Coefficient of Determination Function (C#)http://r2.codeplex.com/project/feeds/rssA C# implementation function for "coefficient of determination" ("R2" or "R-Squared").Source code checked in, #18482http://r2.codeplex.com/SourceControl/changeset/changes/18482Upgrade: New Version of LabDefaultTemplate.xaml. To upgrade your build definitions, please visit the following link: http://go.microsoft.com/fwlink/?LinkId=254563Project Collection Service AccountsMon, 01 Oct 2012 20:29:34 GMTSource code checked in, #18482 20121001082934PSource code checked in, #18481http://r2.codeplex.com/SourceControl/changeset/changes/18481Checked in by server upgradeProject Collection Service AccountsMon, 01 Oct 2012 20:27:42 GMTSource code checked in, #18481 20121001082742PUpdated Wiki: Homehttp://r2.codeplex.com/wikipage?version=6<div class="wikidoc">
<p>A C# implementation of coefficient of determination.<br>
<br>
Enjoy!<br>
<br>
Uri, <a title="Stockato" href="http://www.stockato.com" target="_self">Stockato</a></p>
</div><div class="ClearBoth"></div>kartounTue, 12 Jun 2012 03:46:00 GMTUpdated Wiki: Home 20120612034600AUpdated Wiki: Homehttp://r2.codeplex.com/wikipage?version=5<div class="wikidoc">
<p>A C# implementation of coefficient of determination.<br>
<br>
From Wikipedia:<br>
<br>
"In statistics, the coefficient of determination R2 is used in the context of statistical models whose main purpose is the prediction of future outcomes on the basis of other related information. It is the proportion of variability in a data set that is accounted
for by the statistical model (Steel and Torrie, 1960). It provides a measure of how well future outcomes are likely to be predicted by the model. There are several different definitions of R2 which are only sometimes equivalent. One class of such cases includes
that of linear regression. In this case, if an intercept is included then R2 is simply the square of the sample correlation coefficient between the outcomes and their predicted values, or in the case of simple linear regression, between the outcomes and the
values of the single regressor being used for prediction. In such cases, the coefficient of determination ranges from 0 to 1. Important cases where the computational definition of R2 can yield negative values, depending on the definition used, arise where
the predictions which are being compared to the corresponding outcomes have not been derived from a model-fitting procedure using those data, and where linear regression is conducted without including an intercept."<br>
<br>
Steel, R. G. D. and Torrie, J. H., Principles and Procedures of Statistics, New York: McGraw-Hill, 1960, pp. 187, 287.<br>
<br>
Enjoy!<br>
<br>
Uri, <a href="http://www.linkedin.com/in/kartoun">LinkedIn</a></p>
</div><div class="ClearBoth"></div>kartounThu, 08 Sep 2011 02:47:48 GMTUpdated Wiki: Home 20110908024748AUpdated Wiki: Documentationhttp://r2.codeplex.com/documentation?version=3<div class="wikidoc">A C# implementation of coefficient of determination.<br /><br />From Wikipedia:<br /><br />"In statistics, the coefficient of determination R2 is used in the context of statistical models whose main purpose is the prediction of future outcomes on the basis of other related information. It is the proportion of variability in a data set that is accounted for by the statistical model (Steel and Torrie, 1960). It provides a measure of how well future outcomes are likely to be predicted by the model. There are several different definitions of R2 which are only sometimes equivalent. One class of such cases includes that of linear regression. In this case, if an intercept is included then R2 is simply the square of the sample correlation coefficient between the outcomes and their predicted values, or in the case of simple linear regression, between the outcomes and the values of the single regressor being used for prediction. In such cases, the coefficient of determination ranges from 0 to 1. Important cases where the computational definition of R2 can yield negative values, depending on the definition used, arise where the predictions which are being compared to the corresponding outcomes have not been derived from a model-fitting procedure using those data, and where linear regression is conducted without including an intercept."<br /><br />Steel, R. G. D. and Torrie, J. H., Principles and Procedures of Statistics, New York: McGraw-Hill, 1960, pp. 187, 287.</div><div class="ClearBoth"></div>kartounWed, 11 May 2011 14:26:37 GMTUpdated Wiki: Documentation 20110511022637PUpdated Wiki: Homehttp://r2.codeplex.com/wikipage?version=4<div class="wikidoc">A C# implementation of coefficient of determination.