Relevant bibliographies by topics / Binary Logistic Regression Model

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Binary Logistic Regression Model'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Binary Logistic Regression Model"

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Noora, Shrestha. "Assessing Discriminatory Performance of a Binary Logistic Regression Model." International Journal of Advances in Scientific Research and Engineering (ijasre) 5, no. 7 (2019): 194–98. https://doi.org/10.31695/IJASRE.2019.33448.

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<em>The evaluation of fitted binary logistic regression model is very important in assessing the appropriateness of a model for specific purposes. The study proposes to assess the discriminatory performance of a binary logistic regression model to correctly classify between the cases and non-cases. The discriminatory performance of binary logistic regression model is measured using two approaches. The first approach is the use of fitted binary logistic regression model to correctly predict the subjects that are cases and non-cases, with the help of the parameters sensitivity and specificity. T
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Saran, Nurdan Ayse, and Fatih Nar. "Fast binary logistic regression." PeerJ Computer Science 11 (January 30, 2025): e2579. https://doi.org/10.7717/peerj-cs.2579.

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This study presents a novel numerical approach that improves the training efficiency of binary logistic regression, a popular statistical model in the machine learning community. Our method achieves training times an order of magnitude faster than traditional logistic regression by employing a novel Soft-Plus approximation, which enables reformulation of logistic regression parameter estimation into matrix-vector form. We also adopt the Lf-norm penalty, which allows using fractional norms, including the L2-norm, L1-norm, and L0-norm, to regularize the model parameters. We put Lf-norm formulati
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Harris, Jenine K. "Primer on binary logistic regression." Family Medicine and Community Health 9, Suppl 1 (2021): e001290. http://dx.doi.org/10.1136/fmch-2021-001290.

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Family medicine has traditionally prioritised patient care over research. However, recent recommendations to strengthen family medicine include calls to focus more on research including improving research methods used in the field. Binary logistic regression is one method frequently used in family medicine research to classify, explain or predict the values of some characteristic, behaviour or outcome. The binary logistic regression model relies on assumptions including independent observations, no perfect multicollinearity and linearity. The model produces ORs, which suggest increased, decrea
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Midi, Habshah, S. K. Sarkar, and Sohel Rana. "Collinearity diagnostics of binary logistic regression model." Journal of Interdisciplinary Mathematics 13, no. 3 (2010): 253–67. http://dx.doi.org/10.1080/09720502.2010.10700699.

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Dombi, József, and Tamás Jónás. "Kappa Regression: An Alternative to Logistic Regression." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 28, no. 02 (2020): 237–67. http://dx.doi.org/10.1142/s0218488520500105.

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In this study, a new regression method called Kappa regression is introduced to model conditional probabilities. The regression function is based on Dombi’s Kappa function, which is well known in fuzzy theory. Here, we discuss how the Kappa function relates to the Logistic function as well as how it can be used to approximate the Logistic function. We introduce the so-called Generalized Kappa Differential Equation and show that both the Kappa and the Logistic functions can be derived from it. Kappa regression, like binary Logistic regression, models the conditional probability of the event tha
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Srimaneekarn, Natchalee, Anthony Hayter, Wei Liu, and Chanita Tantipoj. "Binary Response Analysis Using Logistic Regression in Dentistry." International Journal of Dentistry 2022 (March 8, 2022): 1–7. http://dx.doi.org/10.1155/2022/5358602.

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Multivariate analysis with binary response is extensively utilized in dental research due to variations in dichotomous outcomes. One of the analyses for binary response variable is binary logistic regression, which explores the associated factors and predicts the response probability of the binary variable. This article aims to explain the statistical concepts of binary logistic regression analysis applicable to the field of dental research, including model fitting, goodness of fit test, and model validation. Moreover, interpretation of the model and logistic regression are also discussed with
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Li, Biwei. "Factors Affecting the Punctuality of Logistics Services Using Binary Logistic Regression." BCP Business & Management 34 (December 14, 2022): 704–12. http://dx.doi.org/10.54691/bcpbm.v34i.3085.

