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Journal articles on the topic 'Logistic regression analysis'

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Published: 4 June 2021
Last updated: 31 July 2024

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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 (May 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 (December 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 a fuzzy class logistic regression model. The effects of the explanatory variables on the response variables are described. The focus is on binary responses for the logistic regression mixture analysis with a fuzzy class model. An algorithm, called a fuzzy classification maximum likelihood (FCML), is then created. The mean squared error (MSE) based accuracy criterion for the FCML and EM algorithms to the parameter estimation of logistic regression mixture models are compared using the samples drawn from logistic regression mixtures of two classes. Numerical results show that the proposed FCML algorithm presents good accuracy and is recommended as a new tool for the parameter estimation of the logistic regression mixture models.
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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 (April 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 (March 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 (June 1, 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 (December 31, 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 regression. Analysis result indicate that glycosylated hemoglobin, glycerin trilaurate, total cholesterol of serum and blood sugar concentration present obvious positive relation (P < 0.05), whereas insulin and blood sugar present negative relation(P < 0.05); body mass index (BMI) and relative factors are dangerous; physical excise and relative factors are protective. In conclusion, multi-linear regression analysis and logistic regression analysis respectively have their own emphasis; for example, multi-linear regression analysis emphasizes on analyzing linear dependent relation with an dependent variable and multiple independent variables, whereas logistic regression analysis emphasizes on analyzing the relation between probability of occurring an incident and independent variables.
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9

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 (June 18, 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 methods, and allows overcoming some of the limitations of previous developments through robust variable selection. This technique was here developed for the analyses of landslide controlling factors, but the concept is widely applicable for statistical analyses of natural hazards.
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10

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 (February 2001): 45–56. http://dx.doi.org/10.1177/0272989x0102100106.

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11

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

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12

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

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13

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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14

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

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15

Amores-Ampuero, Anabel, and Inmaculada Alemán. "Comparison of cranial sex determination by discriminant analysis and logistic regression." Anthropologischer Anzeiger 73, no. 3 (September 1, 2016): 207–14. http://dx.doi.org/10.1127/anthranz/2016/0604.

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16

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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17

Midi, Habshah, S. K. Sarkar, and Sohel Rana. "Collinearity diagnostics of binary logistic regression model." Journal of Interdisciplinary Mathematics 13, no. 3 (June 2010): 253–67. http://dx.doi.org/10.1080/09720502.2010.10700699.

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18

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 (December 2017): 54–59. http://dx.doi.org/10.31474/1996-1588-2017-2-25-54-59.

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19

Pyke, Sandra W., and Peter M. Sheridan. "Logistic Regression Analysis of Graduate Student Retention." Canadian Journal of Higher Education 23, no. 2 (August 31, 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 indicate that higher graduate GPAs, increased length of time in the program, increased funding from all sources, full- or part-time registration status in the coursework only program, and full-time registration status in the coursework plus major research paper program significantly improve the student's chances of graduating with the degree. For doctoral candidates, only increased length of time in the program and increased funding from all sources significantly increase the chances of graduating with the doctorate.
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20

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

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21

Sebastian, Helen, and Rupali Wagh. "Churn Analysis in Telecommunication Using Logistic Regression." Oriental journal of computer science and technology 10, no. 1 (March 24, 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 form of various data visualizations. Churn is a very important area in which the telecom domain can make or lose their customers and hence the business/industry spends a lot of time doing predictions, which in turn helps to make the necessary business conclusions. Churn can be avoided by studying the past history of the customers. Logistic Regression is been used to make necessary analysis. To proceed with logistic regression we must first eliminate the outliers that are present, this has be achieved by cleaning the data (for redundancy, false data etc) and the resultant has been populated into a prediction excel using which the analysis has been performed.
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22

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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23

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

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24

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

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25

Lu, Wei, and James M. Bailey. "Reliability of Pharmacodynamic Analysis by Logistic Regression." Anesthesiology 92, no. 4 (April 1, 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 patients in a population with log-normal distributions of C50 and gamma. Coefficients of variation were calculated. The authors also determined the percentage of simulations in which the 95% confidence intervals contained the true parameter value. Results The coefficient of variation of parameter estimates decreased with increasing n and gamma. The 95% confidence intervals for C50 estimation contained the true parameter value in more than 90% of the simulations. However, the 95% confidence intervals of gamma did not contain the true value in a substantial number of simulations of data from multiple patients. Conclusion The coefficient of variation of parameter estimates may be as large as 40-50% for small studies (n &lt; or = 20). The 95% confidence intervals of C50 almost always contain the true value, underscoring the need for always reporting confidence intervals. However, when data from multiple patients is naively pooled, the estimates of gamma may be biased, and the 95% confidence intervals may not contain the true value.
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26

