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Dissertations / Theses on the topic 'Logistic regression analysis'

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Published: 4 June 2021

Last updated: 25 July 2025

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1

Lo, Sau Yee. "Measurement error in logistic regression model /." View abstract or full-text, 2004. http://library.ust.hk/cgi/db/thesis.pl?MATH%202004%20LO.

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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2004.<br>Includes bibliographical references (leaves 82-83). Also available in electronic version. Access restricted to campus users.

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2

Olsén, Johan. "Logistic regression modelling for STHR analysis." Thesis, KTH, Matematisk statistik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-148971.

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Coronary artery heart disease (CAD) is a common condition which can impair the quality of life and lead to cardiac infarctions. Traditional criteria during exercise tests are good but far from perfect. A lot of patients with inconclusive tests are referred to radiological examinations. By finding better evaluation criteria during the exercise test we can save a lot of money and let the patients avoid unnecessary examinations. Computers record amounts of numerical data during the exercise test. In this retrospective study 267 patients with inconclusive exercise test and performed radiological e

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3

Hu, ChungLynn. "Nonignorable nonresponse in the logistic regression analysis /." The Ohio State University, 1998. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487950153601414.

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4

Emfevid, Lovisa, and Hampus Nyquist. "Financial Risk Profiling using Logistic Regression." Thesis, KTH, Matematisk statistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229821.

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As automation in the financial service industry continues to advance, online investment advice has emerged as an exciting new field. Vital to the accuracy of such service is the determination of the individual investors’ ability to bear financial risk. To do so, the statistical method of logistic regression is used. The aim of this thesis is to identify factors which are significant in determining a financial risk profile of a retail investor. In other words, the study seeks to map out the relationship between several socioeconomic- and psychometric variables to develop a predictive model able

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5

Webster, Gregg. "Bayesian logistic regression models for credit scoring." Thesis, Rhodes University, 2011. http://hdl.handle.net/10962/d1005538.

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The Bayesian approach to logistic regression modelling for credit scoring is useful when there are data quantity issues. Data quantity issues might occur when a bank is opening in a new location or there is change in the scoring procedure. Making use of prior information (available from the coefficients estimated on other data sets, or expert knowledge about the coefficients) a Bayesian approach is proposed to improve the credit scoring models. To achieve this, a data set is split into two sets, “old” data and “new” data. Priors are obtained from a model fitted on the “old” data. This model is

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6

Pan, Tianshu. "Using the multivariate multilevel logistic regression model to detect DIF a comparison with HGLM and logistic regression DIF detection methods /." Diss., Connect to online resource - MSU authorized users, 2008.

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Thesis (PH. D.)--Michigan State University. Measurement and Quantitative Methods, 2008.<br>Title from PDF t.p. (viewed on Sept. 8, 2009) Includes bibliographical references (p. 85-89). Also issued in print.

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7

McGlothlin, Anna E. Stamey James D. Seaman John Weldon. "Logistic regression with misclassified response and covariate measurement error a Bayesian approach /." Waco, Tex. : Baylor University, 2007. http://hdl.handle.net/2104/5101.

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8

Lindroth, Henriksson Amelia, and Simon Koller. "Logistic Regression Analysis of Patent Approval Rate in Sweden." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230143.

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This thesis was conducted to investigate what factors impact the outcome of a patent application for the Swedish market. The method used was logistic regression and the data was extracted from the database of The Swedish Patent and Registration Offi ce, PRV. The analysis in this thesis started with 47 covariates, including the 35 IPO technical fields, resulting in a model consisting of five covariates. The most important covariates were determined to be the number of notices issued by PRV, whether or not a patent attorney was used and applicant type. The number of notices had a positive impact

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9

Heise, Mark A. "Optimal designs for a bivariate logistic regression model." Diss., Virginia Tech, 1993. http://hdl.handle.net/10919/38538.