<br /><br />From Wikipedia:<br /><br />"In statistics, the coefficient of determination R2 is used in the context of statistical models whose main purpose is the prediction of future outcomes on the basis of other related information. It is the proportion of variability in a data set that is accounted for by the statistical model (Steel and Torrie, 1960). It provides a measure of how well future outcomes are likely to be predicted by the model. There are several different definitions of R2 which are only sometimes equivalent. One class of such cases includes that of linear regression. In this case, if an intercept is included then R2 is simply the square of the sample correlation coefficient between the outcomes and their predicted values, or in the case of simple linear regression, between the outcomes and the values of the single regressor being used for prediction. In such cases, the coefficient of determination ranges from 0 to 1. Important cases where the computational definition of R2 can yield negative values, depending on the definition used, arise where the predictions which are being compared to the corresponding outcomes have not been derived from a model-fitting procedure using those data, and where linear regression is conducted without including an intercept."<br /><br />Steel, R. G. D. and Torrie, J. H., Principles and Procedures of Statistics, New York: McGraw-Hill, 1960, pp. 187, 287.</div><div class="ClearBoth"></div>kartounWed, 11 May 2011 14:26:27 GMTUpdated Wiki: Home 20110511022627PReleased: Coefficient Of Determination (May 11, 2011)http://r2.codeplex.com/releases/view/66086
<div class="wikidoc">A C# implementation of coefficient of determination.<br>
<br>
From Wikipedia:<br>
<br>
"In statistics, the coefficient of determination R2 is used in the context of statistical models whose main purpose is the prediction of future outcomes on the basis of other related information. It is the proportion of variability in a data set that is
accounted for by the statistical model Steel and Torrie, 1960. It provides a measure of how well future outcomes are likely to be predicted by the model. There are several different definitions of R2 which are only sometimes equivalent. One class of such cases
includes that of linear regression. In this case, if an intercept is included then R2 is simply the square of the sample correlation coefficient between the outcomes and their predicted values, or in the case of simple linear regression, between the outcomes
and the values of the single regressor being used for prediction. In such cases, the coefficient of determination ranges from 0 to 1. Important cases where the computational definition of R2 can yield negative values, depending on the definition used, arise
where the predictions which are being compared to the corresponding outcomes have not been derived from a model-fitting procedure using those data, and where linear regression is conducted without including an intercept."<br>
<br>
Steel, R. G. D. and Torrie, J. H., Principles and Procedures of Statistics, New York: McGraw-Hill, 1960, pp. 187, 287.</div>
<div></div>
Wed, 11 May 2011 14:24:49 GMTReleased: Coefficient Of Determination (May 11, 2011) 20110511022449PUpdated Release: Coefficient Of Determination (May 11, 2011)http://r2.codeplex.com/releases/view/66086<div class="wikidoc">A C# implementation of coefficient of determination.<br /><br />From Wikipedia:<br /><br />"In statistics, the coefficient of determination R2 is used in the context of statistical models whose main purpose is the prediction of future outcomes on the basis of other related information. It is the proportion of variability in a data set that is accounted for by the statistical model Steel and Torrie, 1960. It provides a measure of how well future outcomes are likely to be predicted by the model. There are several different definitions of R2 which are only sometimes equivalent. One class of such cases includes that of linear regression. In this case, if an intercept is included then R2 is simply the square of the sample correlation coefficient between the outcomes and their predicted values, or in the case of simple linear regression, between the outcomes and the values of the single regressor being used for prediction. In such cases, the coefficient of determination ranges from 0 to 1. Important cases where the computational definition of R2 can yield negative values, depending on the definition used, arise where the predictions which are being compared to the corresponding outcomes have not been derived from a model-fitting procedure using those data, and where linear regression is conducted without including an intercept."<br /><br />Steel, R. G. D. and Torrie, J. H., Principles and Procedures of Statistics, New York: McGraw-Hill, 1960, pp. 187, 287.