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Journal articles on the topic 'Logistic regression analysis'

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Published: 4 June 2021

Last updated: 25 July 2025

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1

Jessen, Hans Christian, and S. Menard. "Applied Logistic Regression Analysis." Statistician 45, no. 4 (1996): 534. http://dx.doi.org/10.2307/2988559.

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2

Kilic, Selim. "Binary logistic regression analysis." Journal of Mood Disorders 5, no. 4 (2015): 191. http://dx.doi.org/10.5455/jmood.20151202122141.

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3

Ziegel, Eric R., and Scott Menard. "Applied Logistic Regression Analysis." Technometrics 38, no. 2 (1996): 192. http://dx.doi.org/10.2307/1270433.

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4

YANG, MIIN-SHEN, and HWEI-MING CHEN. "FUZZY CLASS LOGISTIC REGRESSION ANALYSIS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 12, no. 06 (2004): 761–80. http://dx.doi.org/10.1142/s0218488504003193.

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Distribution mixtures are used as models to analyze grouped data. The estimation of parameters is an important step for mixture distributions. The latent class model is generally used as the analysis of mixture distributions for discrete data. In this paper, we consider the parameter estimation for a mixture of logistic regression models. We know that the expectation maximization (EM) algorithm was most used for estimating the parameters of logistic regression mixture models. In this paper, we propose a new type of fuzzy class model and then derive an algorithm for the parameter estimation of

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5

Tripepi, G., K. J. Jager, F. W. Dekker, and C. Zoccali. "Linear and logistic regression analysis." Kidney International 73, no. 7 (2008): 806–10. http://dx.doi.org/10.1038/sj.ki.5002787.

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6

ABBOTT, ROBERT D. "LOGISTIC REGRESSION IN SURVIVAL ANALYSIS." American Journal of Epidemiology 121, no. 3 (1985): 465–71. http://dx.doi.org/10.1093/oxfordjournals.aje.a114019.

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7

Shott, S. "Logistic regression and discriminant analysis." Journal of the American Veterinary Medical Association 198, no. 11 (1991): 1902–5. http://dx.doi.org/10.2460/javma.1991.198.11.1902.

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8

Wu, Ju. "Study Applicable for Multi-Linear Regression Analysis and Logistic Regression Analysis." Open Electrical & Electronic Engineering Journal 8, no. 1 (2014): 782–86. http://dx.doi.org/10.2174/1874129001408010782.

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Current study focus on using method of multi-linear regression analysis and logistic regression analysis, and discuss about the condition and scope of multi-linear regression analysis and logistic regression analysis. A modeling method has been introduced keeping in the basic principles of multi-linear regression analysis and logistic regression analysis. The modeling method and two forms of analytic methods have been analyzed, based on two clinic test data of diabetes and Model-2 diabetes as objects of study in combination with the analytic methods of multi-linear regression and logistic regr

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9

Qiu, Qixuan. "Based on Bayesian Regression and Logistic Regression Model Forecast Logistics Transportation Delay." Applied and Computational Engineering 135, no. 1 (2025): 175–83. https://doi.org/10.54254/2755-2721/2025.21217.

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In the logistics supply chain transportation, it is inevitable to encounter some unpredictable risks, these risks will greatly affect the accuracy and timeliness of logistics transportation, in order to explore the problem of logistics transport delays, this article uses Bayesian regression and logistic regression to establish a model, by analyzing the relationship between the journey risk and the logistics transport delays, Bayesian regression through Mean Squared Error(MSE) and R carry out a test, logistic regression through Accuracy, Receiver Operating Characteristic Curve (ROC Curve) and P

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10

Valton, Kamberaj. "CATEGORICAL DATA ANALYSIS USING LOGISTIC REGRESSION." https://link.springer.com/journal/10898 3 (September 7, 2021): 11. https://doi.org/10.5281/zenodo.5534800.

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The categorized data that will be analyzed in this paper will be of the type that will use the logistic regression method. The rest of the paper will focus on logistic regression and then the combination of these in the title of the topic mentioned above. Logistic regression is a technique widely used for categorical data analysis, offering increased flexibility compared to traditional intersection analysis. A binary result can be predicted using one or more categorical variables, continuous variables or combinations.

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11

Guns, M., and V. Vanacker. "Logistic regression applied to natural hazards: rare event logistic regression with replications." Natural Hazards and Earth System Sciences 12, no. 6 (2012): 1937–47. http://dx.doi.org/10.5194/nhess-12-1937-2012.