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In drug-testing experiments the primary responses of interest are efficacy and toxicity. These can be modeled as a bivariate quantal response using the Gumbel model for bivariate logistic regression. D-optimal and Q-optimal experimental designs are developed for this model The Q-optimal design minimizes the average asymptotic prediction variance of p(l,O;d), the probability of efficacy without toxicity at dose d, over a desired range of doses. In addition, a new optimality criterion, T -optimality, is developed which minimizes the asymptotic variance of the estimate of the therapeutic index.

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10

Jin, Yi. "Regression Analysis of University Giving Data." Digital WPI, 2007. https://digitalcommons.wpi.edu/etd-theses/1.

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This project analyzed the giving data of Worcester Polytechnic Institute's alumni and other constituents (parents, friends, neighbors, etc.) from fiscal year 1983 to 2007 using a two-stage modeling approach. Logistic regression analysis was conducted in the first stage to predict the likelihood of giving for each constituent, followed by linear regression method in the second stage which was used to predict the amount of contribution to be expected from each contributor. Box-Cox transformation was performed in the linear regression phase to ensure the assumption underlying the model holds. Due

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11

Guo, Ruijuan. "Sample comparisons using microarrays: - Application of False Discovery Rate and quadratic logistic regression." Digital WPI, 2008. https://digitalcommons.wpi.edu/etd-theses/28.

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In microarray analysis, people are interested in those features that have different characters in diseased samples compared to normal samples. The usual p-value method of selecting significant genes either gives too many false positives or cannot detect all the significant features. The False Discovery Rate (FDR) method controls false positives and at the same time selects significant features. We introduced Benjamini's method and Storey's method to control FDR, applied the two methods to human Meningioma data. We found that Benjamini's method is more conservative and that, after the number of

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12

Simmons, Carol Ivy. "A Logistic Regression Analysis of Multiple Independent Variables Impacting Psychiatric Readmissions." Thesis, Capella University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10289773.

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<p> This dissertation explored several internal and external factors in relation to psychiatric readmissions. Internal factors are directly related to the individual i.e., demographic information, diagnosis, admission history and status. External factors are factors outside of the individuals control i.e., length of hospital stay and reimbursement processes. The goal of the study was to explore the impact of multiple factors in relation to the phenomenon of psychiatric readmissions. Dynamic Systems Theory (1994) was used as a theoretical foundation to understand the complexities associated wit

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13

Adnan, Arisman. "Analysis of taste-panel data using ANOVA and ordinal logistic regression." Thesis, University of Newcastle Upon Tyne, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.402150.

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14

Chen, Wei. "Analysis of Rheumatoid Arthritis Data using Logistic Regression and Penalized Approach." Scholar Commons, 2015. http://scholarcommons.usf.edu/etd/5923.

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In this paper, a rheumatoid arthritis (RA) medicine clinical dataset with an ordinal response is selected to study this new medicine. In the dataset, there are four features, sex, age,treatment, and preliminary. Sex is a binary categorical variable with 1 indicates male, and 0 indicates female. Age is the numerical age of the patients. And treatment is a binary categorical variable with 1 indicates has RA, and 0 indicates does not have RA. And preliminary is a five class categorical variable indicates the patient’s RA severity status before taking the medication. The response Y is 5 class ordi

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15

Wu, Songfei. "A combination procedure of universal kriging and logistic regression a thesis presented to the faculty of the Graduate School, Tennessee Technological University /." Click to access online, 2008. http://proquest.umi.com/pqdweb?index=31&sid=1&srchmode=1&vinst=PROD&fmt=6&startpage=-1&clientid=28564&vname=PQD&RQT=309&did=1679675411&scaling=FULL&ts=1251312326&vtype=PQD&rqt=309&TS=1251312380&clientId=28564.

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16

Whitmore, Marjorie Lee Threet. "A Comparison of Two Differential Item Functioning Detection Methods: Logistic Regression and an Analysis of Variance Approach Using Rasch Estimation." Thesis, University of North Texas, 1995. https://digital.library.unt.edu/ark:/67531/metadc278366/.