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The Indian logistics industry is in a period of rapid development. The purpose of this study is to analyze the factors affecting the punctuality of trucks for logistics distribution services in India based on data analysis. The data comes from 6880 pieces of data from the VTS data platform. A binary regression model was established for data analysis, and the model was tested to ensure statistical significance. Binary logistic regression was used to examine truck types, geographic locations, commodity types, order types, and suppliers for logistics services to examine significant differences be
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Chen, Lu, and Balgobin Nandram. "Bayesian Logistic Regression Model for Sub-Areas." Stats 6, no. 1 (2023): 209–31. http://dx.doi.org/10.3390/stats6010013.

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Many population-based surveys have binary responses from a large number of individuals in each household within small areas. One example is the Nepal Living Standards Survey (NLSS II), in which health status binary data (good versus poor) for each individual from sampled households (sub-areas) are available in the sampled wards (small areas). To make an inference for the finite population proportion of individuals in each household, we use the sub-area logistic regression model with reliable auxiliary information. The contribution of this model is twofold. First, we extend an area-level model
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Sultan, Maysoon A. "FACTORS AFFECTING OSTEOPOROSIS USING BINARY LOGISTIC REGRESSION MODEL." JP Journal of Biostatistics 24, no. 3 (2024): 439–48. http://dx.doi.org/10.17654/0973514324024.

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Kuha, Jouni, and Colin Mills. "On Group Comparisons With Logistic Regression Models." Sociological Methods & Research 49, no. 2 (2018): 498–525. http://dx.doi.org/10.1177/0049124117747306.

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It is widely believed that regression models for binary responses are problematic if we want to compare estimated coefficients from models for different groups or with different explanatory variables. This concern has two forms. The first arises if the binary model is treated as an estimate of a model for an unobserved continuous response and the second when models are compared between groups that have different distributions of other causes of the binary response. We argue that these concerns are usually misplaced. The first of them is only relevant if the unobserved continuous response is re
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Dissertations / Theses on the topic "Binary Logistic Regression Model"

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Wang, Jie. "Incorporating survey weights into logistic regression models." Digital WPI, 2013. https://digitalcommons.wpi.edu/etd-theses/267.

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Incorporating survey weights into likelihood-based analysis is a controversial issue because the sampling weights are not simply equal to the reciprocal of selection probabilities but they are adjusted for various characteristics such as age, race, etc. Some adjustments are based on nonresponses as well. This adjustment is accomplished using a combination of probability calculations. When we build a logistic regression model to predict categorical outcomes with survey data, the sampling weights should be considered if the sampling design does not give each individual an equal chance of being s
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Konis, Kjell Peter. "Linear programming algorithms for detecting separated data in binary logistic regression models." Thesis, University of Oxford, 2007. http://ora.ox.ac.uk/objects/uuid:8f9ee0d0-d78e-4101-9ab4-f9cbceed2a2a.

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This thesis is a study of the detection of separation among the sample points in binary logistic regression models. We propose a new algorithm for detecting separation and demonstrate empirically that it can be computed fast enough to be used routinely as part of the fitting process for logistic regression models. The parameter estimates of a binary logistic regression model fit using the method of maximum likelihood sometimes do not converge to finite values. This phenomenon (also known as monotone likelihood or infinite parameters) occurs because of a condition among the sample points known
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Zhang, Dongquan. "Effects of model selection on the coverage probability of confidence intervals in binary-response logistic regression." College Park, Md.: University of Maryland, 2008. http://hdl.handle.net/1903/8538.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2008.<br>Thesis research directed by: Dept. of Measurement, Statistics and Evaluation. 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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Bergtold, Jason Scott. "Advances in Applied Econometrics: Binary Discrete Choice Models, Artificial Neural Networks, and Asymmetries in the FAST Multistage Demand System." Diss., Virginia Tech, 2004. http://hdl.handle.net/10919/27266.

