1672 lines
56 KiB
C#
1672 lines
56 KiB
C#
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//-------------------------------------------------------------
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// <copyright company=<3D>Microsoft Corporation<6F>>
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// Copyright <20> Microsoft Corporation. All Rights Reserved.
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// </copyright>
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//-------------------------------------------------------------
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// @owner=alexgor, deliant
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//=================================================================
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// File: StatisticFormula.cs
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//
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// Namespace: DataVisualization.Charting
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//
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// Classes: StatisticFormula, TTestResult, FTestResult, AnovaResult,
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// ZTestResult
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//
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// Purpose: StatisticFormula class provides helper methods for statistical
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// calculations like TTest, FTest, Anova, ZTest and others.
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// Actual calculations are made in the DataFormula class and
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// the StatisticFormula class mange formula parameters, input and
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// output series.
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//
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// TTestResult, FTestResult, AnovaResult and ZTestResult
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// classes are used to store the results of the calculatiions.
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//
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// StatisticFormula class is exposed to the user through
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// DataManipulator.StatisticFormula property. Here is an example of
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// using the Anova test:
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//
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// AnovaResult result = Chart1.DataManipulator.StatisticFormula.Anova(0.6, "Group1,Group2,Group3");
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//
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// NOTE: First versions of the chart use single method to execute
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// ALL formulas. Formula name and parameters were passed as
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// strings. Input and outpat data was passed through data
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// series.
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//
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// This approach was hard to use by the end-user and was
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// changed to a specific method for each formula. StatisticFormula
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// class provides that simplified interface for all statistics
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// formulas. Internally it still uses the DataFormula.Formula
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// method with string parameters.
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//
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// Reviewed: AG - April 1, 2003
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// AG - Microsoft 14, 2007
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//
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//===================================================================
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using System;
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using System.Diagnostics.CodeAnalysis;
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#if Microsoft_CONTROL
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namespace System.Windows.Forms.DataVisualization.Charting
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#else
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namespace System.Web.UI.DataVisualization.Charting
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#endif
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{
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/// <summary>
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/// The StatisticFormula class provides helper methods for statistical calculations.
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/// Actual calculations are made in the DataFormula class and the StatisticFormula
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/// class provide a simplified API which automatically prepares parameters and
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/// deals with input and output series.
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/// </summary>
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#if ASPPERM_35
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[AspNetHostingPermission(System.Security.Permissions.SecurityAction.InheritanceDemand, Level = AspNetHostingPermissionLevel.Minimal)]
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[AspNetHostingPermission(System.Security.Permissions.SecurityAction.LinkDemand, Level = AspNetHostingPermissionLevel.Minimal)]
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#endif
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public class StatisticFormula
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{
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#region Fields
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// Name used for temporary data series
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private string _tempOutputSeriesName = "Statistical Analyses Formula Temporary Output Series 2552003";
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// Reference to the class which describes calculation settings and
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// provides access to chart common elements.
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private DataFormula _formulaData = null;
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#endregion // Fields
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#region Constructor
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/// <summary>
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/// StatisticFormula Constructor
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/// </summary>
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/// <param name="formulaData">Formula Data</param>
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internal StatisticFormula( DataFormula formulaData )
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{
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this._formulaData = formulaData;
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}
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#endregion // Constructor
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#region Tests
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/// <summary>
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/// This formula performs a Z Test using Normal distribution.
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/// </summary>
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/// <param name="hypothesizedMeanDifference">Hypothesized mean difference.</param>
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/// <param name="varianceFirstGroup">Variance first group.</param>
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/// <param name="varianceSecondGroup">Variance second group.</param>
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/// <param name="probability">Probability.</param>
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/// <param name="firstInputSeriesName">First input series name.</param>
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/// <param name="secondInputSeriesName">Second input series name.</param>
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/// <returns>ZTestResult object.</returns>
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public ZTestResult ZTest(
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double hypothesizedMeanDifference,
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double varianceFirstGroup,
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double varianceSecondGroup,
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double probability,
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string firstInputSeriesName,
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string secondInputSeriesName )
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{
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// Check arguments
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if (firstInputSeriesName == null)
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throw new ArgumentNullException("firstInputSeriesName");
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if (secondInputSeriesName == null)
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throw new ArgumentNullException("secondInputSeriesName");
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// Create output class
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ZTestResult zTestResult = new ZTestResult();
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// Make string with parameters
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string parameter = hypothesizedMeanDifference.ToString(System.Globalization.CultureInfo.InvariantCulture);
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parameter += "," + varianceFirstGroup.ToString(System.Globalization.CultureInfo.InvariantCulture);
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parameter += "," + varianceSecondGroup.ToString(System.Globalization.CultureInfo.InvariantCulture);
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parameter += "," + probability.ToString(System.Globalization.CultureInfo.InvariantCulture);
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// Create temporary output series.
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_formulaData.Common.DataManager.Series.Add( new Series(_tempOutputSeriesName) );
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// Set input series string
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string inputSeriesParameter = firstInputSeriesName.ToString(System.Globalization.CultureInfo.InvariantCulture) + "," + secondInputSeriesName.ToString(System.Globalization.CultureInfo.InvariantCulture);
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// Execute formula
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try
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{
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_formulaData.Formula("ZTest", parameter, inputSeriesParameter, _tempOutputSeriesName);
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DataPointCollection points = _formulaData.Common.DataManager.Series[_tempOutputSeriesName].Points;
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// Fill Output class
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zTestResult.firstSeriesMean = points[0].YValues[0];
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zTestResult.secondSeriesMean = points[1].YValues[0];
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zTestResult.firstSeriesVariance = points[2].YValues[0];
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zTestResult.secondSeriesVariance = points[3].YValues[0];
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zTestResult.zValue = points[4].YValues[0];
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zTestResult.probabilityZOneTail = points[5].YValues[0];
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zTestResult.zCriticalValueOneTail = points[6].YValues[0];
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zTestResult.probabilityZTwoTail = points[7].YValues[0];
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zTestResult.zCriticalValueTwoTail = points[8].YValues[0];
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}
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finally
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{
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// Remove Temporary output series
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_formulaData.Common.DataManager.Series.Remove(_formulaData.Common.DataManager.Series[_tempOutputSeriesName]);
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}
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// Return result class
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return zTestResult;
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}
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/// <summary>
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/// Perform a T Test using Students distribution (T distribution) with unequal variances.
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/// </summary>
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/// <param name="hypothesizedMeanDifference">Hypothesized mean difference.</param>
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/// <param name="probability">Probability.</param>
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/// <param name="firstInputSeriesName">First input series name.</param>
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/// <param name="secondInputSeriesName">Second input series name.</param>
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/// <returns>TTestResult object.</returns>
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public TTestResult TTestUnequalVariances(
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double hypothesizedMeanDifference,
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double probability,
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string firstInputSeriesName,
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string secondInputSeriesName )
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{
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// Check arguments
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if (firstInputSeriesName == null)
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throw new ArgumentNullException("firstInputSeriesName");
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if (secondInputSeriesName == null)
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throw new ArgumentNullException("secondInputSeriesName");
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// Create output class
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TTestResult tTestResult = new TTestResult();
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// Make string with parameters
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string parameter = hypothesizedMeanDifference.ToString(System.Globalization.CultureInfo.InvariantCulture);
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parameter += "," + probability.ToString(System.Globalization.CultureInfo.InvariantCulture);
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// Create temporary output series.
