1 | /*
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2 | * Licensed to the Apache Software Foundation (ASF) under one or more
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3 | * contributor license agreements. See the NOTICE file distributed with
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4 | * this work for additional information regarding copyright ownership.
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5 | * The ASF licenses this file to You under the Apache License, Version 2.0
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6 | * (the "License"); you may not use this file except in compliance with
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7 | * the License. You may obtain a copy of the License at
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8 | *
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9 | * http://www.apache.org/licenses/LICENSE-2.0
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10 | *
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11 | * Unless required by applicable law or agreed to in writing, software
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12 | * distributed under the License is distributed on an "AS IS" BASIS,
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13 | * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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14 | * See the License for the specific language governing permissions and
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15 | * limitations under the License.
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16 | */
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17 | package agents.anac.y2019.harddealer.math3.stat.interval;
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18 |
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19 | import agents.anac.y2019.harddealer.math3.exception.NotPositiveException;
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20 | import agents.anac.y2019.harddealer.math3.exception.NotStrictlyPositiveException;
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21 | import agents.anac.y2019.harddealer.math3.exception.NumberIsTooLargeException;
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22 | import agents.anac.y2019.harddealer.math3.exception.OutOfRangeException;
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23 | import agents.anac.y2019.harddealer.math3.exception.util.LocalizedFormats;
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24 |
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25 | /**
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26 | * Factory methods to generate confidence intervals for a binomial proportion.
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27 | * The supported methods are:
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28 | * <ul>
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29 | * <li>Agresti-Coull interval</li>
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30 | * <li>Clopper-Pearson method (exact method)</li>
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31 | * <li>Normal approximation (based on central limit theorem)</li>
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32 | * <li>Wilson score interval</li>
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33 | * </ul>
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34 | *
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35 | * @since 3.3
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36 | */
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37 | public final class IntervalUtils {
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38 |
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39 | /** Singleton Agresti-Coull instance. */
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40 | private static final BinomialConfidenceInterval AGRESTI_COULL = new AgrestiCoullInterval();
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41 |
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42 | /** Singleton Clopper-Pearson instance. */
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43 | private static final BinomialConfidenceInterval CLOPPER_PEARSON = new ClopperPearsonInterval();
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44 |
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45 | /** Singleton NormalApproximation instance. */
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46 | private static final BinomialConfidenceInterval NORMAL_APPROXIMATION = new NormalApproximationInterval();
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47 |
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48 | /** Singleton Wilson score instance. */
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49 | private static final BinomialConfidenceInterval WILSON_SCORE = new WilsonScoreInterval();
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50 |
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51 | /**
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52 | * Prevent instantiation.
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53 | */
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54 | private IntervalUtils() {
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55 | }
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56 |
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57 | /**
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58 | * Create an Agresti-Coull binomial confidence interval for the true
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59 | * probability of success of an unknown binomial distribution with the given
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60 | * observed number of trials, successes and confidence level.
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61 | *
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62 | * @param numberOfTrials number of trials
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63 | * @param numberOfSuccesses number of successes
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64 | * @param confidenceLevel desired probability that the true probability of
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65 | * success falls within the returned interval
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66 | * @return Confidence interval containing the probability of success with
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67 | * probability {@code confidenceLevel}
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68 | * @throws NotStrictlyPositiveException if {@code numberOfTrials <= 0}.
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69 | * @throws NotPositiveException if {@code numberOfSuccesses < 0}.
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70 | * @throws NumberIsTooLargeException if {@code numberOfSuccesses > numberOfTrials}.
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71 | * @throws OutOfRangeException if {@code confidenceLevel} is not in the interval {@code (0, 1)}.
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72 | */
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73 | public static ConfidenceInterval getAgrestiCoullInterval(int numberOfTrials, int numberOfSuccesses,
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74 | double confidenceLevel) {
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75 | return AGRESTI_COULL.createInterval(numberOfTrials, numberOfSuccesses, confidenceLevel);
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76 | }
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77 |
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78 | /**
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79 | * Create a Clopper-Pearson binomial confidence interval for the true
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80 | * probability of success of an unknown binomial distribution with the given
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81 | * observed number of trials, successes and confidence level.
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82 | * <p>
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83 | * Preconditions:
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84 | * <ul>
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85 | * <li>{@code numberOfTrials} must be positive</li>
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86 | * <li>{@code numberOfSuccesses} may not exceed {@code numberOfTrials}</li>
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87 | * <li>{@code confidenceLevel} must be strictly between 0 and 1 (exclusive)</li>
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88 | * </ul>
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89 | * </p>
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90 | *
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91 | * @param numberOfTrials number of trials
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92 | * @param numberOfSuccesses number of successes
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93 | * @param confidenceLevel desired probability that the true probability of
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94 | * success falls within the returned interval
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95 | * @return Confidence interval containing the probability of success with
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96 | * probability {@code confidenceLevel}
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97 | * @throws NotStrictlyPositiveException if {@code numberOfTrials <= 0}.