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The dissertation examines advancements in the methods and techniques used in the field of econometrics. These advancements include: (i) a re-examination of the underlying statistical foundations of statistical models with binary dependent variables. (ii) using feed-forward backpropagation artificial neural networks for modeling dichotomous choice processes, and (iii) the estimation of unconditional demand elasticities using the flexible multistage demand system with asymmetric partitions and fixed effects across time. The first paper re-examines the underlying statistical foundations of stati
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Beebe, Claire Elizabeth. "A comparison of stratified and unstratified modeling for binary logistic regression in the presence of a simulated interaction." Oklahoma City : [s.n.], 2008.

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Lopez, Andrea Salome Viteri. "Caracterização da chuva estimada pelo radar durante eventos de alagamento na cidade de São Paulo." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/14/14133/tde-25092018-163917/.

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Este projeto de mestrado apresenta uma caracterização das chuvas estimadas pelo radar meteorológico Doppler de dupla polarização banda S (SPOL) do Departamento de Águas e Energia Elétrica (DAEE) e Fundação Centro Tecnológico de Hidráulica (FCTH) durante eventos com ou sem alagamento para cada bairro da cidade de São Paulo durante o ano de 2015. A caracterização foi determinada a partir da função densidade de probabilidade (PDF) da chuva acumulada e da taxa de precipitação, duração da chuva e fração da área de cada bairro onde ocorreu a chuva. Na média, os eventos de alagamento estavam associad
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Sperry, Rita A. "Prediction of retention and probation status of first-year college students in learning communities using binary logistic regression models." Thesis, Texas A&M University - Corpus Christi, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3626219.

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<p> The first year of college is a critical period of transition for incoming college students. Learning communities have been identified as an approach to link students together in courses that are intentionally integrated and designed with first-year students' needs in mind. Yet, learning community teaching teams are often not provided with data prior to the start of the semester about their students in order to target interventions. Also, it remains unclear as to which students are most benefitted by participating in learning communities. One question then becomes, what variables known on o
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Aslan, Yasemin. "Which Method Gives The Best Forecast For Longitudinal Binary Response Data?: A Simulation Study." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612582/index.pdf.

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Panel data, also known as longitudinal data, are composed of repeated measurements taken from the same subject over different time points. Although it is generally used in time series applications, forecasting can also be used in panel data due to its time dimension. However, there is limited number of studies in this area in the literature. In this thesis, forecasting is studied for panel data with binary response because of its increasing importance and increasing fundamental roles. A simulation study is held to compare the efficiency of different methods and to find the one that gives the o
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Katta, Vanishravan. "Development of Crash Severity Model for Predicting Risk Factors in Work Zones for Ohio." University of Toledo / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1384556981.

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Aphane, Mogau Marvin. "Small-scale mango farmers, transaction costs and changing agro-food markets: evidence from Vhembe and Mopani districts, Limpopo Province." Thesis, University of the Western Cape, 2011. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_7333_1365584421.

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<p>The main objective of this study was to identify ways in which transaction costs can be lowered to improve small-scale farmers&rsquo<br>participation in and returns from agricultural output markets, with specific reference to small-scale mango farmers in Limpopo province. This study hypothesizes that transaction costs are lower in informal spot markets and increase when small-scale farmers sell in more structured markets (formal markets). This study builds on transaction cost economics (TCE) to demonstrate how to overcome transaction cost barriers that small-scale mango farmers face in the
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Books on the topic "Binary Logistic Regression Model"

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Loftsgaarden, Don O. Constructing and testing logistic regression models for binary data: Applications to the national fire danger rating system. U.S. Dept. of Agriculture, Forest Service, Intermountain Research Station, 1992.

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Loftsgaarden, Don O. Constructing and testing logistic regression models for binary data: Applications to the National Fire Danger Rating System. U.S. Dept. of Agriculture, Forest Service, Intermountain Research Station, 1992.

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Houston, Walter M. Empirical Bayes estimates of parameters from the logistic regression model. ACT, Inc., 1997.

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Houston, Walter M. Empirical Bayes estimates of parameters from the logistic regression model. ACT, Inc., 1997.

