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Articles de revues sur le sujet « Binary dependent variable »

Auteur : Grafiati

Publié le 9 mars 2023

Mis à jour le 31 juillet 2025

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1

Fahmy, Rifqi Nur. "Determinan Keputusan Melakukan Migrasi Ulang-Alik." Efficient: Indonesian Journal of Development Economics 1, no. 3 (2018): 242–51. http://dx.doi.org/10.15294/efficient.v1i3.27869.

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The aim of this research is to analyze the influence of dependent variable of family dependent, education level, age, marital status, and distance partially to workforce’s decision to migrate from Surakarta to Karanganyar Regency. This research used binary logistic regression analysis method. The sample in this research is 100 respondents. The result of binary logistic regression model analysis in this research shows that from five independent variables, there are two variables that have significant effect on workforce’s decision to do the commuter migration that is dependent variable of famil

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Kao, Chihwa, and John F. Schnell. "Errors in variables in panel data with a binary dependent variable." Economics Letters 24, no. 1 (1987): 45–49. http://dx.doi.org/10.1016/0165-1765(87)90179-0.

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Chesher, Andrew, and Adam M. Rosen. "What Do Instrumental Variable Models Deliver with Discrete Dependent Variables?" American Economic Review 103, no. 3 (2013): 557–62. http://dx.doi.org/10.1257/aer.103.3.557.

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We compare nonparametric instrumental variables (IV) models with linear models and 2SLS methods when dependent variables are discrete. A 2SLS method can deliver a consistent estimator of a Local Average Treatment Effect but is not informative about other treatment effect parameters. The IV models set identify a range of interesting structural and treatment effect parameters. We give set identification results for a counterfactual probability and an Average Treatment Effect in a IV binary threshold crossing model. We illustrate using data on female employment and family size (employed by Joshua

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Dukalang, Hendra H. "PERBANDINGAN REGRESI LOGISTIK BINER DAN PROBIT BINER DALAM PEMODELAN TINGKAT PARTISIPASI ANGKATAN KERJA." Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi 7, no. 2 (2019): 62–70. http://dx.doi.org/10.34312/euler.v7i2.10355.

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Regression is a data analysis method used to model the relationship between one response variable and one or more predictor variables. In regression modelling, data is often used. In general, the regression model that is often used is simple or multiple regression in modelling where the response variable is quantitative data. The fundamental difference from regression models using quantitative data is the main objective is to estimate the average value of the dependent variable using certain values of the independent variable. Whereas in a regression model with a qualitative dependent variable

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Horowitz, Joel L., and N. E. Savin. "Binary Response Models: Logits, Probits and Semiparametrics." Journal of Economic Perspectives 15, no. 4 (2001): 43–56. http://dx.doi.org/10.1257/jep.15.4.43.

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A binary-response model is a mean-regression model in which the dependent variable takes only the values zero and one. This paper describes and illustrates the estimation of logit and probit binary-response models. The linear probability model is also discussed. Reasons for not using this model in applied research are explained and illustrated with data. Semiparametric and nonparametric models are also described. In contrast to logit and probit models, semi- and nonparametric models avoid the restrictive and unrealistic assumption that the analyst knows the functional form of the relation betw

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Araveeporn, Autcha. "The Higher-Order of Adaptive Lasso and Elastic Net Methods for Classification on High Dimensional Data." Mathematics 9, no. 10 (2021): 1091. http://dx.doi.org/10.3390/math9101091.

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The lasso and elastic net methods are the popular technique for parameter estimation and variable selection. Moreover, the adaptive lasso and elastic net methods use the adaptive weights on the penalty function based on the lasso and elastic net estimates. The adaptive weight is related to the power order of the estimator. Normally, these methods focus to estimate parameters in terms of linear regression models that are based on the dependent variable and independent variable as a continuous scale. In this paper, we compare the lasso and elastic net methods and the higher-order of the adaptive

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Kling, Gerhard, Charles Harvey, and Mairi Maclean. "Establishing Causal Order in Longitudinal Studies Combining Binary and Continuous Dependent Variables." Organizational Research Methods 20, no. 4 (2015): 770–99. http://dx.doi.org/10.1177/1094428115618760.