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Differential item functioning (DIF) detection rates were examined for the logistic regression and analysis of variance (ANOVA) DIF detection methods. The methods were applied to simulated data sets of varying test length (20, 40, and 60 items) and sample size (200, 400, and 600 examinees) for both equal and unequal underlying ability between groups as well as for both fixed and varying item discrimination parameters. Each test contained 5% uniform DIF items, 5% non-uniform DIF items, and 5% combination DIF (simultaneous uniform and non-uniform DIF) items. The factors were completely crossed, a

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Alexander, Erika D. "Comparisons of Improvement-Over-Chance Effect Sizes for Two Groups Under Variance Heterogeneity and Prior Probabilities." Thesis, University of North Texas, 2003. https://digital.library.unt.edu/ark:/67531/metadc4242/.

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The distributional properties of improvement-over-chance, I, effect sizes derived from linear and quadratic predictive discriminant analysis (PDA) and from logistic regression analysis (LRA) for the two-group univariate classification were examined. Data were generated under varying levels of four data conditions: population separation, variance pattern, sample size, and prior probabilities. None of the indices provided acceptable estimates of effect for all the conditions examined. There were only a small number of conditions under which both accuracy and precision were acceptable. The result

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18

Junek, William N. "Forecasting Volcanic Activity Using An Event Tree Analysis System and Logistic Regression." Doctoral diss., University of Central Florida, 2012. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5333.

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Forecasts of short term volcanic activity are generated using an event tree process that is driven by a set of empirical statistical models derived through logistic regression. Each of the logistic models are constructed from a sparse and geographically diverse dataset that was assembled from a collection of historic volcanic unrest episodes. The dataset consists of monitoring measurements (e.g. seismic), source modeling results, and historic eruption information. Incorporating this data into a single set of models provides a simple mechanism for simultaneously accounting for the geophysica

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19

Weng, Chin-Fang. "Fixed versus mixed parameterization in logistic regression models application to meta-analysis /." College Park, Md.: University of Maryland, 2008. http://hdl.handle.net/1903/8985.

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Thesis (M.A.) -- University of Maryland, College Park, 2008.<br>Thesis research directed by: Dept. of Mathematics. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.

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Crixell, JoAnna Christine Seaman John Weldon Stamey James D. "Logistic regression with covariate measurement error in an adaptive design a Bayesian approach /." Waco, Tex. : Baylor University, 2008. http://hdl.handle.net/2104/5229.

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21

Louw, Nelmarie. "Aspects of the pre- and post-selection classification performance of discriminant analysis and logistic regression." Thesis, Stellenbosch : Stellenbosch University, 1997. http://hdl.handle.net/10019.1/55402.

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Thesis (PhD)--Stellenbosch University, 1997.<br>One copy microfiche.<br>ENGLISH ABSTRACT: Discriminani analysis and logistic regression are techniques that can be used to classify entities of unknown origin into one of a number of groups. However, the underlying models and assumptions for application of the two techniques differ. In this study, the two techniques are compared with respect to classification of entities. Firstly, the two techniques were compared in situations where no data dependent variable selection took place. Several underlying distributions were studied: the normal d

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22

Guo, Ruijuan. "Sample comparisons using microarrays -- application of false discovery rate and quadratic logistic regression." Worcester, Mass. : Worcester Polytechnic Institute, 2007. http://www.wpi.edu/Pubs/ETD/Available/etd-010808-173747/.

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23

Maxwell, Kori Lloyd Hugh. "Logistic Regression Analysis to Determine the Significant Factors Associated with Substance Abuse in School-Aged Children." Digital Archive @ GSU, 2009. http://digitalarchive.gsu.edu/math_theses/67.