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Agnihotri, Arpita, and Saurabh Bhattacharya. A Global Strategy at a Manufacturing Start-Up: Penetrating the International Market Using Binary Logistic Regression and the CAGE Distance Framework. Edited by Bruce Alan Walters. SAGE Publications, Inc., 2024. http://dx.doi.org/10.4135/9781071961094.

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Some aspects of statistical inference of logistic regression model parameters. Acta Universitatis Upsaliensis, 1996.

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Publishing, AI. Regression Models With Python For Beginners: Theory and Applications of Linear Models and Logistic Model with python from Scratch. AI Publishing, 2020.

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Thompson, Norris B., and SreyRam Kuy. Multivariable Predictors of Postoperative Surgical Site Infection after General and Vascular Surgery. Edited by SreyRam Kuy. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199384075.003.0013.

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This landmark study proposed a model for predicting surgical site infections (SSI). Using logistic regression analysis, variables independently associated with increased risk of SSI were identified, which included smoking, alcohol use, comorbidities, disseminated cancer, weight loss greater than 10%, emergency surgery, and length of operative time. This chapter describes the basics of the study, including funding, year study began, year study was published, study location, who was studied, who was excluded, how many patients, study design, study intervention, follow-up, endpoints, results, and
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Book chapters on the topic "Binary Logistic Regression Model"

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Wilson, Jeffrey R., and Kent A. Lorenz. "Standard Binary Logistic Regression Model." In ICSA Book Series in Statistics. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23805-0_3.

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Wilson, Jeffrey R., Kent A. Lorenz, and Lori P. Selby. "Standard Binary Logistic Regression Model." In ICSA Book Series in Statistics. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-62427-8_3.

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Dobson, Annette J. "Binary variables and logistic regression." In An Introduction to Generalized Linear Models. Springer US, 1990. http://dx.doi.org/10.1007/978-1-4899-7252-1_8.

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Gallo, Michael A., and Ulreen O. McKinney. "Other Regression Models: Mediation and Binary Logistic Regression." In Applied Multiple Regression/Correlation Analysis for Aviation Research. Routledge, 2025. https://doi.org/10.4324/9781003506935-14.

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Harrell, Frank E. "Case Study in Binary Logistic Regression, Model Selection and Approximation: Predicting Cause of Death." In Regression Modeling Strategies. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19425-7_11.

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Wilson, Jeffrey R., Ding-Geng Chen, and Karl E. Peace. "Modeling Correlated Binary Outcomes Through Hierarchical Logistic Regression Models." In Statistical Analytics for Health Data Science with SAS and R. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003315674-11.

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West, Brady T., Steven G. Heeringa, and Patricia A. Berglund. "Logistic Regression and Generalized Linear Models for Binary Survey Variables." In Applied Survey Data Analysis, 3rd ed. Chapman and Hall/CRC, 2025. https://doi.org/10.1201/9781003472094-9.

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Rebolledo, Jonathan, and Roman Rodriguez-Aguilar. "K-Modes with Binary Logistic Regression: An Application in Marketing Research." In Intelligent Computing and Optimization. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-50158-6_6.

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Jaff, Mootaz M., and Abdul Azeez Kadar Hamsa. "Evaluating the Ability to Telecommute by the Potential Female Telecommuters by Logistic Regression Model." In Lecture Notes in Mobility. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-85578-8_44.

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Abstract This paper evaluates the ability of potential female employees to telecommute in the selected business organizations in Kuala Lumpur, Malaysia. The ability to telecommute by the potential female employees was determined by targeting few employment types namely clerical, professional, associate professional and manager through a pre-designed questionnaire survey. A binary logistic regression model was developed to evaluate the effects of the explanatory variables on the ability to telecommute by controlling and not controlling the “employing industries” and “employment categories”. The
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Amudha, S., A. Alice Nithya, J. Praveen Kumar, S. Shivram Prasadh, and M. Kavin Nandha. "Classification of Toxicity in Social Media Comments Using the Binary Relevance – Logistic Regression and BERT Model." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-68905-5_28.