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Longitudinal studies with a mix of binary outcomes and continuous variables are common in organizational research. Selecting the dependent variable is often difficult due to conflicting theories and contradictory empirical studies. In addition, organizational researchers are confronted with methodological challenges posed by latent variables relating to observed binary outcomes and within-subject correlation. We draw on Dueker’s qualitative vector autoregression (QVAR) and Lunn, Osorio, and Whittaker’s multivariate probit model to develop a solution to these problems in the form of a qualitati

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Araveeporn, Autcha. "Comparison of Logistic Regression and Discriminant Analysis for Classification of Multicollinearity Data." WSEAS TRANSACTIONS ON MATHEMATICS 22 (February 16, 2023): 120–31. http://dx.doi.org/10.37394/23206.2023.22.15.

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The objective of this study is to concentrate on the classification method of the logistic regression and the discriminant analysis by using the simulation dataset and the liver patients as the actual data. These datasets are used the binary dependent variable depending on the correlated independent variables or called multicollinearity data. The standard classification method is logistic regression, which uses the logit function’s probability to conduct the dichotomous dependent variable. The iteration process can be solved to estimate logit function parameters and explain the relationship be

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Compton, Ryan. "A Data-Driven Approach to the Fragile Families Challenge: Prediction through Principal-Components Analysis and Random Forests." Socius: Sociological Research for a Dynamic World 5 (January 2019): 237802311881872. http://dx.doi.org/10.1177/2378023118818720.

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Sociological research typically involves exploring theoretical relationships, but the emergence of “big data” enables alternative approaches. This work shows the promise of data-driven machine-learning techniques involving feature engineering and predictive model optimization to address a sociological data challenge. The author’s group develops improved generalizable models to identify at-risk families. Principal-components analysis and decision tree modeling are used to predict six main dependent variables in the Fragile Families Challenge, successfully modeling one binary variable but no con

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Thomas, Jonathan M. "On testing the logistic assumption in binary dependent variable models." Empirical Economics 18, no. 2 (1993): 381–92. http://dx.doi.org/10.1007/bf01205409.

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de Jong, Robert M., and Tiemen Woutersen. "DYNAMIC TIME SERIES BINARY CHOICE." Econometric Theory 27, no. 4 (2011): 673–702. http://dx.doi.org/10.1017/s0266466610000472.

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This paper considers dynamic time series binary choice models. It proves near epoch dependence and strong mixing for the dynamic binary choice model with correlated errors. Using this result, it shows in a time series setting the validity of the dynamic probit likelihood procedure when lags of the dependent binary variable are used as regressors, and it establishes the asymptotic validity of Horowitz’s smoothed maximum score estimation of dynamic binary choice models with lags of the dependent variable as regressors. For the semiparametric model, the latent error is explicitly allowed to be co

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Spirling, Arthur. "Bayesian Approaches for Limited Dependent Variable Change Point Problems." Political Analysis 15, no. 4 (2007): 387–405. http://dx.doi.org/10.1093/pan/mpm022.

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Limited dependent variable (LDV) data are common in political science, and political methodologists have given much good advice on dealing with them. We review some methods for LDV “change point problems” and demonstrate the use of Bayesian approaches for count, binary, and duration-type data. Our applications are drawn from American politics, Comparative politics, and International Political Economy. We discuss the tradeoffs both philosophically and computationally. We conclude with possibilities for multiple change point work.

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Salim, Abdurrahman, and Muhammad Rijal Alfian. "Optimalisasi Regresi Logistik Menggunakan Algoritma Genetika Pada Data Klasifikasi." Jurnal Teknologi Informasi dan Terapan 6, no. 2 (2019): 50–55. http://dx.doi.org/10.25047/jtit.v6i2.109.