Lu, Wei, James G. Ramsay, and James M. Bailey. "Reliability of Pharmacodynamic Analysis by Logistic Regression." Anesthesiology 99, no. 6 (December 1, 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, responses were simulated and analyzed with NONMEM. The bias and coefficient of variation of C50 and gamma estimates were determined as a function of numbers of patients in the simulated study, the number of simulated data points per patient, and the "true" value of gamma. In addition, 100 sparse binary human data sets were generated from an evaluation of midazolam for postoperative sedation of adult patients undergoing cardiac surgery by random selection of a single data point (sedation score vs. midazolam plasma concentration) from each of the 30 patients in the study. C50 and gamma were estimated for each of these data sets by using NONMEM and were compared with the estimates from the complete data set of 656 observations. Results Estimates of C50 were unbiased, even for sparse data (one data point per patient) with coefficients of variation of 30-50%. Estimates of gamma were highly biased for sparse data for all values of gamma greater than 1, and the value of gamma was overestimated. Unbiased estimation of gamma required 10 data points per patient. The coefficient of variation of gamma estimates was greater than that of the C50 estimates. Clinical data for sedation with midazolam confirmed the simulation results, showing an overestimate of gamma with sparse data. Conclusion Although accurate estimations of C50 from sparse binary data are possible, estimates of gamma are biased. Data with 10 or more observations per patient is necessary for accurate estimations of gamma.
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27

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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28

Acharya, Ashith B., Sudeendra Prabhu, and Mahadevayya V. Muddapur. "Odontometric sex assessment from logistic regression analysis." International Journal of Legal Medicine 125, no. 2 (January 27, 2010): 199–204. http://dx.doi.org/10.1007/s00414-010-0417-9.

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29

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

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30

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

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31

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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Peng, Chao-Ying Joanne, and Tak-Shing Harry So. "Logistic Regression Analysis and Reporting: A Primer." Understanding Statistics 1, no. 1 (February 2, 2002): 31–70. http://dx.doi.org/10.1207/s15328031us0101_04.

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33

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 (January 1986): 195–209. http://dx.doi.org/10.1016/0022-3956(86)90003-8.

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34

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

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35

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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36

Wen, Zilu, Jinyu Liu, and Chenxi Liu. "Football Momentum Analysis based on Logistic Regression." Frontiers in Computing and Intelligent Systems 7, no. 2 (March 11, 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 scoring point, incorporating these metrics as variables. The coefficients obtained from MATLAB analysis (e.g., p1_cwr at 22.73 and p2_ff at -3.26) reveal the positive or negative correlation of these factors with a player's winning chances. In the case of the "2023-wimbledon-1301" match, the logistic model's predictions showed a symmetrical distribution of win probabilities between players, suggesting a balance in momentum swings throughout the match. Initial volatility in Player 1's success rate indicated a strong start, which diminished over time, possibly due to fatigue or the opponent's improving performance. Despite the fluctuations and a period of deadlock, Player 1's consistent performance and superior win rate for most of the game secured the victory. This outcome underscores the importance of maintaining momentum and physical resilience in tennis.
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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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38

Liao, Taobo. "Logistic regression-based side-channel analysis attacks." Theoretical and Natural Science 18, no. 1 (December 8, 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 perform Side-Channel-Attack on the traces of the encryption system. Since in the original article, the authors only analysis the performance of multilayer perceptron (MLP) and convolution neural network (CNN), this passage aims to apply similar methodology to logistic regression and analyse its performance in different circumstances. Moreover, some interesting properties about logistic regression was found, and it can sometimes perform better than systems in the original passage in certain situation.
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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 (June 27, 2019): 378–93. http://dx.doi.org/10.1002/sam.11428.

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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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41

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 between on-time and late. Binary logistic regression concluded that the shorter the distance, the signing of the perfect contract, and the selection of a specific type of vehicle, the better development of the logistics level is easier to be on time. This study provides suggestions for improving management strategies, which can be used as a reference for the development of logistics companies.
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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 (December 3, 2019): 1–20. http://dx.doi.org/10.14689/ejer.2019.84.6.