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98 | * @throws NotPositiveException if {@code numberOfSuccesses < 0}.
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99 | * @throws NumberIsTooLargeException if {@code numberOfSuccesses > numberOfTrials}.
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100 | * @throws OutOfRangeException if {@code confidenceLevel} is not in the interval {@code (0, 1)}.
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101 | */
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102 | public static ConfidenceInterval getClopperPearsonInterval(int numberOfTrials, int numberOfSuccesses,
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103 | double confidenceLevel) {
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104 | return CLOPPER_PEARSON.createInterval(numberOfTrials, numberOfSuccesses, confidenceLevel);
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105 | }
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106 |
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107 | /**
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108 | * Create a binomial confidence interval for the true probability of success
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109 | * of an unknown binomial distribution with the given observed number of
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110 | * trials, successes and confidence level using the Normal approximation to
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111 | * the binomial distribution.
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112 | *
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113 | * @param numberOfTrials number of trials
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114 | * @param numberOfSuccesses number of successes
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115 | * @param confidenceLevel desired probability that the true probability of
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116 | * success falls within the interval
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117 | * @return Confidence interval containing the probability of success with
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118 | * probability {@code confidenceLevel}
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119 | */
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120 | public static ConfidenceInterval getNormalApproximationInterval(int numberOfTrials, int numberOfSuccesses,
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121 | double confidenceLevel) {
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122 | return NORMAL_APPROXIMATION.createInterval(numberOfTrials, numberOfSuccesses, confidenceLevel);
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123 | }
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124 |
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125 | /**
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126 | * Create a Wilson score binomial confidence interval for the true
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127 | * probability of success of an unknown binomial distribution with the given
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128 | * observed number of trials, successes and confidence level.
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129 | *
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130 | * @param numberOfTrials number of trials
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131 | * @param numberOfSuccesses number of successes
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132 | * @param confidenceLevel desired probability that the true probability of
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133 | * success falls within the returned interval
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134 | * @return Confidence interval containing the probability of success with
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135 | * probability {@code confidenceLevel}
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136 | * @throws NotStrictlyPositiveException if {@code numberOfTrials <= 0}.
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137 | * @throws NotPositiveException if {@code numberOfSuccesses < 0}.
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138 | * @throws NumberIsTooLargeException if {@code numberOfSuccesses > numberOfTrials}.
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139 | * @throws OutOfRangeException if {@code confidenceLevel} is not in the interval {@code (0, 1)}.
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140 | */
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141 | public static ConfidenceInterval getWilsonScoreInterval(int numberOfTrials, int numberOfSuccesses,
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142 | double confidenceLevel) {
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143 | return WILSON_SCORE.createInterval(numberOfTrials, numberOfSuccesses, confidenceLevel);
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144 | }
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145 |
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146 | /**
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147 | * Verifies that parameters satisfy preconditions.
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148 | *
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149 | * @param numberOfTrials number of trials (must be positive)
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150 | * @param numberOfSuccesses number of successes (must not exceed numberOfTrials)
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151 | * @param confidenceLevel confidence level (must be strictly between 0 and 1)
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152 | * @throws NotStrictlyPositiveException if {@code numberOfTrials <= 0}.
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153 | * @throws NotPositiveException if {@code numberOfSuccesses < 0}.
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154 | * @throws NumberIsTooLargeException if {@code numberOfSuccesses > numberOfTrials}.
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155 | * @throws OutOfRangeException if {@code confidenceLevel} is not in the interval {@code (0, 1)}.
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156 | */
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157 | static void checkParameters(int numberOfTrials, int numberOfSuccesses, double confidenceLevel) {
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158 | if (numberOfTrials <= 0) {
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159 | throw new NotStrictlyPositiveException(LocalizedFormats.NUMBER_OF_TRIALS, numberOfTrials);
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160 | }
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161 | if (numberOfSuccesses < 0) {
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162 | throw new NotPositiveException(LocalizedFormats.NEGATIVE_NUMBER_OF_SUCCESSES, numberOfSuccesses);
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163 | }
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164 | if (numberOfSuccesses > numberOfTrials) {
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165 | throw new NumberIsTooLargeException(LocalizedFormats.NUMBER_OF_SUCCESS_LARGER_THAN_POPULATION_SIZE,
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166 | numberOfSuccesses, numberOfTrials, true);
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167 | }
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168 | if (confidenceLevel <= 0 || confidenceLevel >= 1) {
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169 | throw new OutOfRangeException(LocalizedFormats.OUT_OF_BOUNDS_CONFIDENCE_LEVEL,
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170 | confidenceLevel, 0, 1);
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171 | }
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172 | }
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173 |
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174 | }
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