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Abstract. Statistical analysis of natural hazards needs particular attention, as most of these phenomena are rare events. This study shows that the ordinary rare event logistic regression, as it is now commonly used in geomorphologic studies, does not always lead to a robust detection of controlling factors, as the results can be strongly sample-dependent. In this paper, we introduce some concepts of Monte Carlo simulations in rare event logistic regression. This technique, so-called rare event logistic regression with replications, combines the strength of probabilistic and statistical method

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12

Steyerberg, Ewout W., Marinus J. C. Eijkemans, Frank E. Harrell, and J. Dik F. Habbema. "Prognostic Modeling with Logistic Regression Analysis." Medical Decision Making 21, no. 1 (2001): 45–56. http://dx.doi.org/10.1177/0272989x0102100106.

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13

Garner, J. B. "RE: “LOGISTIC REGRESSION IN SURVIVAL ANALYSIS”." American Journal of Epidemiology 123, no. 3 (1986): 557. http://dx.doi.org/10.1093/oxfordjournals.aje.a114275.

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14

Abbott, Robert D. "RE: “LOGISTIC REGRESSION IN SURVIVAL ANALYSIS”." American Journal of Epidemiology 124, no. 5 (1986): 864. http://dx.doi.org/10.1093/oxfordjournals.aje.a114465.

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15

Bach, Francis. "Self-concordant analysis for logistic regression." Electronic Journal of Statistics 4 (2010): 384–414. http://dx.doi.org/10.1214/09-ejs521.

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16

Huang, Ying, Margaret S. Pepe, and Ziding Feng. "Logistic regression analysis with standardized markers." Annals of Applied Statistics 7, no. 3 (2013): 1640–62. http://dx.doi.org/10.1214/13-aoas634.

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17

Amores-Ampuero, Anabel, and Inmaculada Alemán. "Comparison of cranial sex determination by discriminant analysis and logistic regression." Anthropologischer Anzeiger 73, no. 3 (2016): 207–14. http://dx.doi.org/10.1127/anthranz/2016/0604.

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18

Gao, Jinling, and Zengtai Gong. "Uncertain logistic regression models." AIMS Mathematics 9, no. 5 (2024): 10478–93. http://dx.doi.org/10.3934/math.2024512.

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<abstract><p>Logistic regression is a generalized nonlinear regression analysis model and is often used for data mining, automatic disease diagnosis, economic prediction, and other fields. In this paper, we first aimed to introduce the concept of uncertain logistic regression based on the uncertainty theory, as well as investigating the likelihood function in the sense of uncertain measure to represent the likelihood of unknown parameters.</p></abstract>

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19

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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20

Huskova, V. G., and P. I. Bidyuk. "CREDIT ANALYSIS OF BORROWERS USING LOGISTIC REGRESSION." Naukovi praci Donec'kogo nacional'nogo tehnicnogo universitetu. Seria, Informatika, kibernetika i obcisluval'na tehnika 2, no. 25 (2017): 54–59. http://dx.doi.org/10.31474/1996-1588-2017-2-25-54-59.

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21

Pyke, Sandra W., and Peter M. Sheridan. "Logistic Regression Analysis of Graduate Student Retention." Canadian Journal of Higher Education 23, no. 2 (1993): 44–64. http://dx.doi.org/10.47678/cjhe.v23i2.183161.

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Logistic regression analysis was utilized to predict the retention of 477 master's and 124 doctoral candidates at a large Canadian university. Selected demographic (e.g., sex, marital status, age, citizenship), academic (e.g., GPA, discipline, type of study, time to degree completion) and financial support variables (e.g., funding received from internal and external scholarships and from research, graduate and teaching assistantships) were used as independent variables. The dichotomous dependent variable was whether the student successful- ly completed the degree. Results for master's students

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22

Hill, L. Robert, William G. Hammond, and John R. Benfield. "Logistic Regression Analysis in Experimental Bronchial Carcinogenesis." American Statistician 45, no. 3 (1991): 184. http://dx.doi.org/10.2307/2684287.

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23

Sebastian, Helen, and Rupali Wagh. "Churn Analysis in Telecommunication Using Logistic Regression." Oriental journal of computer science and technology 10, no. 1 (2017): 207–12. http://dx.doi.org/10.13005/ojcst/10.01.28.

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Since the beginning of data mining the discovery of knowledge from the Databases has been carried out to solve various problems and has helped the business come up with practical solutions. Large companies are behind improving revenue due to the increase loss in customers. The process where one customer leaves one company and joins another is called as churn. This paper will be discussing how to predict the customers that might churn, R package is being used to do the prediction. R package helps represent large dataset churn in the form of graphs which will help to depict the outcome in the fo

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24

Maalouf, Maher. "Logistic regression in data analysis: an overview." International Journal of Data Analysis Techniques and Strategies 3, no. 3 (2011): 281. http://dx.doi.org/10.1504/ijdats.2011.041335.

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25

Gleicher, David, and Lonnie K. Stevans. "Who Survived Titanic? A Logistic Regression Analysis." International Journal of Maritime History 16, no. 2 (2004): 61–94. http://dx.doi.org/10.1177/084387140401600205.