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Substance abuse is the overindulgence in and dependence on a drug or chemical leading to detrimental effects on the individual’s health and the welfare of those surrounding him or her. Logistic regression analysis is an important tool used in the analysis of the relationship between various explanatory variables and nominal response variables. The objective of this study is to use this statistical method to determine the factors which are considered to be significant contributors to the use or abuse of substances in school-aged children and also determine what measures can be implemented to mi

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24

Geroukis, Asterios, and Erik Brorson. "Predicting Insolvency : A comparison between discriminant analysis and logistic regression using principal components." Thesis, Uppsala universitet, Statistiska institutionen, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-243289.

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In this study, we compare the two statistical techniques logistic regression and discriminant analysis to see how well they classify companies based on clusters – made from the solvency ratio – using principal components as independent variables. The principal components are made with different financial ratios. We use cluster analysis to find groups with low, medium and high solvency ratio of 1200 different companies found on the NASDAQ stock market and use this as an apriori definition of risk. The results shows that the logistic regression outperforms the discriminant analysis in classifyi

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25

Hardin, Patrik, and Sam Tabari. "Modelling Non-life Insurance Policyholder Price Sensitivity : A Statistical Analysis Performed with Logistic Regression." Thesis, KTH, Matematisk statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-209773.

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This bachelor thesis within mathematical statistics studies the possibility of modelling the renewal probability for commercial non-life insurance policyholders. The project was carried out in collaboration with the non-life insurance company If P&C Insurance Ltd. at their headquarters in Stockholm, Sweden. The paper includes an introduction to underlying concepts within insurance and mathematics and a detailed review of the analytical process followed by a discussion and conclusions. The first stages of the project were the initial collection and processing of explanatory insurance data a

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26

Sturm, Pamela S. "Knowing when a higher education institution is in trouble." Huntington, WV : [Marshall University Libraries], 2005. http://www.marshall.edu/etd/descript.asp?ref=583.

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27

Vizcain, Dorian Charles. "Investigating the Hispanic/Latino Male Dropout Phenomenon: Using Logistic Regression and Survival Analysis." [Tampa, Fla] : University of South Florida, 2005. http://purl.fcla.edu/usf/dc/et/SFE0001322.

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28

Welch, Catherine E. "Factors Affecting Postsecondary Enrollment among Vermont High School Graduates| A Logistic Regression Analysis." Thesis, New England College, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=13859163.

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<p> The State of Vermont has long had one of the highest high school graduation rates in New England, hovering around 87.8% with a lagging college enrollment rate of 52.3% at any 2- or 4-year postsecondary institution in the country (New England Secondary School Consortium, 2015). This research explored the factors that have the greatest effect on the college enrollment patterns of Vermont high school graduates. Specifically, this study explored the relationship between the following factors and 2- and 4-year college enrollment: (a) academic preparation, (b) access to college information, (c)

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29

VENUGOPALAN, ARAVIND. "STATISTICAL ANALYSIS OF POSTERIOR FOSSA SURGERIES FOR TRIGEMINAL NEURALGIA USING LOGISTIC REGRESSION MODELS." University of Cincinnati / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1155831511.

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30

Spence, Maria A. Stancil. "Successful vocational rehabilitation for persons with significant mental disabilities : a logistic regression analysis /." The Ohio State University, 2000. http://rave.ohiolink.edu/etdc/view?acc_num=osu1488196234910504.

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31

Yanik, Todd E. "Detection of erroneous payments utilizing supervised and utilizing supervised and unsupervised data mining techniques." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2004. http://library.nps.navy.mil/uhtbin/hyperion/04Sep%5FYanik.pdf.

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CRISANTI, MARK. "THE PREDICTIVE ACCURACY OF BOOSTED CLASSIFICATION TREES RELATIVE TO DISCRIMINANT ANALYSIS AND LOGISTIC REGRESSION." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1178566287.

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33

Lee, Michelle Oi San. "Sample size calculation for testing an interaction effect in a logistic regression under measurement error model /." View Abstract or Full-Text, 2003. http://library.ust.hk/cgi/db/thesis.pl?MATH%202003%20LEE.

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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2003.<br>Includes bibliographical references (leaves 66-67). Also available in electronic version. Access restricted to campus users.

