1 /* 2 * Licensed to the Apache Software Foundation (ASF) under one or more 3 * contributor license agreements. See the NOTICE file distributed with 4 * this work for additional information regarding copyright ownership. 5 * The ASF licenses this file to You under the Apache License, Version 2.0 6 * (the "License"); you may not use this file except in compliance with 7 * the License. You may obtain a copy of the License at 8 * 9 * http://www.apache.org/licenses/LICENSE-2.0 10 * 11 * Unless required by applicable law or agreed to in writing, software 12 * distributed under the License is distributed on an "AS IS" BASIS, 13 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 14 * See the License for the specific language governing permissions and 15 * limitations under the License. 16 */ 17 package org.apache.commons.math.stat.regression; 18 19 /** 20 * The multiple linear regression can be represented in matrix-notation. 21 * <pre> 22 * y=X*b+u 23 * </pre> 24 * where y is an <code>n-vector</code> <b>regressand</b>, X is a <code>[n,k]</code> matrix whose <code>k</code> columns are called 25 * <b>regressors</b>, b is <code>k-vector</code> of <b>regression parameters</b> and <code>u</code> is an <code>n-vector</code> 26 * of <b>error terms</b> or <b>residuals</b>. 27 * 28 * The notation is quite standard in literature, 29 * cf eg <a href="http://www.econ.queensu.ca/ETM">Davidson and MacKinnon, Econometrics Theory and Methods, 2004</a>. 30 * @version $Revision: 811685 $ $Date: 2009-09-05 19:36:48 +0200 (sam. 05 sept. 2009) $ 31 * @since 2.0 32 */ 33 public interface MultipleLinearRegression { 34 35 /** 36 * Estimates the regression parameters b. 37 * 38 * @return The [k,1] array representing b 39 */ 40 double[] estimateRegressionParameters(); 41 42 /** 43 * Estimates the variance of the regression parameters, ie Var(b). 44 * 45 * @return The [k,k] array representing the variance of b 46 */ 47 double[][] estimateRegressionParametersVariance(); 48 49 /** 50 * Estimates the residuals, ie u = y - X*b. 51 * 52 * @return The [n,1] array representing the residuals 53 */ 54 double[] estimateResiduals(); 55 56 /** 57 * Returns the variance of the regressand, ie Var(y). 58 * 59 * @return The double representing the variance of y 60 */ 61 double estimateRegressandVariance(); 62 63 /** 64 * Returns the standard errors of the regression parameters. 65 * 66 * @return standard errors of estimated regression parameters 67 */ 68 double[] estimateRegressionParametersStandardErrors(); 69 70 } 71
