A diagnostic function to examine candidate distributions to model univariate data

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dc.contributor.author Richards, John
dc.date.accessioned 2010-05-10T13:31:52Z
dc.date.available 2010-05-10T13:31:52Z
dc.date.issued 2010-05-10T13:31:52Z
dc.identifier.uri http://hdl.handle.net/2097/4093
dc.description.abstract To help with identifying distributions to effectively model univariate continuous data, the R function diagnostic is proposed. The function will aid in determining reasonable candidate distributions that the data may have come from. It uses a combination of the Pearson goodness of fit statistic, Anderson-Darling statistic, Lin’s concordance correlation between the theoretical quantiles and observed quantiles, and the maximum difference between the theoretical quantiles and the observed quantiles. The function generates reasonable candidate distributions, QQ plots, and histograms with superimposed density curves. When a simulation study was done, the function worked adequately; however, it was also found that many of the distributions look very similar if the parameters are chosen carefully. The function was then used to attempt to decipher which distribution could be used to model weekly grocery expenditures of a family household. en_US
dc.language.iso en_US en_US
dc.publisher Kansas State University en
dc.subject statistics en_US
dc.subject R en_US
dc.subject statistical computing en_US
dc.subject goodness of fit en_US
dc.subject probability distributions en_US
dc.subject diagnostic en_US
dc.title A diagnostic function to examine candidate distributions to model univariate data en_US
dc.type Report en_US
dc.description.degree Master of Science en_US
dc.description.level Masters en_US
dc.description.department Department of Statistics en_US
dc.description.advisor Suzanne Dubnicka en_US
dc.subject.umi Statistics (0463) en_US
dc.date.published 2010 en_US
dc.date.graduationmonth May en_US

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