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26

Liao, Taobo. "Logistic regression-based side-channel analysis attacks." Theoretical and Natural Science 18, no. 1 (2023): 216–23. http://dx.doi.org/10.54254/2753-8818/18/20230394.

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Advanced Encryption Standard (AES) is a modern concept in cryptography. Most of modern encryption schemes and devises are built based on this standard. Those encryption systems have really high resistance to most of modern attacking methods. However, this passage will introduce the most powerful way of attacking: Side-Channel Analysis (SCA). By performing such attacking by artificial intelligence model, it shows that they can break the encryption system in a very efficient and effective way. By using ASCAD database, this passage analysis some properties when using logistic regression to perfor

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27

Ranganathan, Priya, CS Pramesh, and Rakesh Aggarwal. "Common pitfalls in statistical analysis: Logistic regression." Perspectives in Clinical Research 8, no. 3 (2017): 148. http://dx.doi.org/10.4103/picr.picr_87_17.

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28

Hill, L. Robert, William G. Hammond, and John R. Benfield. "Logistic Regression Analysis in Experimental Bronchial Carcinogenesis." American Statistician 45, no. 3 (1991): 184–86. http://dx.doi.org/10.1080/00031305.1991.10475799.

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29

Lu, Wei, and James M. Bailey. "Reliability of Pharmacodynamic Analysis by Logistic Regression." Anesthesiology 92, no. 4 (2000): 985–92. http://dx.doi.org/10.1097/00000542-200004000-00015.

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Background Many pharmacologic studies record data as binary yes-or-no variables, and analysis is performed using logistic regression. This study investigates the accuracy of estimation of the drug concentration associated with a 50% probability of drug effect (C50) and the term describing the steepness of the concentration-effect relation (gamma). Methods The authors developed a technique for simulating pharmacodynamic studies with binary yes-or-no responses. Simulations were conducted assuming either that each data point was derived from the same patient or that data were pooled from multiple

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30

Lu, Wei, James G. Ramsay, and James M. Bailey. "Reliability of Pharmacodynamic Analysis by Logistic Regression." Anesthesiology 99, no. 6 (2003): 1255–62. http://dx.doi.org/10.1097/00000542-200312000-00005.

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Background Many pharmacologic studies record data as binary, yes-or-no, variables with analysis using logistic regression. In a previous study, it was shown that estimates of C50, the drug concentration associated with a 50% probability of drug effect, were unbiased, whereas estimates of gamma, the term describing the steepness of the concentration-effect relationship, were biased when sparse data were naively pooled for analysis. In this study, it was determined whether mixed-effects analysis improved the accuracy of parameter estimation. Methods Pharmacodynamic studies with binary, yes-or-no

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31

Donnelly, Seamus, and Jay Verkuilen. "Empirical logit analysis is not logistic regression." Journal of Memory and Language 94 (June 2017): 28–42. http://dx.doi.org/10.1016/j.jml.2016.10.005.

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32

Acharya, Ashith B., Sudeendra Prabhu, and Mahadevayya V. Muddapur. "Odontometric sex assessment from logistic regression analysis." International Journal of Legal Medicine 125, no. 2 (2010): 199–204. http://dx.doi.org/10.1007/s00414-010-0417-9.

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33

Geraghty, Dermot, and Margaret O’Mahony. "Urban Noise Analysis Using Multinomial Logistic Regression." Journal of Transportation Engineering 142, no. 6 (2016): 04016020. http://dx.doi.org/10.1061/(asce)te.1943-5436.0000843.

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34

Lemeshow, Stanley, and David W. Hosmer. "Logistic Regression Analysis: Applications to Ophthalmic Research." American Journal of Ophthalmology 147, no. 5 (2009): 766–67. http://dx.doi.org/10.1016/j.ajo.2008.07.042.

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35

ROBERTS, G., N. K. RAO, and S. KUMAR. "Logistic regression analysis of sample survey data." Biometrika 74, no. 1 (1987): 1–12. http://dx.doi.org/10.1093/biomet/74.1.1.

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36

Peng, Chao-Ying Joanne, and Tak-Shing Harry So. "Logistic Regression Analysis and Reporting: A Primer." Understanding Statistics 1, no. 1 (2002): 31–70. http://dx.doi.org/10.1207/s15328031us0101_04.

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37

Fleiss, Joseph L., Janet B. W. Williams, and Alan F. Dubro. "The logistic regression analysis of psychiatric data." Journal of Psychiatric Research 20, no. 3 (1986): 195–209. http://dx.doi.org/10.1016/0022-3956(86)90003-8.