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_formulaData.Common.DataManager.Series.Add( new Series(_tempOutputSeriesName) );
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// Set input series string
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try
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{
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string inputSeriesParameter = firstInputSeriesName.ToString(System.Globalization.CultureInfo.InvariantCulture) + "," + secondInputSeriesName.ToString(System.Globalization.CultureInfo.InvariantCulture);
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// Execute formula
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_formulaData.Formula("TTestUnequalVariances", parameter, inputSeriesParameter, _tempOutputSeriesName);
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DataPointCollection points = _formulaData.Common.DataManager.Series[_tempOutputSeriesName].Points;
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// Fill Output class
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tTestResult.firstSeriesMean = points[0].YValues[0];
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tTestResult.secondSeriesMean = points[1].YValues[0];
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tTestResult.firstSeriesVariance = points[2].YValues[0];
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tTestResult.secondSeriesVariance = points[3].YValues[0];
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tTestResult.tValue = points[4].YValues[0];
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tTestResult.degreeOfFreedom = points[5].YValues[0];
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tTestResult.probabilityTOneTail = points[6].YValues[0];
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tTestResult.tCriticalValueOneTail = points[7].YValues[0];
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tTestResult.probabilityTTwoTail = points[8].YValues[0];
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tTestResult.tCriticalValueTwoTail = points[9].YValues[0];
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}
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finally
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{
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// Remove Temporary output series
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_formulaData.Common.DataManager.Series.Remove(_formulaData.Common.DataManager.Series[_tempOutputSeriesName]);
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}
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// Return result class
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return tTestResult;
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}
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/// <summary>
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/// Perform a T Test using Students distribution (T distribution) with equal variances.
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/// </summary>
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/// <param name="hypothesizedMeanDifference">Hypothesized mean difference.</param>
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/// <param name="probability">Probability.</param>
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/// <param name="firstInputSeriesName">First input series name.</param>
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/// <param name="secondInputSeriesName">Second input series name.</param>
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/// <returns>TTestResult object.</returns>
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public TTestResult TTestEqualVariances(
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double hypothesizedMeanDifference,
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double probability,
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string firstInputSeriesName,
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string secondInputSeriesName )
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{
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// Check arguments
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if (firstInputSeriesName == null)
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throw new ArgumentNullException("firstInputSeriesName");
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if (secondInputSeriesName == null)
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throw new ArgumentNullException("secondInputSeriesName");
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// Create output class
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TTestResult tTestResult = new TTestResult();
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// Make string with parameters
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string parameter = hypothesizedMeanDifference.ToString(System.Globalization.CultureInfo.InvariantCulture);
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parameter += "," + probability.ToString(System.Globalization.CultureInfo.InvariantCulture);
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// Create temporary output series.
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_formulaData.Common.DataManager.Series.Add( new Series(_tempOutputSeriesName) );
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// Set input series string
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string inputSeriesParameter = firstInputSeriesName.ToString(System.Globalization.CultureInfo.InvariantCulture) + "," + secondInputSeriesName.ToString(System.Globalization.CultureInfo.InvariantCulture);
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// Execute formula
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try
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{
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_formulaData.Formula("TTestEqualVariances", parameter, inputSeriesParameter, _tempOutputSeriesName);
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DataPointCollection points = _formulaData.Common.DataManager.Series[_tempOutputSeriesName].Points;
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// Fill Output class
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tTestResult.firstSeriesMean = points[0].YValues[0];
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tTestResult.secondSeriesMean = points[1].YValues[0];
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tTestResult.firstSeriesVariance = points[2].YValues[0];
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tTestResult.secondSeriesVariance = points[3].YValues[0];
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tTestResult.tValue = points[4].YValues[0];
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tTestResult.degreeOfFreedom = points[5].YValues[0];
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tTestResult.probabilityTOneTail = points[6].YValues[0];
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tTestResult.tCriticalValueOneTail = points[7].YValues[0];
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tTestResult.probabilityTTwoTail = points[8].YValues[0];
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tTestResult.tCriticalValueTwoTail = points[9].YValues[0];
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}
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finally
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{
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// Remove Temporary output series
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_formulaData.Common.DataManager.Series.Remove(_formulaData.Common.DataManager.Series[_tempOutputSeriesName]);
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}
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// Return result class
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return tTestResult;
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}
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/// <summary>
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/// Performs a T Test using Students distribution (T distribution) with paired samples.
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/// This is useful when there is a natural pairing of observations in samples.
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/// </summary>
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/// <param name="hypothesizedMeanDifference">Hypothesized mean difference.</param>
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/// <param name="probability">Probability.</param>
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/// <param name="firstInputSeriesName">First input series name.</param>
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/// <param name="secondInputSeriesName">Second input series name.</param>
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/// <returns>TTestResult object.</returns>
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public TTestResult TTestPaired(
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double hypothesizedMeanDifference,
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double probability,
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string firstInputSeriesName,
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string secondInputSeriesName )
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{
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// Check arguments
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if (firstInputSeriesName == null)
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throw new ArgumentNullException("firstInputSeriesName");
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if (secondInputSeriesName == null)
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throw new ArgumentNullException("secondInputSeriesName");
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// Create output class
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TTestResult tTestResult = new TTestResult();
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// Make string with parameters
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string parameter = hypothesizedMeanDifference.ToString(System.Globalization.CultureInfo.InvariantCulture);
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parameter += "," + probability.ToString(System.Globalization.CultureInfo.InvariantCulture);
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// Create temporary output series.
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_formulaData.Common.DataManager.Series.Add( new Series(_tempOutputSeriesName) );
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// Set input series string
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string inputSeriesParameter = firstInputSeriesName.ToString(System.Globalization.CultureInfo.InvariantCulture) + "," + secondInputSeriesName.ToString(System.Globalization.CultureInfo.InvariantCulture);
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// Execute formula
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try
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{
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_formulaData.Formula("TTestPaired", parameter, inputSeriesParameter, _tempOutputSeriesName);
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DataPointCollection points = _formulaData.Common.DataManager.Series[_tempOutputSeriesName].Points;
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// Fill Output class
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tTestResult.firstSeriesMean = points[0].YValues[0];
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tTestResult.secondSeriesMean = points[1].YValues[0];
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tTestResult.firstSeriesVariance = points[2].YValues[0];
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tTestResult.secondSeriesVariance = points[3].YValues[0];
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tTestResult.tValue = points[4].YValues[0];
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tTestResult.degreeOfFreedom = points[5].YValues[0];
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tTestResult.probabilityTOneTail = points[6].YValues[0];
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tTestResult.tCriticalValueOneTail = points[7].YValues[0];
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tTestResult.probabilityTTwoTail = points[8].YValues[0];
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tTestResult.tCriticalValueTwoTail = points[9].YValues[0];
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}
|
|||
|
|
finally
|
|||
|
|
{
|
|||
|
|
// Remove Temporary output series
|
|||
|
|
_formulaData.Common.DataManager.Series.Remove(_formulaData.Common.DataManager.Series[_tempOutputSeriesName]);
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
// Return result class
|
|||
|
|
return tTestResult;
|
|||
|
|
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Removes empty points from series.
|
|||
|
|
/// </summary>
|
|||
|
|
/// <param name="seriesName">series name</param>
|
|||
|
|
private void RemoveEmptyPoints(string seriesName)
|
|||
|
|
{
|
|||
|
|
Series series = _formulaData.Common.DataManager.Series[seriesName];
|
|||
|
|
for (int pointIndex = 0; pointIndex < series.Points.Count; pointIndex++)
|
|||
|
|
{
|
|||
|
|
if (series.Points[pointIndex].IsEmpty)
|
|||
|
|
{
|
|||
|
|
series.Points.RemoveAt(pointIndex--);
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// This formula performs a two-sample F Test using the F distribution, and is used to see if the samples have different variances.
|
|||
|
|
/// </summary>
|
|||
|
|
/// <param name="probability">Probability.</param>
|
|||
|
|
/// <param name="firstInputSeriesName">First input series name.</param>
|
|||
|
|
/// <param name="secondInputSeriesName">Second input series name.</param>
|
|||
|
|
/// <returns>FTestResult object.</returns>
|
|||
|
|
public FTestResult FTest(
|
|||
|
|
double probability,
|
|||
|
|
string firstInputSeriesName,
|
|||
|
|
string secondInputSeriesName )
|
|||
|
|
{
|
|||
|
|
// Check arguments
|
|||
|
|
if (firstInputSeriesName == null)
|
|||
|
|
throw new ArgumentNullException("firstInputSeriesName");
|
|||
|
|
if (secondInputSeriesName == null)
|
|||
|
|
throw new ArgumentNullException("secondInputSeriesName");
|
|||
|
|
|
|||
|
|
// Create output class
|
|||
|
|
FTestResult fTestResult = new FTestResult();
|
|||
|
|
|
|||
|
|
// Make string with parameters
|
|||
|
|
string parameter = probability.ToString(System.Globalization.CultureInfo.InvariantCulture);
|
|||
|
|
|
|||
|
|
// Set input series string
|
|||
|
|
string inputSeriesParameter = firstInputSeriesName.ToString(System.Globalization.CultureInfo.InvariantCulture) + "," + secondInputSeriesName.ToString(System.Globalization.CultureInfo.InvariantCulture);
|
|||
|
|
|
|||
|
|
// Create temporary output series.
