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Articles 1 - 30 of 42
Full-Text Articles in Physical Sciences and Mathematics
A Monte Carlo Analysis Of Nonprobability Sampling & Post Hoc Corrections, Julia Hong
A Monte Carlo Analysis Of Nonprobability Sampling & Post Hoc Corrections, Julia Hong
Masters Theses & Specialist Projects
Nonprobability samples are often used in place of probability samples because the former are less trouble and less expensive. Unfortunately, it is difficult to determine how well a sample represents population parameters when using nonprobability samples. Researchers attempt to mitigate the disadvantages of nonprobability sampling by performing post hoc corrections, but this adjustment may not successfully undo the effects of nonprobability sampling. To examine these effects, a Monte Carlo simulation was conducted to create a pseudo-population from which samples were drawn. Forty-one conditions were replicated 10,000 times each, with each sample consisting of 100 observations. A post-stratification adjustment was made …
A Monte Carlo Analysis Of Seven Dichotomous Variable Confidence Interval Equations, Morgan Juanita Dubose
A Monte Carlo Analysis Of Seven Dichotomous Variable Confidence Interval Equations, Morgan Juanita Dubose
Masters Theses & Specialist Projects
Department of Psychological Sciences Western Kentucky University There are two options to estimate a range of likely values for the population mean of a continuous variable: one for when the population standard deviation is known and another for when the population standard deviation is unknown. There are seven proposed equations to calculate the confidence interval for the population mean of a dichotomous variable: normal approximation interval, Wilson interval, Jeffreys interval, Clopper-Pearson, Agresti-Coull, arcsine transformation, and logit transformation. In this study, I compared the percent effectiveness of each equation using a Monte Carlo analysis and the interval range over a range …
A Monte Carlo Analysis Of Ordinary Least Squares Versus Equal Weights, James Brewer Ayres
A Monte Carlo Analysis Of Ordinary Least Squares Versus Equal Weights, James Brewer Ayres
Masters Theses & Specialist Projects
Equal weights are an alternative weighting procedure to the optimal weights offered by ordinary least squares regression analysis. Also called units weights, equal weights are formed by standardizing scores on the predictor variables and averaging these standardized scores to create a composite score. Research is limited regarding the conditions under which equal weights result in cross-validated 𝑅𝑅2 values that meet or exceed optimal weights. In this study, I explored the effect of various predictor-criterion correlations, predictor intercorrelations, and sample sizes to determine the relative performance of equal and optimal weighting schemes upon cross-validation. Results indicated that optimally weighted predictors explained …
An Analysis Of The Success Of Farmers Markets In Kentucky Using Logistic Regression And Support Vector Machines, Jeron Russell
An Analysis Of The Success Of Farmers Markets In Kentucky Using Logistic Regression And Support Vector Machines, Jeron Russell
Mahurin Honors College Capstone Experience/Thesis Projects
The purpose of this research is to look at the relationship that market-specific, economic, and demographic variables have with the success of farmers markets in Kentucky. It additionally seeks to build a tool for predicting farmers market success that could be used by policy makers to aid in decision-making processes concerning farmers markets. Logistic regression and Support Vector Machines (SVMs) are used on data acquired from the Kentucky Department of Agriculture and the American Community Survey in order to analyze the data in a traditional statistical approach as well as a machine learning approach. The results included an SVM model …
Sensitivity Analyses For Tumor Growth Models, Ruchini Dilinika Mendis
Sensitivity Analyses For Tumor Growth Models, Ruchini Dilinika Mendis
Masters Theses & Specialist Projects
This study consists of the sensitivity analysis for two previously developed tumor growth models: Gompertz model and quotient model. The two models are considered in both continuous and discrete time. In continuous time, model parameters are estimated using least-square method, while in discrete time, the partial-sum method is used. Moreover, frequentist and Bayesian methods are used to construct confidence intervals and credible intervals for the model parameters. We apply the Markov Chain Monte Carlo (MCMC) techniques with the Random Walk Metropolis algorithm with Non-informative Prior and the Delayed Rejection Adoptive Metropolis (DRAM) algorithm to construct parameters' posterior distributions and then …
Score Test And Likelihood Ratio Test For Zero-Inflated Binomial Distribution And Geometric Distribution, Xiaogang Dai
Score Test And Likelihood Ratio Test For Zero-Inflated Binomial Distribution And Geometric Distribution, Xiaogang Dai
Masters Theses & Specialist Projects
