PREDICTION INTERVALS FOR SUMMED TOTALS
Methods are discussed for calculating prediction intervals for total estimates that are sums of individually derived estimates. Special attention is given to the problems encountered when the variances of each of the individually derived estimates cannot be assumed to be equal. This problem is essen...
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| creator | Dei Rossi, J A |
| description | Methods are discussed for calculating prediction intervals for total estimates that are sums of individually derived estimates. Special attention is given to the problems encountered when the variances of each of the individually derived estimates cannot be assumed to be equal. This problem is essentially identical to the well-known Behren-Fisher problem except that here the context is one of deriving a 't-ratio' for summed means. For the case of unequal variances, the prediction interval is based on a statistic with an approximate t-distribution and the interval itself must be viewed as an approximation. Examples are given for each of the cases considered. |
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Special attention is given to the problems encountered when the variances of each of the individually derived estimates cannot be assumed to be equal. This problem is essentially identical to the well-known Behren-Fisher problem except that here the context is one of deriving a 't-ratio' for summed means. For the case of unequal variances, the prediction interval is based on a statistic with an approximate t-distribution and the interval itself must be viewed as an approximation. Examples are given for each of the cases considered.</description><language>eng</language><subject>APPROXIMATION(MATHEMATICS) ; CORRELATION TECHNIQUES ; COST ANALYSIS ; DISTRIBUTION FUNCTIONS ; MATHEMATICAL PREDICTION ; Operations Research ; STATISTICAL PROCESSES</subject><creationdate>1968</creationdate><rights>Approved for public release; distribution is unlimited. 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This problem is essentially identical to the well-known Behren-Fisher problem except that here the context is one of deriving a 't-ratio' for summed means. For the case of unequal variances, the prediction interval is based on a statistic with an approximate t-distribution and the interval itself must be viewed as an approximation. Examples are given for each of the cases considered.</description><subject>APPROXIMATION(MATHEMATICS)</subject><subject>CORRELATION TECHNIQUES</subject><subject>COST ANALYSIS</subject><subject>DISTRIBUTION FUNCTIONS</subject><subject>MATHEMATICAL PREDICTION</subject><subject>Operations Research</subject><subject>STATISTICAL PROCESSES</subject><fulltext>true</fulltext><rsrctype>report</rsrctype><creationdate>1968</creationdate><recordtype>report</recordtype><sourceid>1RU</sourceid><recordid>eNrjZFALCHJ18XQO8fT3U_D0C3ENCnP0CVZw8w9SCA719XV1UQjxDwGK8DCwpiXmFKfyQmluBhk31xBnD92Ukszk-OKSzLzUknhHFwMzcwtTA1NjAtIAd_Yg1w</recordid><startdate>196810</startdate><enddate>196810</enddate><creator>Dei Rossi, J A</creator><scope>1RU</scope><scope>BHM</scope></search><sort><creationdate>196810</creationdate><title>PREDICTION INTERVALS FOR SUMMED TOTALS</title><author>Dei Rossi, J A</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-dtic_stinet_AD06785053</frbrgroupid><rsrctype>reports</rsrctype><prefilter>reports</prefilter><language>eng</language><creationdate>1968</creationdate><topic>APPROXIMATION(MATHEMATICS)</topic><topic>CORRELATION TECHNIQUES</topic><topic>COST ANALYSIS</topic><topic>DISTRIBUTION FUNCTIONS</topic><topic>MATHEMATICAL PREDICTION</topic><topic>Operations Research</topic><topic>STATISTICAL PROCESSES</topic><toplevel>online_resources</toplevel><creatorcontrib>Dei Rossi, J A</creatorcontrib><creatorcontrib>RAND CORP SANTA MONICA CA</creatorcontrib><collection>DTIC Technical Reports</collection><collection>DTIC STINET</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Dei Rossi, J A</au><aucorp>RAND CORP SANTA MONICA CA</aucorp><format>book</format><genre>unknown</genre><ristype>RPRT</ristype><btitle>PREDICTION INTERVALS FOR SUMMED TOTALS</btitle><date>1968-10</date><risdate>1968</risdate><abstract>Methods are discussed for calculating prediction intervals for total estimates that are sums of individually derived estimates. Special attention is given to the problems encountered when the variances of each of the individually derived estimates cannot be assumed to be equal. This problem is essentially identical to the well-known Behren-Fisher problem except that here the context is one of deriving a 't-ratio' for summed means. For the case of unequal variances, the prediction interval is based on a statistic with an approximate t-distribution and the interval itself must be viewed as an approximation. Examples are given for each of the cases considered.</abstract><oa>free_for_read</oa></addata></record> |
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| language | eng |
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| source | DTIC Technical Reports |
| subjects | APPROXIMATION(MATHEMATICS) CORRELATION TECHNIQUES COST ANALYSIS DISTRIBUTION FUNCTIONS MATHEMATICAL PREDICTION Operations Research STATISTICAL PROCESSES |
| title | PREDICTION INTERVALS FOR SUMMED TOTALS |
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