Theses and Dissertations from UMD

Permanent URI for this communityhttp://hdl.handle.net/1903/2

New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM

More information is available at Theses and Dissertations at University of Maryland Libraries.

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2014 - 20182

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Subject: search.filters.subject.Logistic regression

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    A COMPARATIVE ANALYSIS OF RANDOM FOREST AND LOGISTIC REGRESSION FOR WEED RISK ASSESSMENT
    (2018) Harris, Chinchu; Peer, Wendy; Plant Science and Landscape Architecture (PSLA); Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Invasive species have largely negative impacts on the environment and the economy. The management and regulation of invasive plants are facilitated using screening tools, such as weed risk assessments (WRAs) to predict the invasive potential of non-native plants. The identification of these species and their subsequent regulation on importation helps to reduce the risk of future ecosystem and economic costs. Globally, there are many different types of highly useful WRAs already available. However, in this day of big data and powerful predictive analytics, there is an increasing demand for the development of new and more robust screening tools. In this thesis, I use the machine learning algorithm, Random forests, to develop a new WRA. I show that random forest model has greater predictive accuracies than an existing logistic regression model and that random forest is a better learner. In addition, variable importance analysis was performed to identify factors associated with invasive status classification of non-native plants. The study suggests that random forests make powerful weed risk screening tools and should be utilized for assessing invasive risk potential along with other WRAs. An integrative approach for evaluating weed risk can greatly serve to facilitate the WRA process.
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    Social Bonds as Predictors of College Student Willingness to Report Hazing
    (2014) Bittinger, Joshua; Gottfredson, Denise; Criminology and Criminal Justice; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Little is currently known about what factors influence a victim's willingness to report hazing experienced in higher education. This problem of hazing has largely been ignored by criminologists, despite anti-hazing statutes existing in many states. The present study aims to examine this problem through a criminological lens using Social Bonding Theory (SBT). SBT suggests that deviant behavior is more likely to occur when a person is poorly bonded to conventional society (Hirschi, 1969). This theory was originally intended to explain deviant behavior; however, this study investigates its utility in explaining reporting behavior of victimization. Data were collected from surveys administered at the University of Maryland (N = 56), utilizing vignettes to present hypothetical hazing situations and were analyzed using logistic regression. Results provide no support for the use of SBT to predict a student's willingness to report experienced hazing, as described in the vignettes. Limitations and implications are discussed.