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: 670469 $ $Date: 2008-06-23 04:01:38 -0400 (Mon, 23 Jun 2008) $
033 */
034 public interface OneWayAnova {
035 /**
036 * Computes the ANOVA F-value for a collection of <code>double[]</code>
037 * arrays.
038 *
039 * <p><strong>Preconditions</strong>: <ul>
040 * <li>The categoryData <code>Collection</code> must contain
041 * <code>double[]</code> arrays.</li>
042 * <li> There must be at least two <code>double[]</code> arrays in the
043 * <code>categoryData</code> collection and each of these arrays must
044 * contain at least two values.</li></ul></p>
045 *
046 * @param categoryData <code>Collection</code> of <code>double[]</code>
047 * arrays each containing data for one category
048 * @return Fvalue
049 * @throws IllegalArgumentException if the preconditions are not met
050 * @throws MathException if the statistic can not be computed do to a
051 * convergence or other numerical error.
052 */
053 public double anovaFValue(Collection<double[]> categoryData)
054 throws IllegalArgumentException, MathException;
055
056 /**
057 * Computes the ANOVA P-value for a collection of <code>double[]</code>
058 * arrays.
059 *
060 * <p><strong>Preconditions</strong>: <ul>
061 * <li>The categoryData <code>Collection</code> must contain
062 * <code>double[]</code> arrays.</li>
063 * <li> There must be at least two <code>double[]</code> arrays in the
064 * <code>categoryData</code> collection and each of these arrays must
065 * contain at least two values.</li></ul></p>
066 *
067 * @param categoryData <code>Collection</code> of <code>double[]</code>
068 * arrays each containing data for one category
069 * @return Pvalue
070 * @throws IllegalArgumentException if the preconditions are not met
071 * @throws MathException if the statistic can not be computed do to a
072 * convergence or other numerical error.
073 */
074 public double anovaPValue(Collection<double[]> categoryData)
075 throws IllegalArgumentException, MathException;
076
077 /**
078 * Performs an ANOVA test, evaluating the null hypothesis that there
079 * is no difference among the means of the data categories.
080 *
081 * <p><strong>Preconditions</strong>: <ul>
082 * <li>The categoryData <code>Collection</code> must contain
083 * <code>double[]</code> arrays.</li>
084 * <li> There must be at least two <code>double[]</code> arrays in the
085 * <code>categoryData</code> collection and each of these arrays must
086 * contain at least two values.</li>
087 * <li>alpha must be strictly greater than 0 and less than or equal to 0.5.
088 * </li></ul></p>
089 *
090 * @param categoryData <code>Collection</code> of <code>double[]</code>
091 * arrays each containing data for one category
092 * @param alpha significance level of the test
093 * @return true if the null hypothesis can be rejected with
094 * confidence 1 - alpha
095 * @throws IllegalArgumentException if the preconditions are not met
096 * @throws MathException if the statistic can not be computed do to a
097 * convergence or other numerical error.
098 */
099 public boolean anovaTest(Collection<double[]> categoryData, double alpha)
100 throws IllegalArgumentException, MathException;
101
102 }