|
|||
|
|
_formulaData.Common.DataManager.Series.Add( new Series(_tempOutputSeriesName) );
|
|||
|
|
|
|||
|
|
|
|||
|
|
// remove empty points from the collection.
|
|||
|
|
RemoveEmptyPoints(firstInputSeriesName);
|
|||
|
|
RemoveEmptyPoints(secondInputSeriesName);
|
|||
|
|
|
|||
|
|
// Execute formula
|
|||
|
|
try
|
|||
|
|
{
|
|||
|
|
_formulaData.Formula("FTest", parameter, inputSeriesParameter, _tempOutputSeriesName);
|
|||
|
|
|
|||
|
|
DataPointCollection points = _formulaData.Common.DataManager.Series[_tempOutputSeriesName].Points;
|
|||
|
|
|
|||
|
|
// Fill Output class
|
|||
|
|
fTestResult.firstSeriesMean = points[0].YValues[0];
|
|||
|
|
fTestResult.secondSeriesMean = points[1].YValues[0];
|
|||
|
|
fTestResult.firstSeriesVariance = points[2].YValues[0];
|
|||
|
|
fTestResult.secondSeriesVariance = points[3].YValues[0];
|
|||
|
|
fTestResult.fValue = points[4].YValues[0];
|
|||
|
|
fTestResult.probabilityFOneTail = points[5].YValues[0];
|
|||
|
|
fTestResult.fCriticalValueOneTail = points[6].YValues[0];
|
|||
|
|
}
|
|||
|
|
finally
|
|||
|
|
{
|
|||
|
|
// Remove Temporary output series
|
|||
|
|
_formulaData.Common.DataManager.Series.Remove(_formulaData.Common.DataManager.Series[_tempOutputSeriesName]);
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
// Return result class
|
|||
|
|
return fTestResult;
|
|||
|
|
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// An Anova test is used to determine the existence, or absence of a statistically
|
|||
|
|
/// significant difference between the mean values of two or more groups of data.
|
|||
|
|
/// </summary>
|
|||
|
|
/// <param name="probability">Probability.</param>
|
|||
|
|
/// <param name="inputSeriesNames">Comma-delimited list of input series names.</param>
|
|||
|
|
/// <returns>AnovaResult object.</returns>
|
|||
|
|
public AnovaResult Anova(
|
|||
|
|
double probability,
|
|||
|
|
string inputSeriesNames)
|
|||
|
|
{
|
|||
|
|
// Check arguments
|
|||
|
|
if (inputSeriesNames == null)
|
|||
|
|
throw new ArgumentNullException("inputSeriesNames");
|
|||
|
|
|
|||
|
|
// Create output class
|
|||
|
|
AnovaResult anovaResult = new AnovaResult();
|
|||
|
|
|
|||
|
|
// Make string with parameters
|
|||
|
|
string parameter = probability.ToString(System.Globalization.CultureInfo.InvariantCulture);
|
|||
|
|
|
|||
|
|
// Create temporary output series.
|
|||
|
|
_formulaData.Common.DataManager.Series.Add( new Series(_tempOutputSeriesName) );
|
|||
|
|
|
|||
|
|
// Execute formula
|
|||
|
|
try
|
|||
|
|
{
|
|||
|
|
_formulaData.Formula("Anova", parameter, inputSeriesNames, _tempOutputSeriesName);
|
|||
|
|
|
|||
|
|
DataPointCollection points = _formulaData.Common.DataManager.Series[_tempOutputSeriesName].Points;
|
|||
|
|
|
|||
|
|
// Fill Output class
|
|||
|
|
anovaResult.sumOfSquaresBetweenGroups = points[0].YValues[0];
|
|||
|
|
anovaResult.sumOfSquaresWithinGroups = points[1].YValues[0];
|
|||
|
|
anovaResult.sumOfSquaresTotal = points[2].YValues[0];
|
|||
|
|
anovaResult.degreeOfFreedomBetweenGroups = points[3].YValues[0];
|
|||
|
|
anovaResult.degreeOfFreedomWithinGroups = points[4].YValues[0];
|
|||
|
|
anovaResult.degreeOfFreedomTotal = points[5].YValues[0];
|
|||
|
|
anovaResult.meanSquareVarianceBetweenGroups = points[6].YValues[0];
|
|||
|
|
anovaResult.meanSquareVarianceWithinGroups = points[7].YValues[0];
|
|||
|
|
anovaResult.fRatio = points[8].YValues[0];
|
|||
|
|
anovaResult.fCriticalValue = points[9].YValues[0];
|
|||
|
|
}
|
|||
|
|
finally
|
|||
|
|
{
|
|||
|
|
// Remove Temporary output series
|
|||
|
|
_formulaData.Common.DataManager.Series.Remove(_formulaData.Common.DataManager.Series[_tempOutputSeriesName]);
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
// Return result class
|
|||
|
|
return anovaResult;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
#endregion // Test
|
|||
|
|
|
|||
|
|
#region Distributions
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// This method returns the probability for the standard normal cumulative distribution function.
|
|||
|
|
/// </summary>
|
|||
|
|
/// <param name="zValue">The Z value for which the probability is required.</param>
|
|||
|
|
/// <returns>Returns value from the standard normal cumulative distribution function.</returns>
|
|||
|
|
[SuppressMessage("Microsoft.Naming", "CA1704:IdentifiersShouldBeSpelledCorrectly",
|
|||
|
|
Justification = "Z is a cartesian coordinate and well understood")]
|
|||
|
|
public double NormalDistribution(double zValue)
|
|||
|
|
{
|
|||
|
|
// Make string with parameters
|
|||
|
|
string parameter = zValue.ToString(System.Globalization.CultureInfo.InvariantCulture);
|
|||
|
|
|
|||
|
|
// Create temporary output series.
|
|||
|
|
_formulaData.Common.DataManager.Series.Add( new Series(_tempOutputSeriesName) );
|
|||
|
|
|
|||
|
|
// Execute formula
|
|||
|
|
double result = double.NaN;
|
|||
|
|
try
|
|||
|
|
{
|
|||
|
|
_formulaData.Formula("NormalDistribution", parameter, _tempOutputSeriesName, _tempOutputSeriesName);
|
|||
|
|
|
|||
|
|
DataPointCollection points = _formulaData.Common.DataManager.Series[_tempOutputSeriesName].Points;
|
|||
|
|
|
|||
|
|
// Fill Output class
|
|||
|
|
result = points[0].YValues[0];
|
|||
|
|
}
|
|||
|
|
finally
|
|||
|
|
{
|
|||
|
|
// Remove Temporary output series
|
|||
|
|
_formulaData.Common.DataManager.Series.Remove(_formulaData.Common.DataManager.Series[_tempOutputSeriesName]);
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
// Return result class
|
|||
|
|
return result;
|
|||
|
|
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// This method returns the inverse of the standard normal cumulative distribution.
|
|||
|
|
/// </summary>
|
|||
|
|
/// <param name="probability">Probability.</param>
|
|||
|
|
/// <returns>Returns value from the inverse standard normal cumulative distribution function.</returns>
|
|||
|
|
public double InverseNormalDistribution( double probability )
|
|||
|
|
{
|
|||
|
|
|
|||
|
|
// Make string with parameters
|
|||
|
|
string parameter = probability.ToString(System.Globalization.CultureInfo.InvariantCulture);
|
|||
|
|
|
|||
|
|
// Create temporary output series.