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Conference papers on the topic "Binary Logistic Regression Model"

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Alfardus, Asma, and Danda B. Rawat. "Binary Logistic Regression based Intrusion Detection System for CAN bus Security." In 2024 International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI). IEEE, 2024. https://doi.org/10.1109/iiki65561.2024.00033.

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Saikia, Achinta, Bipin Gogoi, Anuraj Mahanta, and Bukum Doley. "Prediction of Tuberculosis: A Logistic Regression Model." In 2025 International Conference on Machine Learning and Autonomous Systems (ICMLAS). IEEE, 2025. https://doi.org/10.1109/icmlas64557.2025.10968332.

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Chandel, Garima, Swagatika Dash, Ashmita Nag, Raja Mohammed, Bishnu Deo Kumar, and A. N. Mishra. "Heart Disease Detection using Logistic Regression Model." In 2024 International Conference on Communication, Computing and Energy Efficient Technologies (I3CEET). IEEE, 2024. https://doi.org/10.1109/i3ceet61722.2024.10994152.

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Rodriguez-Hernandez, Carlos Felipe, and Prateek Shekhar. "WIP: Exploring STEM Students' Enrollment in Entrepreneurship Education Programs: A Binary Logistic Regression Approach." In 2024 IEEE Frontiers in Education Conference (FIE). IEEE, 2024. https://doi.org/10.1109/fie61694.2024.10893025.

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Aladeitan, Benedicta B., Olukayode Adebimpe, Kayode Ayinde, Adewale Lukman, Olajumoke Oludoun, and Elizabeth Abiodun. "A Modified KL Estimator for the Binary Logistic Regression Model." In 2023 International Conference on Science, Engineering and Business for Sustainable Development Goals (SEB-SDG). IEEE, 2023. http://dx.doi.org/10.1109/seb-sdg57117.2023.10124573.

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Lavazza, Luigi, and Sandro Morasca. "Dealing with Uncertainty in Binary Logistic Regression Fault-proneness Models." In EASE '19: Evaluation and Assessment in Software Engineering. ACM, 2019. http://dx.doi.org/10.1145/3319008.3319012.

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"Analysis and Prediction of P2P Online Lending Platform—Based on Binary Logistic Regression Model." In 2017 the 7th International Workshop on Computer Science and Engineering. WCSE, 2017. http://dx.doi.org/10.18178/wcse.2017.06.224.

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Belvis, Carlo V., Claire Ann R. Javierto, Ma Bernadette O. Santos, Crizelle R. Datinggaling, and Christian Van R. Rivera. "Enhancing Breast Cancer Detection through Machine Learning: A Comparative Analysis of Logistic Regression and Linear Discriminant Analysis." In SRC. University of Bahrain, 2023. https://doi.org/10.12785/src/1570967447.

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This paper intends to analyze classification models that can predict whether breast cancer is cancerous or noncancerous through the attributes of the selected dataset with the application of machine learning algorithms. The high dimensional Breast Cancer Wisconsin (Diagnostic) Dataset is reduced through principal component analysis (PCA). Six major components were extracted with a cumulative variance of 91.063%. The selected classifier models that were evaluated were logistic regression and linear discriminant analysis (LDA). After training and testing, the model’s performance was evaluated us
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Duan, Ming-Liang. "Based on Binary Logistic Regression Model Analysis of Factors of Farmers’ Willingness to Transfer Farmland." In 2023 2nd International Conference on Big Data, Information and Computer Network (BDICN). IEEE, 2023. http://dx.doi.org/10.1109/bdicn58493.2023.00021.

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Alver, Yalcin, and Elia Odabasi. "Enhancing urban sustainable mobility: a binary logistic regression approach to modelling cyclists' behaviour." In 8th International Conference on Road and Rail Infrastructure. University of Zagreb Faculty of Civil Engineering, 2024. http://dx.doi.org/10.5592/co/cetra.2024.1563.

