Discriminant analysis: Difference between revisions

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<div class="definition"><div class="short_definition">A variation of multiple [[linear]] [[regression analysis]] for [[prediction]] of the  occurrence or nonoccurrence of an event.</div><br/> <div class="paragraph">To account for the nonnumerical nature of the [[predictand]], a discriminant function is used as  a type of [[regression function]] usually derived in such a way that positive values of the function  correspond to &ldquo;occurrence&rdquo; and negative values to &ldquo;nonoccurrence.&rdquo; In meteorology, for example,  the occurrence of [[precipitation]] (the [[predictand]]) can be related to measures of [[vertical velocity]],  [[dewpoint]] temperature, [[pressure]] change, and other variables (the predictors) through a discriminant  function. Values of the function above or below a threshold value (typically, zero) can be  used to predict precipitation occurrence.</div><br/> </div>
<div class="definition"><div class="short_definition">A variation of multiple [[linear]] [[regression analysis]] for [[prediction]] of the  occurrence or nonoccurrence of an event.</div><br/> <div class="paragraph">To account for the nonnumerical nature of the [[predictand]], a discriminant function is used as  a type of [[regression function]] usually derived in such a way that positive values of the function  correspond to "occurrence" and negative values to "nonoccurrence." In meteorology, for example,  the occurrence of [[precipitation]] (the [[predictand]]) can be related to measures of [[vertical velocity]],  [[dewpoint]] temperature, [[pressure]] change, and other variables (the predictors) through a discriminant  function. Values of the function above or below a threshold value (typically, zero) can be  used to predict precipitation occurrence.</div><br/> </div>
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Latest revision as of 14:04, 20 February 2012



discriminant analysis[edit | edit source]

A variation of multiple linear regression analysis for prediction of the occurrence or nonoccurrence of an event.

To account for the nonnumerical nature of the predictand, a discriminant function is used as a type of regression function usually derived in such a way that positive values of the function correspond to "occurrence" and negative values to "nonoccurrence." In meteorology, for example, the occurrence of precipitation (the predictand) can be related to measures of vertical velocity, dewpoint temperature, pressure change, and other variables (the predictors) through a discriminant function. Values of the function above or below a threshold value (typically, zero) can be used to predict precipitation occurrence.


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