|
|||
|
|
_formulaData.Common.DataManager.Series.Add( new Series(_tempOutputSeriesName) );
|
|||
|
|
|
|||
|
|
// Execute formula
|
|||
|
|
double result = double.NaN;
|
|||
|
|
try
|
|||
|
|
{
|
|||
|
|
_formulaData.Formula("InverseNormalDistribution", parameter, _tempOutputSeriesName, _tempOutputSeriesName);
|
|||
|
|
|
|||
|
|
DataPointCollection points = _formulaData.Common.DataManager.Series[_tempOutputSeriesName].Points;
|
|||
|
|
|
|||
|
|
// Fill Output class
|
|||
|
|
result = points[0].YValues[0];
|
|||
|
|
}
|
|||
|
|
finally
|
|||
|
|
{
|
|||
|
|
// Remove Temporary output series
|
|||
|
|
_formulaData.Common.DataManager.Series.Remove(_formulaData.Common.DataManager.Series[_tempOutputSeriesName]);
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
// Return result class
|
|||
|
|
return result;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// This method returns the cumulative F distribution function probability.
|
|||
|
|
/// </summary>
|
|||
|
|
/// <param name="value">F Value.</param>
|
|||
|
|
/// <param name="firstDegreeOfFreedom">First degree of freedom.</param>
|
|||
|
|
/// <param name="secondDegreeOfFreedom">Second degree of freedom.</param>
|
|||
|
|
/// <returns>Returns value from the cumulative F distribution function.</returns>
|
|||
|
|
public double FDistribution(
|
|||
|
|
double value,
|
|||
|
|
int firstDegreeOfFreedom,
|
|||
|
|
int secondDegreeOfFreedom )
|
|||
|
|
{
|
|||
|
|
|
|||
|
|
// Make string with parameters
|
|||
|
|
string parameter = value.ToString(System.Globalization.CultureInfo.InvariantCulture);
|
|||
|
|
parameter += "," + firstDegreeOfFreedom.ToString(System.Globalization.CultureInfo.InvariantCulture);
|
|||
|
|
parameter += "," + secondDegreeOfFreedom.ToString(System.Globalization.CultureInfo.InvariantCulture);
|
|||
|
|
|
|||
|
|
// Create temporary output series.
|
|||
|
|
_formulaData.Common.DataManager.Series.Add( new Series(_tempOutputSeriesName) );
|
|||
|
|
|
|||
|
|
// Execute formula
|
|||
|
|
double result = double.NaN;
|
|||
|
|
try
|
|||
|
|
{
|
|||
|
|
_formulaData.Formula("FDistribution", parameter, _tempOutputSeriesName, _tempOutputSeriesName);
|
|||
|
|
|
|||
|
|
DataPointCollection points = _formulaData.Common.DataManager.Series[_tempOutputSeriesName].Points;
|
|||
|
|
|
|||
|
|
// Fill Output class
|
|||
|
|
result = points[0].YValues[0];
|
|||
|
|
}
|
|||
|
|
finally
|
|||
|
|
{
|
|||
|
|
// Remove Temporary output series
|
|||
|
|
_formulaData.Common.DataManager.Series.Remove(_formulaData.Common.DataManager.Series[_tempOutputSeriesName]);
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
// Return result class
|
|||
|
|
return result;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Returns the inverse of the F cumulative distribution.
|
|||
|
|
/// </summary>
|
|||
|
|
/// <param name="probability">Probability.</param>
|
|||
|
|
/// <param name="firstDegreeOfFreedom">First degree of freedom.</param>
|
|||
|
|
/// <param name="secondDegreeOfFreedom">Second degree of freedom.</param>
|
|||
|
|
/// <returns>Returns value from the inverse F distribution function.</returns>
|
|||
|
|
public double InverseFDistribution(
|
|||
|
|
double probability,
|
|||
|
|
int firstDegreeOfFreedom,
|
|||
|
|
int secondDegreeOfFreedom )
|
|||
|
|
{
|
|||
|
|
|
|||
|
|
// Make string with parameters
|
|||
|
|
string parameter = probability.ToString(System.Globalization.CultureInfo.InvariantCulture);
|
|||
|
|
parameter += "," + firstDegreeOfFreedom.ToString(System.Globalization.CultureInfo.InvariantCulture);
|
|||
|
|
parameter += "," + secondDegreeOfFreedom.ToString(System.Globalization.CultureInfo.InvariantCulture);
|
|||
|
|
|
|||
|
|
// Create temporary output series.
|
|||
|
|
_formulaData.Common.DataManager.Series.Add( new Series(_tempOutputSeriesName) );
|
|||
|
|
|
|||
|
|
// Execute formula
|
|||
|
|
double result = double.NaN;
|
|||
|
|
try
|
|||
|
|
{
|
|||
|
|
_formulaData.Formula("InverseFDistribution", parameter, _tempOutputSeriesName, _tempOutputSeriesName);
|
|||
|
|
|
|||
|
|
DataPointCollection points = _formulaData.Common.DataManager.Series[_tempOutputSeriesName].Points;
|
|||
|
|
|
|||
|
|
// Fill Output class
|
|||
|
|
result = points[0].YValues[0];
|
|||
|
|
}
|
|||
|
|
finally
|
|||
|
|
{
|
|||
|
|
// Remove Temporary output series
|
|||
|
|
_formulaData.Common.DataManager.Series.Remove(_formulaData.Common.DataManager.Series[_tempOutputSeriesName]);
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
// Return result class
|
|||
|
|
return result;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Returns the probability for the T distribution (student's distribution).
|
|||
|
|
/// </summary>
|
|||
|
|
/// <param name="value">T value</param>
|
|||
|
|
/// <param name="degreeOfFreedom">Degree of freedom</param>
|
|||
|
|
/// <param name="oneTail">If true, one-tailed distribution is used; otherwise two-tailed distribution is used.</param>
|
|||
|
|
/// <returns>Returns T Distribution cumulative function</returns>
|
|||
|
|
public double TDistribution(
|
|||
|
|
double value,
|
|||
|
|
int degreeOfFreedom,
|
|||
|
|
bool oneTail )
|
|||
|
|
{
|
|||
|
|
|
|||
|
|
// Make string with parameters
|
|||
|
|
string parameter = value.ToString(System.Globalization.CultureInfo.InvariantCulture);
|
|||
|
|
parameter += "," + degreeOfFreedom.ToString(System.Globalization.CultureInfo.InvariantCulture);
|
|||
|
|
if( oneTail )
|
|||
|
|
{
|
|||
|
|
parameter += ",1";
|
|||
|
|
}
|
|||
|
|
else
|
|||
|
|
{
|
|||
|
|
parameter += ",2";
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
|
|||
|
|
// Create temporary output series.
|
|||
|
|
_formulaData.Common.DataManager.Series.Add( new Series(_tempOutputSeriesName) );
|
|||
|
|
|
|||
|
|
// Execute formula
|
|||
|
|
double result = double.NaN;
|
|||
|
|
try
|
|||
|
|
{
|
|||
|
|
_formulaData.Formula("TDistribution", parameter, _tempOutputSeriesName, _tempOutputSeriesName);
|
|||
|
|
|
|||
|
|
DataPointCollection points = _formulaData.Common.DataManager.Series[_tempOutputSeriesName].Points;
|
|||
|
|
|
|||
|
|
// Fill Output class
|
|||
|
|
result = points[0].YValues[0];
|
|||
|
|
}
|
|||
|
|
finally
|
|||
|
|
{
|
|||
|
|
// Remove Temporary output series
|
|||
|
|
_formulaData.Common.DataManager.Series.Remove(_formulaData.Common.DataManager.Series[_tempOutputSeriesName]);
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
// Return result class
|
|||
|
|
return result;
|
|||
|
|
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Returns the T-value of the T distribution as a function of probability and degrees of freedom.
|
|||
|
|
/// </summary>
|
|||
|
|
/// <param name="probability">Probability.</param>
|
|||
|
|
/// <param name="degreeOfFreedom">Degree of freedom.</param>
|
|||
|
|
/// <returns>Returns Inverse T distribution.</returns>
|
|||
|
|
public double InverseTDistribution(
|
|||
|
|
double probability,
|
|||
|
|
int degreeOfFreedom )
|
|||
|
|
{
|
|||
|
|
|
|||
|
|
// Make string with parameters
|
|||
|
|
string parameter = probability.ToString(System.Globalization.CultureInfo.InvariantCulture);
|
|||
|
|
parameter += "," + degreeOfFreedom.ToString(System.Globalization.CultureInfo.InvariantCulture);
|
|||
|
|
|
|||
|
|
// Create temporary output series.