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Abstract— Classification on large of data, and with a variety of features or attributes often makes the law accuracy. It required a method that has immunity in such diverse data types. One of method is Logistic Regression method. Logistic Regression is one of classification method, if response variable has binary characteristic and there are many predictor variable such as combination of category and continue.Methd of Logistic Regression requires a stage selection independent variable in improving the model accuration. So it takes a good method in fixing the deficiency is Genetic Algorithm (GA

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Jiang, Wenxin, and Martin A. Tanner. "RISK MINIMIZATION FOR TIME SERIES BINARY CHOICE WITH VARIABLE SELECTION." Econometric Theory 26, no. 5 (2010): 1437–52. http://dx.doi.org/10.1017/s0266466609990636.

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This paper considers the problem of predicting binary choices by selecting from a possibly large set of candidate explanatory variables, which can include both exogenous variables and lagged dependent variables. We consider risk minimization with the risk function being the predictive classification error. We study the convergence rates of empirical risk minimization in both the frequentist and Bayesian approaches. The Bayesian treatment uses a Gibbs posterior constructed directly from the empirical risk instead of using the usual likelihood-based posterior. Therefore these approaches do not r

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HR, Titi Kurnianti, Muhammad Nadjib Bustan, and R. Ruliana. "Pemodelan Faktor-Faktor yang Mempengaruhi Jenis Kanker Payudara Menggunakan Regresi Logistik Biner (Kasus : Pasien Penderita Kanker Payudara di RSUP Dr. Wahidin Sudirohusodo tahun 2016)." VARIANSI: Journal of Statistics and Its application on Teaching and Research 1, no. 3 (2019): 40. http://dx.doi.org/10.35580/variansiunm12898.

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Abstrak Regresi logistik adalah suatu metode analisis statistik yang diterapkan untuk memodelkan variabel dependen yang memiliki dua kategori atau lebih dengan satu atau lebih variabel independen. Regresi Logistik biner merupakan suatu analisis statistika yang digunakan untuk menganalisis hubungan antara satu atau lebih peubah bebas dengan peubah respon yang bersifat biner atau dichotomous. Peubah bebas pada regresi logistik dapat berupa peubah skala kategorik maupun peubah yang skala kontinu sedangkan peubah respon berupa peubah berskala kategorik. Regresi Logistik Biner dapat diterapkan pada

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Hug, Simon. "The Effect of Misclassifications in Probit Models: Monte Carlo Simulations and Applications." Political Analysis 18, no. 1 (2010): 78–102. http://dx.doi.org/10.1093/pan/mpp033.

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The increased use of models with limited-dependent variables has allowed researchers to test important relationships in political science. Often, however, researchers employing such models fail to acknowledge that the violation of some basic assumptions has in part difference consequences in nonlinear models than in linear ones. In this paper, I demonstrate this for binary probit models in which the dependent variable is systematically miscoded. Contrary to the linear model, such misclassifications affect not only the estimate of the intercept but also those of the other coefficients. In a Mon

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Araveeporn, Autcha. "Comparing the Linear and Quadratic Discriminant Analysis of Diabetes Disease Classification Based on Data Multicollinearity." International Journal of Mathematics and Mathematical Sciences 2022 (September 6, 2022): 1–11. http://dx.doi.org/10.1155/2022/7829795.

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Linear and quadratic discriminant analysis are two fundamental classification methods used in statistical learning. Moments (MM), maximum likelihood (ML), minimum volume ellipsoids (MVE), and t-distribution methods are used to estimate the parameter of independent variables on the multivariate normal distribution in order to classify binary dependent variables. The MM and ML methods are popular and effective methods that approximate the distribution parameter and use observed data. However, the MVE and t-distribution methods focus on the resampling algorithm, a reliable tool for high resistanc

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Al-mayali, Mohammet T. Kahnger. "Bayesian Binary reciprocal LASSO quantile regression (with practical application)." Journal of Kufa for Mathematics and Computer 10, no. 1 (2023): 13–17. http://dx.doi.org/10.31642/jokmc/2018/100102.

