001 /* 002 * Licensed to the Apache Software Foundation (ASF) under one or more 003 * contributor license agreements. See the NOTICE file distributed with 004 * this work for additional information regarding copyright ownership. 005 * The ASF licenses this file to You under the Apache License, Version 2.0 006 * (the "License"); you may not use this file except in compliance with 007 * the License. You may obtain a copy of the License at 008 * 009 * http://www.apache.org/licenses/LICENSE-2.0 010 * 011 * Unless required by applicable law or agreed to in writing, software 012 * distributed under the License is distributed on an "AS IS" BASIS, 013 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 014 * See the License for the specific language governing permissions and 015 * limitations under the License. 016 */ 017 package org.apache.commons.math.stat.inference; 018 019 import org.apache.commons.math.MathException; 020 import java.util.Collection; 021 022 /** 023 * An interface for one-way ANOVA (analysis of variance). 024 * 025 * <p> Tests for differences between two or more categories of univariate data 026 * (for example, the body mass index of accountants, lawyers, doctors and 027 * computer programmers). When two categories are given, this is equivalent to 028 * the {@link org.apache.commons.math.stat.inference.TTest}. 029 * </p> 030 * 031 * @since 1.2 032 * @version $Revision: 811786 $ $Date: 2009-09-06 05:36:08 -0400 (Sun, 06 Sep 2009) $ 033 */ 034 public interface OneWayAnova { 035 036 /** 037 * Computes the ANOVA F-value for a collection of <code>double[]</code> 038 * arrays. 039 * 040 * <p><strong>Preconditions</strong>: <ul> 041 * <li>The categoryData <code>Collection</code> must contain 042 * <code>double[]</code> arrays.</li> 043 * <li> There must be at least two <code>double[]</code> arrays in the 044 * <code>categoryData</code> collection and each of these arrays must 045 * contain at least two values.</li></ul></p> 046 * 047 * @param categoryData <code>Collection</code> of <code>double[]</code> 048 * arrays each containing data for one category 049 * @return Fvalue 050 * @throws IllegalArgumentException if the preconditions are not met 051 * @throws MathException if the statistic can not be computed do to a 052 * convergence or other numerical error. 053 */ 054 double anovaFValue(Collection<double[]> categoryData) 055 throws IllegalArgumentException, MathException; 056 057 /** 058 * Computes the ANOVA P-value for a collection of <code>double[]</code> 059 * arrays. 060 * 061 * <p><strong>Preconditions</strong>: <ul> 062 * <li>The categoryData <code>Collection</code> must contain 063 * <code>double[]</code> arrays.</li> 064 * <li> There must be at least two <code>double[]</code> arrays in the 065 * <code>categoryData</code> collection and each of these arrays must 066 * contain at least two values.</li></ul></p> 067 * 068 * @param categoryData <code>Collection</code> of <code>double[]</code> 069 * arrays each containing data for one category 070 * @return Pvalue 071 * @throws IllegalArgumentException if the preconditions are not met 072 * @throws MathException if the statistic can not be computed do to a 073 * convergence or other numerical error. 074 */ 075 double anovaPValue(Collection<double[]> categoryData) 076 throws IllegalArgumentException, MathException; 077 078 /** 079 * Performs an ANOVA test, evaluating the null hypothesis that there 080 * is no difference among the means of the data categories. 081 * 082 * <p><strong>Preconditions</strong>: <ul> 083 * <li>The categoryData <code>Collection</code> must contain 084 * <code>double[]</code> arrays.</li> 085 * <li> There must be at least two <code>double[]</code> arrays in the 086 * <code>categoryData</code> collection and each of these arrays must 087 * contain at least two values.</li> 088 * <li>alpha must be strictly greater than 0 and less than or equal to 0.5. 089 * </li></ul></p> 090 * 091 * @param categoryData <code>Collection</code> of <code>double[]</code> 092 * arrays each containing data for one category 093 * @param alpha significance level of the test 094 * @return true if the null hypothesis can be rejected with 095 * confidence 1 - alpha 096 * @throws IllegalArgumentException if the preconditions are not met 097 * @throws MathException if the statistic can not be computed do to a 098 * convergence or other numerical error. 099 */ 100 boolean anovaTest(Collection<double[]> categoryData, double alpha) 101 throws IllegalArgumentException, MathException; 102 103 }