</div><div class="ClearBoth"></div>kartounWed, 11 May 2011 14:24:49 GMTUpdated Release: Coefficient Of Determination (May 11, 2011) 20110511022449PUpdated Release: Coefficient Of Determination (May 11, 2011)http://r2.codeplex.com/releases/view/66086<div class="wikidoc">A C# implementation of coefficient of determination.<br /><br />From Wikipedia:<br /><br />"In statistics, the coefficient of determination R2 is used in the context of statistical models whose main purpose is the prediction of future outcomes on the basis of other related information. It is the proportion of variability in a data set that is accounted for by the statistical model Steel and Torrie, 1960. It provides a measure of how well future outcomes are likely to be predicted by the model. There are several different definitions of R2 which are only sometimes equivalent. One class of such cases includes that of linear regression. In this case, if an intercept is included then R2 is simply the square of the sample correlation coefficient between the outcomes and their predicted values, or in the case of simple linear regression, between the outcomes and the values of the single regressor being used for prediction. In such cases, the coefficient of determination ranges from 0 to 1. Important cases where the computational definition of R2 can yield negative values, depending on the definition used, arise where the predictions which are being compared to the corresponding outcomes have not been derived from a model-fitting procedure using those data, and where linear regression is conducted without including an intercept."<br /><br />Steel, R. G. D. and Torrie, J. H., Principles and Procedures of Statistics, New York: McGraw-Hill, 1960, pp. 187, 287.</div><div class="ClearBoth"></div>kartounWed, 11 May 2011 14:20:10 GMTUpdated Release: Coefficient Of Determination (May 11, 2011) 20110511022010PUpdated Release: Coefficient Of Determination (May 11, 2011)http://r2.codeplex.com/releases/view/66086<div class="wikidoc">A C# implementation of coefficient of determination.<br /><br />From Wikipedia:<br /><br />"In statistics, the coefficient of determination R2 is used in the context of statistical models whose main purpose is the prediction of future outcomes on the basis of other related information. It is the proportion of variability in a data set that is accounted for by the statistical model <a href="http://r2.codeplex.com/wikipage?title=1">1</a>. It provides a measure of how well future outcomes are likely to be predicted by the model. There are several different definitions of R2 which are only sometimes equivalent. One class of such cases includes that of linear regression. In this case, if an intercept is included then R2 is simply the square of the sample correlation coefficient between the outcomes and their predicted values, or in the case of simple linear regression, between the outcomes and the values of the single regressor being used for prediction. In such cases, the coefficient of determination ranges from 0 to 1. Important cases where the computational definition of R2 can yield negative values, depending on the definition used, arise where the predictions which are being compared to the corresponding outcomes have not been derived from a model-fitting procedure using those data, and where linear regression is conducted without including an intercept."<br /><br /><a href="http://r2.codeplex.com/wikipage?title=1">1</a> Steel, R. G. D. and Torrie, J. H., Principles and Procedures of Statistics, New York: McGraw-Hill, 1960, pp. 187, 287.</div><div class="ClearBoth"></div>kartounWed, 11 May 2011 14:19:46 GMTUpdated Release: Coefficient Of Determination (May 11, 2011) 20110511021946PPatch Applied: #9383http://r2.codeplex.com/SourceControl/list/patches
<p><a href='http://www.codeplex.com/site/users/view/kartoun'>kartoun</a> has applied patch #9383.</p>
<p><b>Comment:</b><br />-</p>kartounWed, 11 May 2011 14:17:39 GMTPatch Applied: #9383 20110511021739PPatch Declined: #9384http://r2.codeplex.com/SourceControl/list/patches
<p><a href='http://www.codeplex.com/site/users/view/kartoun'>kartoun</a> has declined patch #9384.</p>
<p><b>Comment:</b><br />-</p>kartounWed, 11 May 2011 14:17:26 GMTPatch Declined: #9384 20110511021726PPatch Uploaded: #9384http://r2.codeplex.com/SourceControl/list/patches
<p><a href='http://www.codeplex.com/site/users/view/kartoun'>kartoun</a> has uploaded a patch.</p>