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Quantile regression is one of the methods that has taken a wide space in application in the previous two decades because of the attractive features of these methods to researchers, as it is not affected by outliers values, meaning that it is considered one of the robust methods, and it gives more details of the effect of explanatory variables on the dependent variable.In this paper, a Bayesian hierarchical model for variable selection and estimation in the context of binary quantile regression is proposed. Current approaches to variable selection in the context of binary classification are sen

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Pangku, Mutiara, and Lukfiah Irwan Radjak. "ANALISIS FINANCIAL DISTRESS PADA PEMERINTAH PROVINSI GORONTALO TAHUN 2014-2018." JSAP : Journal Syariah and Accounting Public 4, no. 1 (2021): 1. http://dx.doi.org/10.31314/jsap.4.1.1-8.2021.

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This research was conducted at the Regional Financial and Asset Agency of Gorontalo Province. The purpose of this study is to obtain answers to problems that have been formulated, to analyze financial risks to the government of Gorontalo Province. This study uses binary logistic regression method where the dependent variable financial distress is dummy and only has two possible outcomes, yes and no. From the results of research conducted partially on the independent variables, namely regional financial independence, local revenue, regional expenditure and solvency have no positive effect on th

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et al., Abidin. "Rayleigh-Benard convection in a binary fluid-saturated anisotropic porous layer with variable viscosity effect." International Journal of ADVANCED AND APPLIED SCIENCES 9, no. 2 (2022): 167–72. http://dx.doi.org/10.21833/ijaas.2022.02.019.

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Rayleigh-Benard convection due to buoyancy that occurred in a horizontal binary fluid layer saturated anisotropic porous media is investigated numerically. The system is heated from below and cooled from above. The temperature-dependent viscosity effect was applied to the double-diffusive binary fluid and the critical Rayleigh number for free-free, rigid-free, and rigid-rigid representing the lower-upper boundary were obtained by using the single-term Galerkin expansion procedure. Both boundaries are conducted to temperature. The effect of temperature-dependent viscosity, mechanical anisotropy

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Mutar Awad, Ahmed, Yousif Hamad Efan, and Qays Neamah Ibrahim. "Regression Based Network Security Scenario Prediction Model." BIO Web of Conferences 97 (2024): 00108. http://dx.doi.org/10.1051/bioconf/20249700108.

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Analysing issues in which one or more independent variables predict an outcome may be done using logistic regression. A binary or dichotomous dependent variable is used to quantify the result, which only comprises data coded as 1 (True, Success, etc.) or 0 (False, Failure, etc). Logistic regression is used to identify the best model to represent the connection between a dependent variable (outcome or response variable) and a collection of independent (predictor or explanatory) variables. Biomedical applications of LR (Linear Regression) include cancer detection, survival prediction, and more.

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Kim, Su-Bi, and Su-Young Kim. "Testing the mediated effect of a model with a binary dependent variable." KOREAN JOURNAL OF PSYCHOLOGY : GENERAL 37, no. 3 (2018): 441–70. http://dx.doi.org/10.22257/kjp.2018.09.37.3.441.

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Lai, Peng, Fengli Song, Kaiwen Chen, and Zhi Liu. "Model free feature screening with dependent variable in ultrahigh dimensional binary classification." Statistics & Probability Letters 125 (June 2017): 141–48. http://dx.doi.org/10.1016/j.spl.2017.02.011.

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Shih, Wei, and James A. Sullivan. "A heuristic parameter estimation procedure for a binary dependent variable regression model." Computational Statistics & Data Analysis 8, no. 3 (1989): 313–24. http://dx.doi.org/10.1016/0167-9473(89)90047-9.

