ANALYSIS OF THE EFFECTS OF OVERDISPERSION IN POPULATION DYNAMICS.

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dc.contributor.author Owusu, Seth
dc.date.accessioned 2018-12-04T09:29:48Z
dc.date.available 2018-12-04T09:29:48Z
dc.date.issued 2018-12-04
dc.identifier.citation OwusuS2018 en_US
dc.identifier.uri http://hdl.handle.net/123456789/4866
dc.description Master of Science in (Mathematics) (Statistics option) en_US
dc.description.abstract Population size and growth rate have great impact on the society and economy of every country. Finding the determinants and growth rate of the population have become fundamental to pol- icy makers in developing countries like Ghana, and its capital Accra in particular. Population growth models like the Exponential and Logistic Growth Models are mostly used in population dynamics. Most variables in this area are count variables. Count models are appropriate for modeling count variables. However, count models are hardly used in this eld. Most count data have the problem of overdispersion, where data exhibits more variability than expected under an assumed model. There are possible advantages of including overdispersion in the modeling process. This study sought to investigate the e ects of overdispersion in count data, and compare the Negative Binomial Model which is able to account for overdispersion to the Exponential and Logistic Growth Models which do not account for overdispersion. The results were then applied to a real dataset of the Greater Accra Region of Ghana. The results showed that avoiding overdispersion when it do exist has dire consequences. Partic- ularly, standard errors of the estimates are understated and the signi cance of some covariates are overstated. This was more evident when the data from the Greater Accra Region was modeled. Unless overdispersion is treated, inferences on such results are misleading. The study also revealed that the Negative Binomial Model actually performed better than the population growth models in modeling the population growth. The study recommends that analysts con- sider overdispersion when modeling count data and recommends the Negative Binomial Model to be used in modeling population data as compared to the Exponential and Logistic Growth Models. en_US
dc.description.sponsorship Dr. Jane Akinyi (Supervisor) Prof. George Orwa (Supervisor) en_US
dc.language.iso en en_US
dc.publisher JKUAT-PAUSTI en_US
dc.subject EFFECTS OF OVERDISPERSION en_US
dc.subject POPULATION DYNAMICS. en_US
dc.subject ANALYSIS en_US
dc.title ANALYSIS OF THE EFFECTS OF OVERDISPERSION IN POPULATION DYNAMICS. en_US
dc.type Thesis en_US


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