|
|||
|
|
_formulaData.Common.DataManager.Series.Add( new Series(_tempOutputSeriesName) );
|
|||
|
|
|
|||
|
|
// Execute formula
|
|||
|
|
double result = double.NaN;
|
|||
|
|
try
|
|||
|
|
{
|
|||
|
|
_formulaData.Formula("InverseTDistribution", parameter, _tempOutputSeriesName, _tempOutputSeriesName);
|
|||
|
|
|
|||
|
|
DataPointCollection points = _formulaData.Common.DataManager.Series[_tempOutputSeriesName].Points;
|
|||
|
|
|
|||
|
|
// Fill Output class
|
|||
|
|
result = points[0].YValues[0];
|
|||
|
|
}
|
|||
|
|
finally
|
|||
|
|
{
|
|||
|
|
// Remove Temporary output series
|
|||
|
|
_formulaData.Common.DataManager.Series.Remove(_formulaData.Common.DataManager.Series[_tempOutputSeriesName]);
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
// Return result class
|
|||
|
|
return result;
|
|||
|
|
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
#endregion // Distributions
|
|||
|
|
|
|||
|
|
#region Correlation and Covariance
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// This method gets the covariance value for two series of data.
|
|||
|
|
/// </summary>
|
|||
|
|
/// <param name="firstInputSeriesName">First input series name.</param>
|
|||
|
|
/// <param name="secondInputSeriesName">Second input series name.</param>
|
|||
|
|
/// <returns>Covariance.</returns>
|
|||
|
|
public double Covariance(
|
|||
|
|
string firstInputSeriesName,
|
|||
|
|
string secondInputSeriesName )
|
|||
|
|
{
|
|||
|
|
// Check arguments
|
|||
|
|
if (firstInputSeriesName == null)
|
|||
|
|
throw new ArgumentNullException("firstInputSeriesName");
|
|||
|
|
if (secondInputSeriesName == null)
|
|||
|
|
throw new ArgumentNullException("secondInputSeriesName");
|
|||
|
|
|
|||
|
|
// Create temporary output series.
|
|||
|
|
_formulaData.Common.DataManager.Series.Add( new Series(_tempOutputSeriesName) );
|
|||
|
|
|
|||
|
|
// Set input series string
|
|||
|
|
string inputSeriesParameter = firstInputSeriesName.ToString(System.Globalization.CultureInfo.InvariantCulture) + "," + secondInputSeriesName.ToString(System.Globalization.CultureInfo.InvariantCulture);
|
|||
|
|
|
|||
|
|
// Execute formula
|
|||
|
|
double result = double.NaN;
|
|||
|
|
try
|
|||
|
|
{
|
|||
|
|
_formulaData.Formula("Covariance", "", inputSeriesParameter, _tempOutputSeriesName);
|
|||
|
|
|
|||
|
|
DataPointCollection points = _formulaData.Common.DataManager.Series[_tempOutputSeriesName].Points;
|
|||
|
|
|
|||
|
|
// Fill Output value
|
|||
|
|
result = points[0].YValues[0];
|
|||
|
|
}
|
|||
|
|
finally
|
|||
|
|
{
|
|||
|
|
// Remove Temporary output series
|
|||
|
|
_formulaData.Common.DataManager.Series.Remove(_formulaData.Common.DataManager.Series[_tempOutputSeriesName]);
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
// Return result
|
|||
|
|
return result;
|
|||
|
|
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// This method gets the correlation value for two series of data.
|
|||
|
|
/// </summary>
|
|||
|
|
/// <param name="firstInputSeriesName">First input series name.</param>
|
|||
|
|
/// <param name="secondInputSeriesName">Second input series name.</param>
|
|||
|
|
/// <returns>Returns Correlation</returns>
|
|||
|
|
public double Correlation(
|
|||
|
|
string firstInputSeriesName,
|
|||
|
|
string secondInputSeriesName )
|
|||
|
|
{
|
|||
|
|
// Check arguments
|
|||
|
|
if (firstInputSeriesName == null)
|
|||
|
|
throw new ArgumentNullException("firstInputSeriesName");
|
|||
|
|
if (secondInputSeriesName == null)
|
|||
|
|
throw new ArgumentNullException("secondInputSeriesName");
|
|||
|
|
|
|||
|
|
// Create temporary output series.
|
|||
|
|
_formulaData.Common.DataManager.Series.Add( new Series(_tempOutputSeriesName) );
|
|||
|
|
|
|||
|
|
// Set input series string
|
|||
|
|
string inputSeriesParameter = firstInputSeriesName + "," + secondInputSeriesName;
|
|||
|
|
|
|||
|
|
// Execute formula
|
|||
|
|
double result = double.NaN;
|
|||
|
|
try
|
|||
|
|
{
|
|||
|
|
_formulaData.Formula("Correlation", "", inputSeriesParameter, _tempOutputSeriesName);
|
|||
|
|
|
|||
|
|
DataPointCollection points = _formulaData.Common.DataManager.Series[_tempOutputSeriesName].Points;
|
|||
|
|
|
|||
|
|
// Fill Output value
|
|||
|
|
result = points[0].YValues[0];
|
|||
|
|
}
|
|||
|
|
finally
|
|||
|
|
{
|
|||
|
|
// Remove Temporary output series
|
|||
|
|
_formulaData.Common.DataManager.Series.Remove(_formulaData.Common.DataManager.Series[_tempOutputSeriesName]);
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
// Return result
|
|||
|
|
return result;
|
|||
|
|
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// This method returns the average of all data points stored in the specified series.
|
|||
|
|
/// </summary>
|
|||
|
|
/// <param name="inputSeriesName">Input series name.</param>
|
|||
|
|
/// <returns>The average of all data points.</returns>
|
|||
|
|
public double Mean(
|
|||
|
|
string inputSeriesName )
|
|||
|
|
{
|
|||
|
|
// Check arguments
|
|||
|
|
if (inputSeriesName == null)
|
|||
|
|
throw new ArgumentNullException("inputSeriesName");
|
|||
|
|
|
|||
|
|
// Create temporary output series.
|
|||
|
|
_formulaData.Common.DataManager.Series.Add( new Series(_tempOutputSeriesName) );
|
|||
|
|
|
|||
|
|
// Set input series string
|
|||
|
|
string inputSeriesParameter = inputSeriesName;
|
|||
|
|
|
|||
|
|
// Execute formula
|
|||
|
|
double result = double.NaN;
|
|||
|
|
try
|
|||
|
|
{
|
|||
|
|
_formulaData.Formula("Mean", "", inputSeriesParameter, _tempOutputSeriesName);
|
|||
|
|
|
|||
|
|
DataPointCollection points = _formulaData.Common.DataManager.Series[_tempOutputSeriesName].Points;
|
|||
|
|
|
|||
|
|
// Fill Output value
|
|||
|
|
result = points[0].YValues[0];
|
|||
|
|
}
|
|||
|
|
finally
|
|||
|
|
{
|
|||
|
|
// Remove Temporary output series
|
|||
|
|
_formulaData.Common.DataManager.Series.Remove(_formulaData.Common.DataManager.Series[_tempOutputSeriesName]);
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
// Return result
|
|||
|
|
return result;
|
|||
|
|
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// This method returns the median of all data points in the specified series.
|
|||
|
|
/// </summary>
|
|||
|
|
/// <param name="inputSeriesName">Input series name.</param>
|
|||
|
|
/// <returns>Median.</returns>
|
|||
|
|
public double Median(
|
|||
|
|
string inputSeriesName )
|
|||
|
|
{
|
|||
|
|
// Check arguments
|
|||
|
|
if (inputSeriesName == null)
|
|||
|
|
throw new ArgumentNullException("inputSeriesName");
|
|||
|
|
|
|||
|
|
// Create temporary output series.