<p><b>Description:</b><br />A C# implementation of coefficient of determination.<br /><br />From Wikipedia:<br /><br />"In statistics, the coefficient of determination R2 is used in the context of statistical models whose main purpose is the prediction of future outcomes on the basis of other related information. It is the proportion of variability in a data set that is accounted for by the statistical model [1]. It provides a measure of how well future outcomes are likely to be predicted by the model. There are several different definitions of R2 which are only sometimes equivalent. One class of such cases includes that of linear regression. In this case, if an intercept is included then R2 is simply the square of the sample correlation coefficient between the outcomes and their predicted values, or in the case of simple linear regression, between the outcomes and the values of the single regressor being used for prediction. In such cases, the coefficient of determination ranges from 0 to 1. Important cases where the computational definition of R2 can yield negative values, depending on the definition used, arise where the predictions which are being compared to the corresponding outcomes have not been derived from a model-fitting procedure using those data, and where linear regression is conducted without including an intercept."<br /><br />[1] Steel, R. G. D. and Torrie, J. H., Principles and Procedures of Statistics, New York: McGraw-Hill, 1960, pp. 187, 287.</p>kartounWed, 11 May 2011 14:17:08 GMTPatch Uploaded: #9384 20110511021708PPatch Uploaded: #9383http://r2.codeplex.com/SourceControl/list/patches
<p><a href='http://www.codeplex.com/site/users/view/kartoun'>kartoun</a> has uploaded a patch.</p>
<p><b>Description:</b><br />A C# implementation of coefficient of determination.<br /><br />From Wikipedia:<br /><br />"In statistics, the coefficient of determination R2 is used in the context of statistical models whose main purpose is the prediction of future outcomes on the basis of other related information. It is the proportion of variability in a data set that is accounted for by the statistical model [1]. It provides a measure of how well future outcomes are likely to be predicted by the model. There are several different definitions of R2 which are only sometimes equivalent. One class of such cases includes that of linear regression. In this case, if an intercept is included then R2 is simply the square of the sample correlation coefficient between the outcomes and their predicted values, or in the case of simple linear regression, between the outcomes and the values of the single regressor being used for prediction. In such cases, the coefficient of determination ranges from 0 to 1. Important cases where the computational definition of R2 can yield negative values, depending on the definition used, arise where the predictions which are being compared to the corresponding outcomes have not been derived from a model-fitting procedure using those data, and where linear regression is conducted without including an intercept."<br /><br />[1] Steel, R. G. D. and Torrie, J. H., Principles and Procedures of Statistics, New York: McGraw-Hill, 1960, pp. 187, 287.</p>kartounWed, 11 May 2011 14:15:32 GMTPatch Uploaded: #9383 20110511021532PCreated Release: Coefficient Of Determination (May 11, 2011)http://r2.codeplex.com/releases?ReleaseId=66086<div class="wikidoc">A C# implementation of coefficient of determination.<br /><br />From Wikipedia:<br /><br />"In statistics, the coefficient of determination R2 is used in the context of statistical models whose main purpose is the prediction of future outcomes on the basis of other related information. It is the proportion of variability in a data set that is accounted for by the statistical model <a href="http://r2.codeplex.com/wikipage?title=1">1</a>. It provides a measure of how well future outcomes are likely to be predicted by the model. There are several different definitions of R2 which are only sometimes equivalent. One class of such cases includes that of linear regression. In this case, if an intercept is included then R2 is simply the square of the sample correlation coefficient between the outcomes and their predicted values, or in the case of simple linear regression, between the outcomes and the values of the single regressor being used for prediction. In such cases, the coefficient of determination ranges from 0 to 1. Important cases where the computational definition of R2 can yield negative values, depending on the definition used, arise where the predictions which are being compared to the corresponding outcomes have not been derived from a model-fitting procedure using those data, and where linear regression is conducted without including an intercept."<br /><br /><a href="http://r2.codeplex.com/wikipage?title=1">1</a> Steel, R. G. D. and Torrie, J. H., Principles and Procedures of Statistics, New York: McGraw-Hill, 1960, pp. 187, 287.</div><div class="ClearBoth"></div>kartounWed, 11 May 2011 14:12:30 GMTCreated Release: Coefficient Of Determination (May 11, 2011) 20110511021230P