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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 relevant examples. Practical guidance is also provided for dentists and dental researchers to enhance their basic understanding of binary logistic regression analysis.
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Wang, Xiaoyin. "Bayesian Relative Importance Analysis of Logistic Regression Models." Journal of Statistics Applications & Probability Letters 3, no. 2 (May 1, 2016): 53–69. http://dx.doi.org/10.18576/jsapl/030201.

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Toleva, Borislava. "ANOVA bootstrapped principal components analysis for logistic regression." Croatian Review of Economic, Business and Social Statistics 8, no. 1 (June 1, 2022): 18–31. http://dx.doi.org/10.2478/crebss-2022-0002.

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Abstract Principal components analysis (PCA) is often used as a dimensionality reduction technique. A small number of principal components is selected to be used in a classification or a regression model to boost accuracy. A central issue in the PCA is how to select the number of principal components. Existing algorithms often result in contradictions and the researcher needs to manually select the final number of principal components to be used. In this research the author proposes a novel algorithm that automatically selects the number of principal components. This is achieved based on a combination of ANOVA ranking of principal components, the bootstrap and classification models. Unlike the classical approach, the algorithm we propose improves the accuracy of the logistic regression and selects the best combination of principal components that may not necessarily be ordered. The ANOVA bootstrapped PCA classification we propose is novel as it automatically selects the number of principal components that would maximise the accuracy of the classification model.
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Pohar, Maja, Mateja Blas, and Sandra Turk. "Comparison of logistic regression and linear discriminant analysis." Advances in Methodology and Statistics 1, no. 1 (January 1, 2004): 143–61. http://dx.doi.org/10.51936/ayrt6204.

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Two of the most widely used statistical methods for analyzing categorical outcome variables are linear discriminant analysis and logistic regression. While both are appropriate for the development of linear classification models, linear discriminant analysis makes more assumptions about the underlying data. Hence, it is assumed that logistic regression is the more flexible and more robust method in case of violations of these assumptions. In this paper we consider the problem of choosing between the two methods, and set some guidelines for proper choice. The comparison between the methods is based on several measures of predictive accuracy. The performance of the methods is studied by simulations. We start with an example where all the assumptions of the linear discriminant analysis are satisfied and observe the impact of changes regarding the sample size, covariance matrix, Mahalanobis distance and direction of distance between group means. Next, we compare the robustness of the methods towards categorisation and non-normality of explanatory variables in a closely controlled way. We show that the results of LDA and LR are close whenever the normality assumptions are not too badly violated, and set some guidelines for recognizing these situations. We discuss the inappropriateness of LDA in all other cases.
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Ghandour, DinaAhmedMohamed, and MayAlawiMohamed Abdalla. "THE TRAGEDY OF TITANIC: A LOGISTIC REGRESSION ANALYSIS." International Journal of Advanced Research 5, no. 6 (June 30, 2017): 1454–65. http://dx.doi.org/10.21474/ijar01/4558.

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48

Lakshmi, Rayasam, Satya R. B. Divya, and R. Valarmathi. "Analysis of sentiment in twitter using logistic regression." International Journal of Engineering & Technology 7, no. 3.3 (June 8, 2018): 619. http://dx.doi.org/10.14419/ijet.v7i2.33.14849.

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Social Platforms such as Twitter, Facebook are not always the good places and when explored there exists a dark side to it. The main objective of this research is to identify the sentiment of a tweet in twitter and also further analyse a twitter accounts activity. Logistic regression and text blob are used to identify the sentiment of the tweets, as for the taken datasets they provided the highest accuracy when compared with other algorithms such as GaussianNB, BernoulliNB, SVM. The datasets are extracted from twitter and split into training and testing data using which the model is trained to classify the sentiments of a tweet and then the analysis of a twitter account is done.
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49

Menard, Scott. "Coefficients of Determination for Multiple Logistic Regression Analysis." American Statistician 54, no. 1 (February 2000): 17. http://dx.doi.org/10.2307/2685605.

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Zhou, Haibo, Jianwei Chen, and Jianwen Cai. "Random Effects Logistic Regression Analysis with Auxiliary Covariates." Biometrics 58, no. 2 (June 2002): 352–60. http://dx.doi.org/10.1111/j.0006-341x.2002.00352.x.

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