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dc.contributor.authorYoung, Lucas Blackmore
dc.description.abstractMany tests for the analysis of continuous data have the underlying assumption that the data in question follows a normal distribution (ex. ANOVA, regression, etc.). Within certain research topics, it is common to end up with a dataset that has a disproportionately high number of zero-values but is otherwise relatively normal. These datasets are often referred to as ‘zero-inflated’ and their analysis can be challenging. An example of where these zero-inflated datasets arise is in plant science. We conducted a simulation study to compare the performance of zero-inflated models to a standard ANOVA model on different types of zero-inflated data. Underlying distributions, experimental design scenario, sample sizes, and percentages of zeros were variables of consideration. In this study, we conduct a Type I error assessment followed by a power comparison between the models.en_US
dc.publisherNorth Dakota State Universityen_US
dc.rightsNDSU policy 190.6.2en_US
dc.titleType I Error Assessment and Power Comparison of ANOVA and Zero-Inflated Methods on Zero-Inflated Dataen_US
dc.typeThesisen_US
dc.date.accessioned2021-01-13T22:19:22Z
dc.date.available2021-01-13T22:19:22Z
dc.date.issued2019
dc.identifier.urihttps://hdl.handle.net/10365/31712
dc.rights.urihttps://www.ndsu.edu/fileadmin/policy/190.pdfen_US
ndsu.degreeMaster of Science (MS)en_US
ndsu.collegeScience and Mathematicsen_US
ndsu.departmentStatisticsen_US
ndsu.programStatisticsen_US
ndsu.advisorMagel, Rhonda


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