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diff --git a/src/main/java/org/apache/commons/math3/stat/regression/UpdatingMultipleLinearRegression.java b/src/main/java/org/apache/commons/math3/stat/regression/UpdatingMultipleLinearRegression.java
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+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.commons.math3.stat.regression;
+
+import org.apache.commons.math3.exception.MathIllegalArgumentException;
+import org.apache.commons.math3.exception.NoDataException;
+
+/**
+ * An interface for regression models allowing for dynamic updating of the data.
+ * That is, the entire data set need not be loaded into memory. As observations
+ * become available, they can be added to the regression  model and an updated
+ * estimate regression statistics can be calculated.
+ *
+ * @since 3.0
+ */
+public interface UpdatingMultipleLinearRegression {
+
+    /**
+     * Returns true if a constant has been included false otherwise.
+     *
+     * @return true if constant exists, false otherwise
+     */
+    boolean hasIntercept();
+
+    /**
+     * Returns the number of observations added to the regression model.
+     *
+     * @return Number of observations
+     */
+    long getN();
+
+    /**
+     * Adds one observation to the regression model.
+     *
+     * @param x the independent variables which form the design matrix
+     * @param y the dependent or response variable
+     * @throws ModelSpecificationException if the length of {@code x} does not equal
+     * the number of independent variables in the model
+     */
+    void addObservation(double[] x, double y) throws ModelSpecificationException;
+
+    /**
+     * Adds a series of observations to the regression model. The lengths of
+     * x and y must be the same and x must be rectangular.
+     *
+     * @param x a series of observations on the independent variables
+     * @param y a series of observations on the dependent variable
+     * The length of x and y must be the same
+     * @throws ModelSpecificationException if {@code x} is not rectangular, does not match
+     * the length of {@code y} or does not contain sufficient data to estimate the model
+     */
+    void addObservations(double[][] x, double[] y) throws ModelSpecificationException;
+
+    /**
+     * Clears internal buffers and resets the regression model. This means all
+     * data and derived values are initialized
+     */
+    void clear();
+
+
+    /**
+     * Performs a regression on data present in buffers and outputs a RegressionResults object
+     * @return RegressionResults acts as a container of regression output
+     * @throws ModelSpecificationException if the model is not correctly specified
+     * @throws NoDataException if there is not sufficient data in the model to
+     * estimate the regression parameters
+     */
+    RegressionResults regress() throws ModelSpecificationException, NoDataException;
+
+    /**
+     * Performs a regression on data present in buffers including only regressors
+     * indexed in variablesToInclude and outputs a RegressionResults object
+     * @param variablesToInclude an array of indices of regressors to include
+     * @return RegressionResults acts as a container of regression output
+     * @throws ModelSpecificationException if the model is not correctly specified
+     * @throws MathIllegalArgumentException if the variablesToInclude array is null or zero length
+     */
+    RegressionResults regress(int[] variablesToInclude) throws ModelSpecificationException, MathIllegalArgumentException;
+}