Date of Award
2019
Publisher
North Dakota State University
Document Type
Thesis
Degree Awarded
Master of Science (MS)
Program
Statistics
Department
Statistics
College
Science and Mathematics
Faculty Advisor
Magel, Rhonda
Description
Many 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.
Recommended Citation
Young, Lucas Blackmore, "Type I Error Assessment and Power Comparison of ANOVA and Zero-Inflated Methods on Zero-Inflated Data" (2019). Statistics. 77.
https://digitalcommons.ndsu.edu/stats/77
Rights
NDSU policy 190.6.2
Rights Link
https://www.ndsu.edu/fileadmin/policy/190.pdf
Handle Identifier
https://hdl.handle.net/10365/31712