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Dissertations / Theses on the topic 'Regression equation etc'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Mecham, Charlotte M. "NRCS Curve Number Calibration Using USGS Regression Equations." BYU ScholarsArchive, 2008. https://scholarsarchive.byu.edu/etd/1388.

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The Curve Number (CN) method of estimating the direct runoff response to rainfall events was originally developed in the 1950's primarily for agricultural purposes in the mid-western United States. The accuracy of the CN method is greatly affected by variation of the soil type and land use of the region. Curve Numbers developed for a given region are not appropriate for application in other regions. In order to produce reliable, consistent results, Curve Numbers must be calibrated for the area in which the CN method is to be applied. Calibration is ideally accomplished by direct measurement us

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2

Adsero, Charlotte M. "NRCS Curve Number calibration using USGS regression equations /." Diss., CLICK HERE for online access, 2008. http://contentdm.lib.byu.edu/ETD/image/etd2369.pdf.

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3

Kasturiratna, Dhanuja. "Assessing the Distributional Assumptions in One-Way Regression Model." Bowling Green State University / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1148479945.

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4

Legare, Jonathan Charles. "Study of Structural Equation Models and their Application to Fitchburg Middle School Data." Digital WPI, 2009. https://digitalcommons.wpi.edu/etd-theses/101.

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Structural equation models combine factor analysis models and multivariate regression models to estimate associations between observed variables and unobserved variables. The main achievement of this Capstone Project is the understanding of structural equation models and application of the models to real-world data. In this report, we reviewed structural equation models and several prerequisite topics. We performed a simulation study to compare maximum likelihood structural equation model estimation versus two-stage sequential estimation using multiple linear regression and maximum likelihood

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5

Pemmanaboina, Rajashekar. "Assessing Crash Occurrence On Urban Freeways Using Static And Dynamic Factors By Applying A System Of Interrelated Equations." Master's thesis, University of Central Florida, 2005. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2617.

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Traffic crashes have been identified as one of the main causes of death in the US, making road safety a high priority issue that needs urgent attention. Recognizing the fact that more and effective research has to be done in this area, this thesis aims mainly at developing different statistical models related to the road safety. The thesis includes three main sections: 1) overall crash frequency analysis using negative binomial models, 2) seemingly unrelated negative binomial (SUNB) models for different categories of crashes divided based on type of crash, or condition in which they occur, 3)

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6

Moore, Laura Christine. "Calcium, Magnesium, and the Metabolic Syndrome in the 2001-2010 NHANES Adult Data Using Regression and Structural Equation Modeling Methods." Case Western Reserve University School of Graduate Studies / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=case1417792558.

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7

Brady, Kaitlyn. "Learning Curves in Emergency Ultrasonography." Digital WPI, 2012. https://digitalcommons.wpi.edu/etd-theses/1150.

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"This project utilized generalized estimating equations and general linear modeling to model learning curves for sonographer performance in emergency ultrasonography. Performance was measured in two ways: image quality (interpretable vs. possible hindrance in interpretation) and agreement of findings between the sonographer and an expert reviewing sonographer. Records from 109 sonographers were split into two data sets-- training (n=50) and testing (n=59)--to conduct exploratory analysis and fit the final models for analysis, respectively. We determined that the number of scans of a particular

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8

Addo, Evans Dapaa. "Performance Comparison of Imputation Algorithms on Missing at Random Data." Digital Commons @ East Tennessee State University, 2018. https://dc.etsu.edu/etd/3422.

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Missing data continues to be an issue not only the field of statistics but in any field, that deals with data. This is due to the fact that almost all the widely accepted and standard statistical software and methods assume complete data for all the variables included in the analysis. As a result, in most studies, statistical power is weakened and parameter estimates are biased, leading to weak conclusions and generalizations. Many studies have established that multiple imputation methods are effective ways of handling missing data. This paper examines three different imputation methods (predi

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9

Cimenler, Oguz. "Social Network Analysis of Researchers' Communication and Collaborative Networks Using Self-reported Data." Scholar Commons, 2014. https://scholarcommons.usf.edu/etd/5201.

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This research seeks an answer to the following question: what is the relationship between the structure of researchers' communication network and the structure of their collaborative output networks (e.g. co-authored publications, joint grant proposals, and joint patent applications), and the impact of these structures on their citation performance and the volume of collaborative research outputs? Three complementary studies are performed to answer this main question as discussed below. 1. Study I: A frequently used output to measure scientific (or research) collaboration is co-authorship in s

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10

Idri, Amanda C. "Development of a Correlation Equation Between Shear Wave Values And NSPT Values in Northeastern Ohio." University of Akron / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=akron1574441753941474.

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11

Alharbi, Abdulmajeed A. "Investigating Survey Response Rates and Analytic Choice of Survey Results fromUniversity Faculty in Saudi Arabia." Ohio University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1585051418774214.

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12

Palmer, Laura Michelle. "Impacts of Stationarity Assumption in Floodplain Management: Case Studies." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1483386481824778.

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13

Perera, Robert A. "Bias and precision of parameter estimates in structural equation modeling and multiple regression." 2009. http://etd.nd.edu/ETD-db/theses/available/etd-12112009-133431/.

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14

Byrd, Jimmy. "A Monte Carlo Investigation of Three Different Estimation Methods in Multilevel Structural Equation Modeling Under Conditions of Data Nonnormality and Varied Sample Sizes." 2008. http://hdl.handle.net/1969.1/ETD-TAMU-2008-12-248.

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The purpose of the study was to examine multilevel regression models in the context of multilevel structural equation modeling (SEM) in terms of accuracy of parameter estimates, standard errors, and fit indices in normal and nonnormal data under various sample sizes and differing estimators (maximum likelihood, generalized least squares, and weighted least squares). The finding revealed that the regression coefficients were estimated with little to no bias among the study design conditions investigated. However, the number of clusters (group level) appeared to have the greatest impact on bias

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15

Sun, Xiuzhen. "Bias Reduction and Goodness-of-Fit Tests in Conditional Logistic Regression Models." Thesis, 2010. http://hdl.handle.net/1969.1/ETD-TAMU-2010-08-8205.

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This dissertation consists of three projects in matched case-control studies. In the first project, we employ a general bias preventive approach developed by Firth (1993) to handle the bias of an estimator of the log-odds ratio parameter in conditional logistic regression by solving a modified score equation. The resultant estimator not only reduces bias but also can prevent producing infinite value. Furthermore, we propose a method to calculate the standard error of the resultant estimator. A closed form expression for the estimator of the log-odds ratio parameter is derived in the case of a

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