|
|||
|
|
_formulaData.Common.DataManager.Series.Add( new Series(_tempOutputSeriesName) );
|
|||
|
|
|
|||
|
|
// Set input series string
|
|||
|
|
string inputSeriesParameter = inputSeriesName;
|
|||
|
|
|
|||
|
|
// Execute formula
|
|||
|
|
double result = double.NaN;
|
|||
|
|
try
|
|||
|
|
{
|
|||
|
|
_formulaData.Formula("Median", "", inputSeriesParameter, _tempOutputSeriesName);
|
|||
|
|
|
|||
|
|
DataPointCollection points = _formulaData.Common.DataManager.Series[_tempOutputSeriesName].Points;
|
|||
|
|
|
|||
|
|
// Fill Output value
|
|||
|
|
result = points[0].YValues[0];
|
|||
|
|
}
|
|||
|
|
finally
|
|||
|
|
{
|
|||
|
|
// Remove Temporary output series
|
|||
|
|
_formulaData.Common.DataManager.Series.Remove(_formulaData.Common.DataManager.Series[_tempOutputSeriesName]);
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
// Return result
|
|||
|
|
return result;
|
|||
|
|
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// This method returns the variance for a series.
|
|||
|
|
/// </summary>
|
|||
|
|
/// <param name="inputSeriesName">Input series name.</param>
|
|||
|
|
/// <param name="sampleVariance">If true, the data is a sample of the population. If false, it is the entire population.</param>
|
|||
|
|
/// <returns>Variance.</returns>
|
|||
|
|
public double Variance(
|
|||
|
|
string inputSeriesName,
|
|||
|
|
bool sampleVariance )
|
|||
|
|
{
|
|||
|
|
// Check arguments
|
|||
|
|
if (inputSeriesName == null)
|
|||
|
|
throw new ArgumentNullException("inputSeriesName");
|
|||
|
|
|
|||
|
|
// Create temporary output series.
|
|||
|
|
_formulaData.Common.DataManager.Series.Add( new Series(_tempOutputSeriesName) );
|
|||
|
|
|
|||
|
|
// Set input series string
|
|||
|
|
string inputSeriesParameter = inputSeriesName;
|
|||
|
|
|
|||
|
|
// Formula parameter
|
|||
|
|
string parameter = sampleVariance.ToString(System.Globalization.CultureInfo.InvariantCulture);
|
|||
|
|
|
|||
|
|
// Execute formula
|
|||
|
|
double result = double.NaN;
|
|||
|
|
try
|
|||
|
|
{
|
|||
|
|
_formulaData.Formula("Variance", parameter, inputSeriesParameter, _tempOutputSeriesName);
|
|||
|
|
|
|||
|
|
DataPointCollection points = _formulaData.Common.DataManager.Series[_tempOutputSeriesName].Points;
|
|||
|
|
|
|||
|
|
// Fill Output value
|
|||
|
|
result = points[0].YValues[0];
|
|||
|
|
}
|
|||
|
|
finally
|
|||
|
|
{
|
|||
|
|
// Remove Temporary output series
|
|||
|
|
_formulaData.Common.DataManager.Series.Remove(_formulaData.Common.DataManager.Series[_tempOutputSeriesName]);
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
// Return result
|
|||
|
|
return result;
|
|||
|
|
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// This method returns the beta function for two given values.
|
|||
|
|
/// </summary>
|
|||
|
|
/// <param name="m">First parameter for beta function</param>
|
|||
|
|
/// <param name="n">Second Parameter for beta function</param>
|
|||
|
|
/// <returns>Returns beta function for the two given values.</returns>
|
|||
|
|
[SuppressMessage("Microsoft.Naming", "CA1704:IdentifiersShouldBeSpelledCorrectly",
|
|||
|
|
Justification = "The Beta Function is a mathematical function where arbitrary letters to indicate inputs are common")]
|
|||
|
|
public double BetaFunction(
|
|||
|
|
double m,
|
|||
|
|
double n )
|
|||
|
|
{
|
|||
|
|
// Fix for the VSTS 230829: The BetaFunction for the m=0,n=0 is double.NaN
|
|||
|
|
if (m == 0 && n == 0)
|
|||
|
|
return double.NaN;
|
|||
|
|
|
|||
|
|
// Create temporary output series.
|
|||
|
|
_formulaData.Common.DataManager.Series.Add( new Series(_tempOutputSeriesName) );
|
|||
|
|
|
|||
|
|
// Formula parameter
|
|||
|
|
string parameter = m.ToString(System.Globalization.CultureInfo.InvariantCulture);
|
|||
|
|
parameter += "," + n.ToString(System.Globalization.CultureInfo.InvariantCulture);
|
|||
|
|
|
|||
|
|
// Execute formula
|
|||
|
|
double result = double.NaN;
|
|||
|
|
try
|
|||
|
|
{
|
|||
|
|
_formulaData.Formula("BetaFunction", parameter, _tempOutputSeriesName, _tempOutputSeriesName);
|
|||
|
|
|
|||
|
|
DataPointCollection points = _formulaData.Common.DataManager.Series[_tempOutputSeriesName].Points;
|
|||
|
|
|
|||
|
|
// Fill Output value
|
|||
|
|
result = points[0].YValues[0];
|
|||
|
|
}
|
|||
|
|
finally
|
|||
|
|
{
|
|||
|
|
// Remove Temporary output series
|
|||
|
|
_formulaData.Common.DataManager.Series.Remove(_formulaData.Common.DataManager.Series[_tempOutputSeriesName]);
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
// Return result
|
|||
|
|
return result;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// This method returns the gamma function value for the given variable.
|
|||
|
|
/// </summary>
|
|||
|
|
/// <param name="value">The value.</param>
|
|||
|
|
/// <returns>Returns gamma function</returns>
|
|||
|
|
public double GammaFunction(
|
|||
|
|
double value )
|
|||
|
|
{
|
|||
|
|
|
|||
|
|
// Create temporary output series.
|
|||
|
|
_formulaData.Common.DataManager.Series.Add( new Series(_tempOutputSeriesName) );
|
|||
|
|
|
|||
|
|
// Formula parameter
|
|||
|
|
string parameter = value.ToString(System.Globalization.CultureInfo.InvariantCulture);
|
|||
|
|
|
|||
|
|
// Execute formula
|
|||
|
|
double result = double.NaN;
|
|||
|
|
try
|
|||
|
|
{
|
|||
|
|
_formulaData.Formula("GammaFunction", parameter, _tempOutputSeriesName, _tempOutputSeriesName);
|
|||
|
|
|
|||
|
|
DataPointCollection points = _formulaData.Common.DataManager.Series[_tempOutputSeriesName].Points;
|
|||
|
|
|
|||
|
|
// Fill Output value
|
|||
|
|
result = points[0].YValues[0];
|
|||
|
|
}
|
|||
|
|
finally
|
|||
|
|
{
|
|||
|
|
// Remove Temporary output series
|
|||
|
|
_formulaData.Common.DataManager.Series.Remove(_formulaData.Common.DataManager.Series[_tempOutputSeriesName]);
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
// Return result
|
|||
|
|
return result;
|
|||
|
|
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
#endregion
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
#region Output classes used to store statistical calculations results
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// The TTestResult class stores the results of the TTest statistical calculations.
|
|||
|
|
/// </summary>
|
|||
|
|
#if ASPPERM_35
|
|||
|
|
[AspNetHostingPermission(System.Security.Permissions.SecurityAction.InheritanceDemand, Level = AspNetHostingPermissionLevel.Minimal)]
|
|||
|
|
[AspNetHostingPermission(System.Security.Permissions.SecurityAction.LinkDemand, Level = AspNetHostingPermissionLevel.Minimal)]
|
|||
|
|
#endif
|
|||
|
|
public class TTestResult
|
|||
|
|
{
|
|||
|
|
#region Fields
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// First series' mean.
|
|||
|
|
/// </summary>
|
|||
|
|
internal double firstSeriesMean = 0.0;
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Second series' mean.
|
|||
|
|
/// </summary>
|
|||
|
|
internal double secondSeriesMean = 0.0;
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// First series' variance.
|
|||
|
|
/// </summary>
|
|||
|
|
internal double firstSeriesVariance = 0.0;
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Second series' variance.
|
|||
|
|
/// </summary>
|
|||
|
|
internal double secondSeriesVariance = 0.0;
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// T value.
|
|||
|
|
/// </summary>
|
|||
|
|
internal double tValue = 0.0;
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Degree of freedom.
|
|||
|
|
/// </summary>
|
|||
|
|
internal double degreeOfFreedom = 0.0;
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Probability T one tail.