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Urban mobility is a critical aspect of modern city life, and the integration of sustainable transportation modes, such as cycling, has gained prominence. However, in Izmir, a vibrant city in Turkey, there are limited cycling routes, which poses a challenge for cyclists. This study aims to model the behaviour of individuals who choose to cycle in İzmir despite the lack of proper infrastructure, particularly focusing on their interaction with public transportation. To achieve this, we employ binary logistic regression to analyse the factors influencing individuals' decision to combine cycling wi
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Reports on the topic "Binary Logistic Regression Model"

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Moral, Rafael. Introduction to Generalized Linear Models. Instats Inc., 2024. http://dx.doi.org/10.61700/vteee3zjf6fsm1478.

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This seminar provides a comprehensive introduction to Generalized Linear Models (GLMs), covering binary, binomial, categorical logistic regression, Poisson regression, and advanced topics like overdispersion and zero-inflated models. Participants will gain theoretical knowledge and practical skills in applying GLMs using R, enhancing their ability to perform rigorous statistical analyses in various research scenarios.
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Arhin, Stephen, Babin Manandhar, and Adam Gatiba. Influence of Pavement Conditions on Commercial Motor Vehicle Crashes. Mineta Transportation Institute, 2023. http://dx.doi.org/10.31979/mti.2023.2343.

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Commercial motor vehicle (CMV) safety is a major concern in the United States, including the District of Columbia (DC), where CMVs make up 15% of traffic. This research uses a comprehensive approach, combining statistical analysis and machine learning techniques, to investigate the impact of road pavement conditions on CMV accidents. The study integrates traffic crash data from the Traffic Accident Reporting and Analysis Systems Version 2.0 (TARAS2) database with pavement condition data provided by the District Department of Transportation (DDOT). Data spanning from 2016 to 2020 was collected
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Lubowa, Nasser, Zita Ekeocha, Stephen Robert Byrn, and Kari L. Clase. Pharmaceutical Industry in Uganda: A Review of the Common GMP Non-conformances during Regulatory Inspections. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317442.

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The prevalence of substandard medicines in Africa is high but not well documented. Low and Middle-Income Countries (LMICs) are likely to face considerable challenges with substandard medications. Africa faces inadequate drug regulatory practices, and in general, compliance with Good Manufacturing Practices (GMP) in most of the pharmaceutical industries is lacking. The majority of pharmaceutical manufacturers in developing countries are often overwhelmed by the GMP requirements and therefore are unable to operate in line with internationally acceptable standards. Non-conformances observed durin
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Alwan, Iktimal, Dennis D. Spencer, and Rafeed Alkawadri. Comparison of Machine Learning Algorithms in Sensorimotor Functional Mapping. Progress in Neurobiology, 2023. http://dx.doi.org/10.60124/j.pneuro.2023.30.03.

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Objective: To compare the performance of popular machine learning algorithms (ML) in mapping the sensorimotor cortex (SM) and identifying the anterior lip of the central sulcus (CS). Methods: We evaluated support vector machines (SVMs), random forest (RF), decision trees (DT), single layer perceptron (SLP), and multilayer perceptron (MLP) against standard logistic regression (LR) to identify the SM cortex employing validated features from six-minute of NREM sleep icEEG data and applying standard common hyperparameters and 10-fold cross-validation. Each algorithm was tested using vetted feature
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Meloncelli, Daniel. Regression Analysis using SPSS. Instats Inc., 2025. https://doi.org/10.61700/9zohmz8j1gzcj1476.

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This seminar provides a comprehensive introduction to regression analysis using SPSS, equipping researchers with the skills to apply linear and logistic regression techniques in their work. Participants will gain practical experience in model fitting, assumption checking, and interpreting results, enhancing their ability to leverage data effectively in their respective fields.
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Meloncelli, Daniel. Regression Analysis using R. Instats Inc., 2025. https://doi.org/10.61700/j3s1r521de2ee1472.