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34

Whitten, Tyson. "Defining and Measuring Persistent Offending." Thesis, Griffith University, 2018. http://hdl.handle.net/10072/378078.

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BACKGROUND Persistent offending has been a topic of interest in the criminal justice literature for some time. Despite its popularity, the concept remains without a consistent or agreed upon definition. For example, persistent offending has been variably defined as offending before and during adulthood (e.g., Bergman & Andershed, 2009), frequent offending (e.g., McGloin & Stickle, 2011), or an early age of first offence (e.g. Hay & Forrest, 2009). In turn, inconsistent methods have been used to operationalise and identify persistent offenders, such as offending before and after the age of 21 (

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35

Cronstedt, Axel, and Rebecca Andersson. "Readjusting Historical Credit Ratings : using Ordered Logistic Regression and Principal ComponentAnalysis." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-148567.

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Readjusting Historical Credit Ratings using Ordered Logistic Re-gression and Principal Component Analysis The introduction of the Basel II Accord as a regulatory document for creditrisk presented new concepts of credit risk management and credit risk mea-surements, such as enabling international banks to use internal estimates ofprobability of default (PD), exposure at default (EAD) and loss given default(LGD). These three measurements is the foundation of the regulatory capitalcalculations and are all in turn based on the bank’s internal credit ratings. Ithas hence been of increasing importan

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Stevenson, Clint Wesley. "A logistic regression analysis of utah colleges exit poll response rates using SAS software /." Diss., CLICK HERE for online access, 2006. http://contentdm.lib.byu.edu/ETD/image/etd1578.pdf.

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37

Aranda, Diana Ixchel. "Historical Analysis of Recreational Beach Enterococci Levels; Using Logistic Regression as an Advisory Tool." NSUWorks, 2013. http://nsuworks.nova.edu/occ_stuetd/182.

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Enterococci levels are measured to assess water safety in recreational beaches through a state surveillance program. This surveillance informs the public of beach safety, yet the sampling methodology is limited to only making an advisory posting one sample at a time. This methodology poses a challenge for managers such as: 24 hour advisory waiting period, untested days and extreme variability of enterococci levels in the environment. Therefore, there is a need to integrate adaptive management methodologies that can assist managers to proactively assess beach water safety. This study explored t

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Honarvar, Pauline. "A spatial approach to mineral potential modelling using decision tree and logistic regression analysis /." Internet access available to MUN users only, 2001. http://collections.mun.ca/u?/theses,51228.

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39

Stevenson, Clint W. "A Logistic Regression Analysis of Utah Colleges Exit Poll Response Rates Using SAS Software." BYU ScholarsArchive, 2006. https://scholarsarchive.byu.edu/etd/1116.

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In this study I examine voter response at an interview level using a dataset of 7562 voter contacts (including responses and nonresponses) in the 2004 Utah Colleges Exit Poll. In 2004, 4908 of the 7562 voters approached responded to the exit poll for an overall response rate of 65 percent. Logistic regression is used to estimate factors that contribute to a success or failure of each interview attempt. This logistic regression model uses interviewer characteristics, voter characteristics (both respondents and nonrespondents), and exogenous factors as independent variables. Voter characteristic

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Oates, Krystle S. "A logistic regression analysis of score sending and college matching among high school students." Diss., University of Iowa, 2015. https://ir.uiowa.edu/etd/1994.

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College decisions are often the result of a variety of influences related to student background characteristics, academic characteristics, college preferences and college aspirations. College counselors recommend that students choose a variety of schools, especially schools where the general student body matches the academic achievement of students. These types of schools are generally referred to as match schools. This thesis examined the initial college decisions of high school students in a large

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41

Wang, Junhua. "Large-Sample Logistic Regression with Latent Covariates in a Bayesian Networking Context." TopSCHOLAR®, 2009. http://digitalcommons.wku.edu/theses/103.