|
|||
|
|
/// </summary>
|
|||
|
|
internal double probabilityTOneTail = 0.0;
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Critical T one tail.
|
|||
|
|
/// </summary>
|
|||
|
|
internal double tCriticalValueOneTail = 0.0;
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Probability T two tails.
|
|||
|
|
/// </summary>
|
|||
|
|
internal double probabilityTTwoTail = 0.0;
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Critical T two tails.
|
|||
|
|
/// </summary>
|
|||
|
|
internal double tCriticalValueTwoTail = 0.0;
|
|||
|
|
|
|||
|
|
#endregion
|
|||
|
|
|
|||
|
|
#region Properties
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Gets the mean of the first series.
|
|||
|
|
/// </summary>
|
|||
|
|
public double FirstSeriesMean
|
|||
|
|
{
|
|||
|
|
get
|
|||
|
|
{
|
|||
|
|
return firstSeriesMean;
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Gets the mean of the second series.
|
|||
|
|
/// </summary>
|
|||
|
|
public double SecondSeriesMean
|
|||
|
|
{
|
|||
|
|
get
|
|||
|
|
{
|
|||
|
|
return secondSeriesMean;
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Gets the variance of the first series.
|
|||
|
|
/// </summary>
|
|||
|
|
public double FirstSeriesVariance
|
|||
|
|
{
|
|||
|
|
get
|
|||
|
|
{
|
|||
|
|
return firstSeriesVariance;
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Gets the variance of the second series.
|
|||
|
|
/// </summary>
|
|||
|
|
public double SecondSeriesVariance
|
|||
|
|
{
|
|||
|
|
get
|
|||
|
|
{
|
|||
|
|
return secondSeriesVariance;
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Gets the T value.
|
|||
|
|
/// </summary>
|
|||
|
|
public double TValue
|
|||
|
|
{
|
|||
|
|
get
|
|||
|
|
{
|
|||
|
|
return tValue;
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Gets the degree of freedom.
|
|||
|
|
/// </summary>
|
|||
|
|
public double DegreeOfFreedom
|
|||
|
|
{
|
|||
|
|
get
|
|||
|
|
{
|
|||
|
|
return degreeOfFreedom;
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Gets the probability T one tail value.
|
|||
|
|
/// </summary>
|
|||
|
|
[SuppressMessage("Microsoft.Naming", "CA1702:CompoundWordsShouldBeCasedCorrectly",
|
|||
|
|
Justification = "T One Tail is a statistics term. 'Tone' is not the intended word here.")]
|
|||
|
|
public double ProbabilityTOneTail
|
|||
|
|
{
|
|||
|
|
get
|
|||
|
|
{
|
|||
|
|
return probabilityTOneTail;
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Gets the critical T one tail value.
|
|||
|
|
/// </summary>
|
|||
|
|
public double TCriticalValueOneTail
|
|||
|
|
{
|
|||
|
|
get
|
|||
|
|
{
|
|||
|
|
return tCriticalValueOneTail;
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Gets the probability T two tails value.
|
|||
|
|
/// </summary>
|
|||
|
|
public double ProbabilityTTwoTail
|
|||
|
|
{
|
|||
|
|
get
|
|||
|
|
{
|
|||
|
|
return probabilityTTwoTail;
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Gets the critical T two tails value.
|
|||
|
|
/// </summary>
|
|||
|
|
public double TCriticalValueTwoTail
|
|||
|
|
{
|
|||
|
|
get
|
|||
|
|
{
|
|||
|
|
return tCriticalValueTwoTail;
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
#endregion
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// The FTestResult class stores the results of the FTest statistical calculations.
|
|||
|
|
/// </summary>
|
|||
|
|
#if ASPPERM_35
|
|||
|
|
[AspNetHostingPermission(System.Security.Permissions.SecurityAction.InheritanceDemand, Level = AspNetHostingPermissionLevel.Minimal)]
|
|||
|
|
[AspNetHostingPermission(System.Security.Permissions.SecurityAction.LinkDemand, Level = AspNetHostingPermissionLevel.Minimal)]
|
|||
|
|
#endif
|
|||
|
|
public class FTestResult
|
|||
|
|
{
|
|||
|
|
#region Fields
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// First series' mean.
|
|||
|
|
/// </summary>
|
|||
|
|
internal double firstSeriesMean = 0.0;
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Second series' mean.
|
|||
|
|
/// </summary>
|
|||
|
|
internal double secondSeriesMean = 0.0;
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// First series' variance.
|
|||
|
|
/// </summary>
|
|||
|
|
internal double firstSeriesVariance = 0.0;
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Second series' variance.
|
|||
|
|
/// </summary>
|
|||
|
|
internal double secondSeriesVariance = 0.0;
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// F value.
|
|||
|
|
/// </summary>
|
|||
|
|
internal double fValue = 0.0;
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Probability F one tail.
|
|||
|
|
/// </summary>
|
|||
|
|
internal double probabilityFOneTail = 0.0;
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Critical F one tail.
|
|||
|
|
/// </summary>
|
|||
|
|
internal double fCriticalValueOneTail = 0.0;
|
|||
|
|
|
|||
|
|
#endregion
|
|||
|
|
|
|||
|
|
#region Properties
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Gets the mean of the first series.
|
|||
|
|
/// </summary>
|
|||
|
|
public double FirstSeriesMean
|
|||
|
|
{
|
|||
|
|
get
|
|||
|
|
{
|
|||
|
|
return firstSeriesMean;
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Gets the mean of the second series.
|
|||
|
|
/// </summary>
|
|||
|
|
public double SecondSeriesMean
|
|||
|
|
{
|
|||
|
|
get
|
|||
|
|
{
|
|||
|
|
return secondSeriesMean;
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Gets the variance of the first series.
|
|||
|
|
/// </summary>
|
|||
|
|
public double FirstSeriesVariance
|
|||
|
|
{
|
|||
|
|
get
|
|||
|
|
{
|
|||
|
|
return firstSeriesVariance;
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Gets the variance of the second series.
|
|||
|
|
/// </summary>
|
|||
|
|
public double SecondSeriesVariance
|
|||
|
|
{
|
|||
|
|
get
|
|||
|
|
{
|
|||
|
|
return secondSeriesVariance;
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Gets the F value.
|
|||
|
|
/// </summary>
|
|||
|
|
public double FValue
|
|||
|
|
{
|
|||
|
|
get
|
|||
|
|
{
|
|||
|
|
return fValue;
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Gets the probability F one tail.
|
|||
|
|
/// </summary>
|
|||
|
|
public double ProbabilityFOneTail
|
|||
|
|
{
|
|||
|
|
get
|
|||
|
|
{
|
|||
|
|
return probabilityFOneTail;
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Gets the critical F one tail.
|
|||
|
|
/// </summary>
|
|||
|
|
public double FCriticalValueOneTail
|
|||
|
|
{
|
|||
|
|
get
|
|||
|
|
{
|
|||
|
|
return fCriticalValueOneTail;
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
#endregion
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// The AnovaResult class stores the results of the Anova statistical calculations.
|
|||
|
|
/// </summary>
|
|||
|
|
#if ASPPERM_35
|
|||
|
|
[AspNetHostingPermission(System.Security.Permissions.SecurityAction.InheritanceDemand, Level = AspNetHostingPermissionLevel.Minimal)]
|
|||
|
|
[AspNetHostingPermission(System.Security.Permissions.SecurityAction.LinkDemand, Level = AspNetHostingPermissionLevel.Minimal)]
|
|||
|
|
#endif
|
|||
|
|
public class AnovaResult
|
|||
|
|
{
|
|||
|
|
#region Fields
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Sum of squares between groups.
|
|||
|
|
/// </summary>
|
|||
|
|
internal double sumOfSquaresBetweenGroups = 0.0;
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Sum of squares within groups.
|
|||
|
|
/// </summary>
|
|||
|
|
internal double sumOfSquaresWithinGroups = 0.0;
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Total sum of squares.
|
|||
|
|
/// </summary>
|
|||
|
|
internal double sumOfSquaresTotal = 0.0;
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Degree of freedom between groups.