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This seminar provides a comprehensive introduction to regression analysis using R. Participants will explore the fundamentals of correlation, simple linear regression, multiple regression, and logistic regression. The seminar emphasises practical application, guiding attendees through data preparation, model fitting, assumption checking, and interpretation of results. Hands-on sessions will enable participants to apply regression techniques to real-world datasets, enhancing their analytical skills for research purposes.
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Taylor, Bea, Heather Wardle, and Isabel Taylor. Exploring the problem gambling health-harm paradox. Greo Evidence Insights, 2022. https://doi.org/10.33684/2024.002.

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Purpose: Previous research by NatCen identified a potential health-harm paradox for mental wellbeing and gambling, finding that those with poor mental wellbeing or a diagnosed mental health condition were more likely to experience problem gambling despite being less likely to gamble at all. This report aimed to explore this further, testing three specific hypothesis which could account for this association: 1. That people with poorer mental wellbeing who gamble do so more frequently and it is this increased frequency of gambling that drives elevated rates of gambling severity. 2. That people w
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Fessel, Kimberly. Machine Learning Essentials (Free Seminar). Instats Inc., 2024. http://dx.doi.org/10.61700/l6x4izy1bov9p1764.

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This comprehensive one-hour seminar provides PhD students, academics, and professional researchers with fundamental insights into machine learning concepts, crucial for modern data analysis in many disciplines. Led by data science expert Dr Kimberly Fessel, participants will explore key topics such as supervised and unsupervised learning, model performance (under- vs. overfitting), and popular algorithms like linear and logistic regression, decision trees, and neural networks.
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ศุภศันสนีย์, ประพิม, та สุชาดา รัชชุกูล. ความสัมพันธ์ระหว่างลักษณะทางสังคมกับการป่วยด้วยโรคเรื้อรังและไม่ติดต่อ. จุฬาลงกรณ์มหาวิทยาลัย, 2002. https://doi.org/10.58837/chula.res.2002.22.

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การศึกษาย้อนหลังจากผลไปหาเหตุ (Case-control study) ครั้งนี้ มีวัตถุประสงค์เพื่อหาตัวชี้วัดลักษณะทางสังคมของบุคคลที่ป่วยด้วยโรคเรื้อรังและไม่ติดต่อ และหาความสัมพันธ์ระหว่างลักษณะทางสังคมของบุคคลที่ป่วยด้วยโรคเรื้อรังและไม่ติดต่อกับบุคคลที่มีสุขภาพดี กลุ่มตัวอย่างแบ่งเป็น 2 กลุ่มคือ กลุ่มศึกษา (case) เป็นผู้ป่วยโรคหลอดเลือดหัวใจ 198 ราย ผู้ป่วยโรคหลอดเลือดสมอง 198 ราย และกลุ่มควบคุม (control) เป็นบุคคลที่มีสุขภาพดีจำนวน 202 ราย โดยสุ่มตัวอย่างหลายขั้นตอนในโรงพยาบาล 11 แห่ง เครื่องมือที่ใช้ในการศึกษาเป็นแบบสอบถาม ซึ่งมีค่าความเที่ยงเท่ากับ .80 วิเคราะห์ข้อมูลโดยใช้สถิติความถี่ ค่าร้อยละ และการวิเ
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de Leon, Mark Jerome, Chirbet Ayunon, Maria Benita Balagan, and Kristinne Joyce Lara. Strengthening Tertiary Enrollment through Financial Aid: Insights from a Survey in Cagayan Valley. Philippine Institute for Development Studies, 2025. https://doi.org/10.62986/dp2025.11.

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This study examines how financial aid shapes post-secondary enrollment and persistence among senior high school graduates, challenging assumptions in traditional human capital theory. While the theory posits that reducing financial barriers increases participation, findings suggest a more complex process. Drawing on survey data from the Batch 2024 graduates in Cagayan Valley, the study traces student trajectories across three points: college application (Wave 1), first-semester enrollment (Wave 2), and second-semester persistence (Wave 3). Guided by St. John’s (1992) framework, the analysis em
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