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38

Lu, Kaifeng. "On logistic regression analysis of dichotomized responses." Pharmaceutical Statistics 16, no. 1 (2016): 55–63. http://dx.doi.org/10.1002/pst.1777.

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39

Lawson, Cathy, and Douglas C. Montgomery. "Logistic Regression Analysis of Customer Satisfaction Data." Quality and Reliability Engineering International 22, no. 8 (2006): 971–84. http://dx.doi.org/10.1002/qre.775.

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40

Wen, Zilu, Jinyu Liu, and Chenxi Liu. "Football Momentum Analysis based on Logistic Regression." Frontiers in Computing and Intelligent Systems 7, no. 2 (2024): 60–64. http://dx.doi.org/10.54097/jbsh1q88.

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In tennis, momentum is pivotal and can be quantified using metrics like Consecutive Win Rate (CWR), Unforced Error Rate (UER), Break Point Save Rate (BPSR), and Fatigue Factor (FF). Each metric provides insight into a player's performance and state during a match. CWR is a clear momentum indicator, reflecting a player's game dominance, while UER highlights potential lapses in concentration or physical condition. BPSR evaluates a player's clutch performance in critical situations, and FF gauges physical exertion. Utilizing logistic regression, we can predict a player's probability to win at any

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41

Kailash, Alle. "Logistic Regression for Predictive Modeling." Journal of Scientific and Engineering Research 8, no. 9 (2019): 307–14. https://doi.org/10.5281/zenodo.13347981.

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Customer retention, loyalty measurement, and recovery strategies have become crucial for businesses aiming to minimize client loss. Instead of focusing solely on acquiring new customers, companies now prioritize preventing the loss of existing ones. The telecommunications industry, with its rapid technological advancements and growing user base, generates vast amounts of data. However, this rapid and uncontrolled expansion leads to significant losses due to fraud and technical issues, necessitating the development of new analytical methodologies. This paper addresses the urgent need for effect

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42

Tian, Yiqing, Howard D. Bondell, and Alyson Wilson. "Bayesian variable selection for logistic regression." Statistical Analysis and Data Mining: The ASA Data Science Journal 12, no. 5 (2019): 378–93. http://dx.doi.org/10.1002/sam.11428.

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43

Wu Fung, Samy, Sanna Tyrväinen, Lars Ruthotto, and Eldad Haber. "ADMM-Softmax: an ADMM approach for multinomial logistic regression." ETNA - Electronic Transactions on Numerical Analysis 52 (2020): 214–29. http://dx.doi.org/10.1553/etna_vol52s214.

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44

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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45

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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46

Solanki, Anuj. "Real-Time Extracted Hotel Reviews Analysis Using Logistic Regression." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48602.

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Abstract— In this study, we focus on real-time analysis of hotel reviews using logistic regression. The reviews are directly extracted from Google through web scraping, utilizing Selenium in headless browser mode. Once collected, the reviews undergo preprocessing steps including tokenization, stop word removal, and TF-IDF vectorization. The cleaned and vectorized data is then used to train a logistic regression model, which classifies the reviews as either positive or negative. The classification results are displayed in real-time through a Streamlit-based user interface, enabling users to ins

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47

YILDIZ, Ayse. "Determining the factors for individual credit approval by applying logistic regression and hierarchical logistic regression." International Journal of Management Studies and Social Science Research 05, no. 06 (2023): 58–67. http://dx.doi.org/10.56293/ijmsssr.2023.4705.

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There has been a rapid increase in applications made to lending institutions due to the inadequacy of individuals' savings to meet their growing demands and needs. However, specific criteria are expected from individuals for the approval of these applications. Various methods have been developed to accurately determine these criteria and facilitate the loan approval process efficiently and promptly. Despite being the most used method, binary logistic regression has its limitations. These include the simultaneous inclusion of all variables in the analysis, the oversight of control variables tha

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48

AKSU, Gokhan, and Cigdem REYHANLIOGLU KECEOGLU. "Comparison of Results Obtained from Logistic Regression, CHAID Analysis and Decision Tree Methods." Eurasian Journal of Educational Research 19, no. 84 (2019): 1–20. http://dx.doi.org/10.14689/ejer.2019.84.6.

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49

Bansal, Ankit. "Direct Marketing Campaign Response Analysis using Logistic Regression, CART and Support Vector Machines." International Journal of Science and Research (IJSR) 10, no. 9 (2021): 1808–13. http://dx.doi.org/10.21275/sr21917102249.

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50

Bhavani, Gottemukkula, Sreenivasulu M, Ravinder Naik.V, Vidya Sagar G. E. Ch, and G. E. Ch. "Success determinants of knowledge gain in quality seed production using logistic regression analysis." Agriculture Association of Textile Chemical and Critical Reviews 13, no. 1 (2025): 413–16. https://doi.org/10.21276/aatccreview.2025.13.01.412.

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