</a>,
                                  <a href="../../../../../../org/apache/commons/math3/exception/NoDataException.html" title="class in org.apache.commons.math3.exception">NoDataException</a></pre>
 <div class="block">Performs a regression on data present in buffers and outputs a RegressionResults object.

  <p>If there are fewer than 3 observations in the model and <code>hasIntercept</code> is true
  a <code>NoDataException</code> is thrown.  If there is no intercept term, the model must
  contain at least 2 observations.</p></div>
 <dl>
 <dt><strong>Specified by:</strong></dt>
 <dd><code><a href="../../../../../../org/apache/commons/math3/stat/regression/UpdatingMultipleLinearRegression.html#regress()">regress</a></code>&nbsp;in interface&nbsp;<code><a href="../../../../../../org/apache/commons/math3/stat/regression/UpdatingMultipleLinearRegression.html" title="interface in org.apache.commons.math3.stat.regression">UpdatingMultipleLinearRegression</a></code></dd>
 <dt><span class="strong">Returns:</span></dt><dd>RegressionResults acts as a container of regression output</dd>
 <dt><span class="strong">Throws:</span></dt>
 <dd><code><a href="../../../../../../org/apache/commons/math3/stat/regression/ModelSpecificationException.html" title="class in org.apache.commons.math3.stat.regression">ModelSpecificationException</a></code> - if the model is not correctly specified</dd>
 <dd><code><a href="../../../../../../org/apache/commons/math3/exception/NoDataException.html" title="class in org.apache.commons.math3.exception">NoDataException</a></code> - if there is not sufficient data in the model to
  estimate the regression parameters</dd></dl>
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 <h4>regress</h4>
 <pre>public&nbsp;<a href="../../../../../../org/apache/commons/math3/stat/regression/RegressionResults.html" title="class in org.apache.commons.math3.stat.regression">RegressionResults</a>&nbsp;regress(int[]&nbsp;variablesToInclude)
                           throws <a href="../../../../../../org/apache/commons/math3/exception/MathIllegalArgumentException.html" title="class in org.apache.commons.math3.exception">MathIllegalArgumentException</a></pre>
 <div class="block">Performs a regression on data present in buffers including only regressors
  indexed in variablesToInclude and outputs a RegressionResults object</div>
 <dl>
 <dt><strong>Specified by:</strong></dt>
 <dd><code><a href="../../../../../../org/apache/commons/math3/stat/regression/UpdatingMultipleLinearRegression.html#regress(int[])">regress</a></code>&nbsp;in interface&nbsp;<code><a href="../../../../../../org/apache/commons/math3/stat/regression/UpdatingMultipleLinearRegression.html" title="interface in org.apache.commons.math3.stat.regression">UpdatingMultipleLinearRegression</a></code></dd>
 <dt><span class="strong">Parameters:</span></dt><dd><code>variablesToInclude</code> - an array of indices of regressors to include</dd>
 <dt><span class="strong">Returns:</span></dt><dd>RegressionResults acts as a container of regression output</dd>
 <dt><span class="strong">Throws:</span></dt>
 <dd><code><a href="../../../../../../org/apache/commons/math3/exception/MathIllegalArgumentException.html" title="class in org.apache.commons.math3.exception">MathIllegalArgumentException</a></code> - if the variablesToInclude array is null or zero length</dd>
 <dd><code><a href="../../../../../../org/apache/commons/math3/exception/OutOfRangeException.html" title="class in org.apache.commons.math3.exception">OutOfRangeException</a></code> - if a requested variable is not present in model</dd></dl>
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