The main purpose of this thesis is to compare the performance of the score test and the likelihood ratio test by computing type I errors and type II errors when the tests are applied to the geometric distribution and inflated binomial distribution. We first derive test statistics of the score test and the likelihood ratio test for both distributions. We then use the software package R to perform a simulation to study the behavior of the two tests. We derive the R codes to calculate the two types of error for each distribution. We create lots of samples to approximate …
Investigating The Student Enrollment Decision At Wku, Alec Brown
Investigating The Student Enrollment Decision At Wku, Alec Brown
Mahurin Honors College Capstone Experience/Thesis Projects
The purpose of this research is to investigate the relationships between the enrollment decision of first-time, first-year students admitted to Western Kentucky University and the amount of financial aid awarded, as well as demographic information. The Division of Enrollment Management provided a SAS dataset containing various information about all WKU students admitted in 2013, 2014, and 2015. Additionally, information about the 2016 class of admitted students was provided. The data has been analyzed in SAS Enterprise Miner. We performed analysis using decision tree modeling and logistic regression modeling. Results of these two procedures indicated the importance of credit hours earned …
An Investigation Of The Accuracy Of Parallel Analysis For Determining The Number Of Factors In A Factor Analysis, Mandy Matsumoto
An Investigation Of The Accuracy Of Parallel Analysis For Determining The Number Of Factors In A Factor Analysis, Mandy Matsumoto
Mahurin Honors College Capstone Experience/Thesis Projects
Exploratory factor analysis is an analytic technique used to determine the number of factors in a set of data (usually items on a questionnaire) for which the factor structure has not been previously analyzed. Parallel analysis (PA) is a technique used to determine the number of factors in a factor analysis. There are a number of factors that affect the results of a PA: the choice of the eigenvalue percentile, the strength of the factor loadings, the number of variables, and the sample size of the study. Although PA is the most accurate method to date to determine which factors …
Ua56/1 Fact Book, Wku Institutional Research
Ua56/1 Fact Book, Wku Institutional Research
WKU Archives Records
Statistical and demographic profile of WKU.
Spatial Analysis Of Fatal Automobile Crashes In Kentucky, William Nathan Oris
Spatial Analysis Of Fatal Automobile Crashes In Kentucky, William Nathan Oris
Masters Theses & Specialist Projects
Fatal automobile crashes have claimed the lives of over 33,000 people each year in the United States since 1995. As in any point event, fatal crash events do not occur randomly in time or space. The objectives of this study were to identify spatial patterns and hot spots in FARS (Fatal Analysis Reporting System) fatal crash events based on temporal and demographic characteristics. The methods employed included 1) rate calculation using FARS points and average daily traffic flow; 2) planar kernel density estimation of FARS crash events based on temporal and demographic attributes within the data; and 3) two case …
Empirical Methods For Predicting Student Retention- A Summary From The Literature, Matt Bogard
Empirical Methods For Predicting Student Retention- A Summary From The Literature, Matt Bogard
Economics Faculty Publications
The vast majority of the literature related to the empirical estimation of retention models includes a discussion of the theoretical retention framework established by Bean, Braxton, Tinto, Pascarella, Terenzini and others (see Bean, 1980; Bean, 2000; Braxton, 2000; Braxton et al, 2004; Chapman and Pascarella, 1983; Pascarell and Ternzini, 1978; St. John and Cabrera, 2000; Tinto, 1975) This body of research provides a starting point for the consideration of which explanatory variables to include in any model specification, as well as identifying possible data sources. The literature separates itself into two major camps including research related to the hypothesis testing …
Monopoly, Regulation, And Innovation, Matt Bogard
Monopoly, Regulation, And Innovation, Matt Bogard
Economics Faculty Publications
Recently the Justice department has started investigations into alleged anti-trust violations by Monsanto. This has helped fuel a lot of already hyped discontent with one of the world’s leaders in innovative solutions for sustainable agriculture. This article discusses how the regulatory environment could possibly have contributed to more concentration and power in the biotech industry. Increasing regulation would likely have the opposite effect of creating a level playing field in the agriculture industry. From AgWeb, March 27,2010 http://www.agweb.com/blog/Economic_Sense_190/Monopoly_Regulation__and_Innovation_10771/
Sustainable Agriculture Bibliography, Matt Bogard
Sustainable Agriculture Bibliography, Matt Bogard
Agriculture Department Seminar Series
An annotated bibliography related to the sustainability of biotechnology and pharmaceutical technologies used in modern agriculture.