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We considered the problem of predicting student retention using logistic regression when the most important covariates such as the college variables are latent, but the network structure is known. This network structure specifies the relationship between pre-college to college variables and then from college to student retention variables. Based on this structure, we developed three estimators, examined their large-sample properties, and evaluated their empirical efficiencies using WKU student retention database. Results show that while the hat estimator is expected to be most efficient, the t

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MATYATIM, Rosliza. "The Classification Model for Corporate Failures in Malaysia." Graduate School of International Development, Nagoya University, 2006. http://hdl.handle.net/2237/7314.

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VACCARELLA, SALVATORE. "A multilevel logistic regression model for the analyses of concurrent Human papillomavirus (HPV) infections." Doctoral thesis, Università degli Studi di Milano, 2007. http://hdl.handle.net/2434/33629.

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Human Papillomavirus (HPV) is a sexually transmitted infection that causes cervical cancer. A nested three-level logistic regression model was introduced in order to investigate whether, in the IARC HPV prevalence surveys, co-infection with different HPV types occurs more or less frequently than expected if the infections are independent from one another. Two random effects, at individual and study-area level, were specified, while the fixed-effect covariates at individual level were age and lifetime number of sexual partners. The Best Linear Unbiased Predictors (BLUP) technique was used to es

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Gusnanto, Arief. "Regression on high-dimensional predictor space : with application in chemometrics and microarray data /." Stockholm, 2004. http://diss.kib.ki.se/2004/91-7140-153-9/.

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Wålinder, Andreas. "Evaluation of logistic regression and random forest classification based on prediction accuracy and metadata analysis." Thesis, Linnéuniversitetet, Institutionen för matematik (MA), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-35126.

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Model selection is an important part of classification. In this thesis we study the two classification models logistic regression and random forest. They are compared and evaluated based on prediction accuracy and metadata analysis. The models were trained on 25 diverse datasets. We calculated the prediction accuracy of both models using RapidMiner. We also collected metadata for the datasets concerning number of observations, number of predictor variables and number of classes in the response variable. There is a correlation between performance of logistic regression and random forest wit

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Reischman, Diann. "Order restricted inferences on parameters in generalized linear models with emphasis on logistic regression /." free to MU campus, to others for purchase, 1997. http://wwwlib.umi.com/cr/mo/fullcit?p9842560.

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Flor, Andrew Douglas. "Evaluating Levee Failure Susceptibility on the Mississippi River Using Logistic Regression Analysis and GPS Surveying." Available to subscribers only, 2009. http://proquest.umi.com/pqdweb?did=1791850971&sid=8&Fmt=2&clientId=1509&RQT=309&VName=PQD.

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Mitchell, Marlon R. "Participation in adult education activities logistic regression analysis of baby boomers in the United States /." [Bloomington, Ind.] : Indiana University, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3274281.

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Thesis (Ph.D.)--Indiana University, Instructional Systems Technology, 2007.<br>Source: Dissertation Abstracts International, Volume: 68-07, Section: A, page: 2763. Adviser: Thomas Schwen. Title from dissertation home page (viewed Apr. 9, 2008).

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Kim, Hyun-Joo. "Model selection criteria based on Kullback information measures for Weibull, logistic, and nonlinear regression frameworks /." free to MU campus, to others for purchase, 2000. http://wwwlib.umi.com/cr/mo/fullcit?p9988677.

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Jian, Wen. "Analysis of Longitudinal Data in the Case-Control Studies via Empirical Likelihood." Digital Archive @ GSU, 2006. http://digitalarchive.gsu.edu/math_theses/8.

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The case-control studies are primary tools for the study of risk factors (exposures) related to the disease interested. The case-control studies using longitudinal data are cost and time efficient when the disease is rare and assessing the exposure level of risk factors is difficult. Instead of GEE method, the method of using a prospective logistic model for analyzing case-control longitudinal data was proposed and the semiparametric inference procedure was explored by Park and Kim (2004). In this thesis, we apply an empirical likelihood ratio method to derive limiting distribution of the empi

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