|
|||
|
|
/// </summary>
|
|||
|
|
internal double degreeOfFreedomBetweenGroups = 0.0;
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Degree of freedom within groups.
|
|||
|
|
/// </summary>
|
|||
|
|
internal double degreeOfFreedomWithinGroups = 0.0;
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Total degree of freedom.
|
|||
|
|
/// </summary>
|
|||
|
|
internal double degreeOfFreedomTotal = 0.0;
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Mean square variance between groups.
|
|||
|
|
/// </summary>
|
|||
|
|
internal double meanSquareVarianceBetweenGroups = 0.0;
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Mean square variance between groups.
|
|||
|
|
/// </summary>
|
|||
|
|
internal double meanSquareVarianceWithinGroups = 0.0;
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// F ratio.
|
|||
|
|
/// </summary>
|
|||
|
|
internal double fRatio = 0.0;
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// F critical value.
|
|||
|
|
/// </summary>
|
|||
|
|
internal double fCriticalValue = 0.0;
|
|||
|
|
|
|||
|
|
#endregion
|
|||
|
|
|
|||
|
|
#region Properties
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Gets the sum of squares between groups.
|
|||
|
|
/// </summary>
|
|||
|
|
public double SumOfSquaresBetweenGroups
|
|||
|
|
{
|
|||
|
|
get
|
|||
|
|
{
|
|||
|
|
return sumOfSquaresBetweenGroups;
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Gets the sum of squares within groups.
|
|||
|
|
/// </summary>
|
|||
|
|
public double SumOfSquaresWithinGroups
|
|||
|
|
{
|
|||
|
|
get
|
|||
|
|
{
|
|||
|
|
return sumOfSquaresWithinGroups;
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Gets the total sum of squares.
|
|||
|
|
/// </summary>
|
|||
|
|
public double SumOfSquaresTotal
|
|||
|
|
{
|
|||
|
|
get
|
|||
|
|
{
|
|||
|
|
return sumOfSquaresTotal;
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Gets the degree of freedom between groups.
|
|||
|
|
/// </summary>
|
|||
|
|
public double DegreeOfFreedomBetweenGroups
|
|||
|
|
{
|
|||
|
|
get
|
|||
|
|
{
|
|||
|
|
return degreeOfFreedomBetweenGroups;
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Gets the degree of freedom within groups.
|
|||
|
|
/// </summary>
|
|||
|
|
public double DegreeOfFreedomWithinGroups
|
|||
|
|
{
|
|||
|
|
get
|
|||
|
|
{
|
|||
|
|
return degreeOfFreedomWithinGroups;
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Gets the total degree of freedom.
|
|||
|
|
/// </summary>
|
|||
|
|
public double DegreeOfFreedomTotal
|
|||
|
|
{
|
|||
|
|
get
|
|||
|
|
{
|
|||
|
|
return degreeOfFreedomTotal;
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Gets the mean square variance between groups.
|
|||
|
|
/// </summary>
|
|||
|
|
public double MeanSquareVarianceBetweenGroups
|
|||
|
|
{
|
|||
|
|
get
|
|||
|
|
{
|
|||
|
|
return meanSquareVarianceBetweenGroups;
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Gets the mean square variance within groups.
|
|||
|
|
/// </summary>
|
|||
|
|
public double MeanSquareVarianceWithinGroups
|
|||
|
|
{
|
|||
|
|
get
|
|||
|
|
{
|
|||
|
|
return meanSquareVarianceWithinGroups;
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Gets the F ratio.
|
|||
|
|
/// </summary>
|
|||
|
|
public double FRatio
|
|||
|
|
{
|
|||
|
|
get
|
|||
|
|
{
|
|||
|
|
return fRatio;
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Gets the F critical value.
|
|||
|
|
/// </summary>
|
|||
|
|
public double FCriticalValue
|
|||
|
|
{
|
|||
|
|
get
|
|||
|
|
{
|
|||
|
|
return fCriticalValue;
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
#endregion
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// The ZTestResult class stores the results of the ZTest statistical calculations.
|
|||
|
|
/// </summary>
|
|||
|
|
#if ASPPERM_35
|
|||
|
|
[AspNetHostingPermission(System.Security.Permissions.SecurityAction.InheritanceDemand, Level = AspNetHostingPermissionLevel.Minimal)]
|
|||
|
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[AspNetHostingPermission(System.Security.Permissions.SecurityAction.LinkDemand, Level = AspNetHostingPermissionLevel.Minimal)]
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#endif
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public class ZTestResult
|
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|
|
{
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#region Constructor
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|
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/// <summary>
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|
|
/// ZTestResult Constructor
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|||
|
|
/// </summary>
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|
|
public ZTestResult()
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|||
|
|
{
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|||
|
|
}
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|
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|
|||
|
|
#endregion // Constructor
|
|||
|
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|
|||
|
|
#region Fields
|
|||
|
|
|
|||
|
|
// Internal fields used for public properties
|
|||
|
|
internal double firstSeriesMean;
|
|||
|
|
internal double secondSeriesMean;
|
|||
|
|
internal double firstSeriesVariance;
|
|||
|
|
internal double secondSeriesVariance;
|
|||
|
|
internal double zValue;
|
|||
|
|
internal double probabilityZOneTail;
|
|||
|
|
internal double zCriticalValueOneTail;
|
|||
|
|
internal double probabilityZTwoTail;
|
|||
|
|
internal double zCriticalValueTwoTail;
|
|||
|
|
|
|||
|
|
|
|||
|
|
#endregion // Fields
|
|||
|
|
|
|||
|
|
#region Properties
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Gets the mean of the first series.
|
|||
|
|
/// </summary>
|
|||
|
|
public double FirstSeriesMean
|
|||
|
|
{
|
|||
|
|
get
|
|||
|
|
{
|
|||
|
|
return firstSeriesMean;
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Gets the mean of the second series.
|
|||
|
|
/// </summary>
|
|||
|
|
public double SecondSeriesMean
|
|||
|
|
{
|
|||
|
|
get
|
|||
|
|
{
|
|||
|
|
return secondSeriesMean;
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Gets the variance of the first series.
|
|||
|
|
/// </summary>
|
|||
|
|
public double FirstSeriesVariance
|
|||
|
|
{
|
|||
|
|
get
|
|||
|
|
{
|
|||
|
|
return firstSeriesVariance;
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Gets the variance of the second series.
|
|||
|
|
/// </summary>
|
|||
|
|
public double SecondSeriesVariance
|
|||
|
|
{
|
|||
|
|
get
|
|||
|
|
{
|
|||
|
|
return secondSeriesVariance;
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Gets the Z Value
|
|||
|
|
/// </summary>
|
|||
|
|
public double ZValue
|
|||
|
|
{
|
|||
|
|
get
|
|||
|
|
{
|
|||
|
|
return zValue;
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Gets the probability Z one tail value.
|
|||
|
|
/// </summary>
|
|||
|
|
[SuppressMessage("Microsoft.Naming", "CA1702:CompoundWordsShouldBeCasedCorrectly",
|
|||
|
|
Justification = "Z One Tail is a statistics term. 'Zone' is not the intended word here.")]
|
|||
|
|
public double ProbabilityZOneTail
|
|||
|
|
{
|
|||
|
|
get
|
|||
|
|
{
|
|||
|
|
return probabilityZOneTail;
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Gets the Z critical value one tail value.
|
|||
|
|
/// </summary>
|
|||
|
|
public double ZCriticalValueOneTail
|
|||
|
|
{
|
|||
|
|
get
|
|||
|
|
{
|
|||
|
|
return zCriticalValueOneTail;
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Gets the probability Z two tail value.
|
|||
|
|
/// </summary>
|
|||
|
|
public double ProbabilityZTwoTail
|
|||
|
|
{
|
|||
|
|
get
|
|||
|
|
{
|
|||
|
|
return probabilityZTwoTail;
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/// <summary>
|
|||
|
|
/// Gets the Z critical value two tail value.
|
|||
|
|
/// </summary>
|
|||
|
|
public double ZCriticalValueTwoTail
|
|||
|
|
{
|
|||
|
|
get
|
|||
|
|
{
|
|||
|
|
return zCriticalValueTwoTail;
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
#endregion // Properties
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
#endregion // Output Classes
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
|