Using R, Matt Bogard
Using R, Matt Bogard
Economics Faculty Publications
R is a statistical programming language with a command line interface that is becoming more and more popular every day. I have used R for data visualization, data mining/machine learning, as well as social network analysis. Initially embraced largely in academia, R is becoming the software of choice in various corporate settings.
Mathematical Themes In Economics, Machine Learning, And Bioinformatics, Matt Bogard
Mathematical Themes In Economics, Machine Learning, And Bioinformatics, Matt Bogard
Economics Faculty Publications
Graduate students in economics are often introduced to some very useful mathematical tools that many outside the discipline may not associate with training in economics. This essay looks at some of these tools and concepts, including constrained optimization, separating hyperplanes, supporting hyperplanes, and ‘duality.’ Applications of these tools are explored including topics from machine learning and bioinformatics.
Random Walks With Elastic And Reflective Lower Boundaries, Lucas Clay Devore
Random Walks With Elastic And Reflective Lower Boundaries, Lucas Clay Devore
Masters Theses & Specialist Projects
No abstract provided.
Why Study Applied/Agricultural Economics, Matt Bogard
Why Study Applied/Agricultural Economics, Matt Bogard
Agriculture Department Seminar Series
Agricultural Economics is a very applied field covering many topics beyond those stereotypically thought of as pertaining to agriculture. These may include finance and risk management, environmental and natural resource economics, game theory, or public policy analysis to name a few.
Ua56/1 Fact Book, Wku Institutional Research
Ua56/1 Fact Book, Wku Institutional Research
WKU Archives Records
Statistical and demographic profile of WKU.
Ua56/1 Fact Book, Wku Institutional Research
Ua56/1 Fact Book, Wku Institutional Research
WKU Archives Records
Statistical and demographic profile of WKU.
Ua56/1 Fact Book, Wku Institutional Research
Ua56/1 Fact Book, Wku Institutional Research
WKU Archives Records
Statistical and demographic profile of WKU.
Ua56/1 Fact Book, Wku Institutional Research
Ua56/1 Fact Book, Wku Institutional Research
WKU Archives Records
Statistical and demographic profile of WKU.
Ua56/1 Fact Book, Wku Institutional Research
Ua56/1 Fact Book, Wku Institutional Research
WKU Archives Records
Statistical and demographic profile of WKU.
Ua56/1 Fact Book, Wku Institutional Research
Ua56/1 Fact Book, Wku Institutional Research
WKU Archives Records
Statistical and demographic profile of WKU.
Ua56/1 Fact Book, Wku Institutional Research
Ua56/1 Fact Book, Wku Institutional Research
WKU Archives Records
Statistical and demographic profile of WKU.
Ua56/1 Facts At A Glance, Wku Institutional Research
Ua56/1 Facts At A Glance, Wku Institutional Research
WKU Archives Records
Statistical and demographic profile of WKU.
Ua56/1 Fact Book, Wku Institutional Research
Ua56/1 Fact Book, Wku Institutional Research
WKU Archives Records
Statistical and demographic profile of WKU.
Ua56/1 Facts At A Glance, Wku Institutional Research
Ua56/1 Facts At A Glance, Wku Institutional Research
WKU Archives Records
Statistical and demographic profile of WKU.
Ua56/1 Fact Book, Wku Institutional Research
Ua56/1 Fact Book, Wku Institutional Research
WKU Archives Records
Statistical and demographic profile of WKU.
Ua56/1 Fact Book, Wku Institutional Research
Ua56/1 Fact Book, Wku Institutional Research
WKU Archives Records
Statistical and demographic profile of WKU.
Ua56/1 Fact Book, Wku Institutional Research
Ua56/1 Fact Book, Wku Institutional Research
WKU Archives Records
Statistical and demographic profile of WKU.