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Esarey, Justin, and Andrew Pierce. "Assessing Fit Quality and Testing for Misspecification in Binary-Dependent Variable Models." Political Analysis 20, no. 4 (2012): 480–500. http://dx.doi.org/10.1093/pan/mps026.

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In this article, we present a technique and critical test statistic for assessing the fit of a binary-dependent variable model (e.g., a logit or probit). We examine how closely a model's predicted probabilities match the observed frequency of events in the data set, and whether these deviations are systematic or merely noise. Our technique allows researchers to detect problems with a model's specification that obscure substantive understanding of the underlying data-generating process, such as missing interaction terms or unmodeled nonlinearities. We also show that these problems go undetected

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Banerjee, Debashmita, Roshan Kumar, Srishti Tripathi, and Benrithung Murry. "Application of Binary Logistic Regression in Biological Studies." Journal of the Practice of Cardiovascular Sciences 10, no. 1 (2024): 48–52. http://dx.doi.org/10.4103/jpcs.jpcs_82_23.

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Binary logistic regression (BLR) is a statistical method that utilizes one or more independent variables to make predictions about the outcome of a categorical dependent variable. The examples in this article demonstrate the utilization of biological data in statistical software such as SPSS and Excel to illustrate BLR. The primary objective of this essay is to provide readers with a comprehensive understanding of binomial logistic regression. This presentation examines the appropriate circumstances for utilizing this specific regression technique and provides guidance on assessing the adequac

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Hamza, M. A., E. H. Ali, A. H. Alzubaidi, and E. A. Abdalltef. "Economic Analysis of the Most Important Determinants of Sustainable Efficiency in Potato Farms in Baghdad Governorate." IOP Conference Series: Earth and Environmental Science 1449, no. 1 (2025): 012180. https://doi.org/10.1088/1755-1315/1449/1/012180.

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Abstract The data were obtained through a questionnaire collected randomly from 102 potato farms in Baghdad Governorate, which sustainable efficiency meets the needs of the present and lays the strategic foundations for the future. Sustainable efficiency is defined as sustainability concepts that reduce environmental impact by reducing resources and managing waste while adding economic value, as achieving sustainable development requires attention not only to economic growth, but also to other social issues. Therefore, 102 samples of soil were taken and analysed and the amount of nitrogen rema

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Husniyah, Nailuh, Enita Dewi Br Tarigan, and Yan Batara Putra Siringoringo. "Faktor-Faktor yang Mempengaruhi Preferensi Pemilih Usia Muda dalam Pemilihan Presiden 2024 di Kota Medan menggunakan Regresi Logistik Biner." Pattimura Proceeding: Conference of Science and Technology 5, no. 1 (2024): 133–42. http://dx.doi.org/10.30598/ppcst.knmxxiiv5i1p133-142.

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This study aims to identify factors that influence young voters' preferences in the 2024 presidential election in Medan City. There are 12 independent variables used in this study, namely socio-culture, age, education, track record, policy, political issues, political interest, campaign, social media, trust, candidate quality, and debate results. The dependent variable is voter preference, with a value of 0 for not voting and 1 for voting. This study used binary logistic regression method with purposive sampling technique to collect data from 271 respondents of young voters in Medan City. The

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Evenden, Emily, and Robert Gilmore Pontius Jr. "Encoding a Categorical Independent Variable for Input to TerrSet’s Multi-Layer Perceptron." ISPRS International Journal of Geo-Information 10, no. 10 (2021): 686. http://dx.doi.org/10.3390/ijgi10100686.

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The profession debates how to encode a categorical variable for input to machine learning algorithms, such as neural networks. A conventional approach is to convert a categorical variable into a collection of binary variables, which causes a burdensome number of correlated variables. TerrSet’s Land Change Modeler proposes encoding a categorical variable onto the continuous closed interval from 0 to 1 based on each category’s Population Evidence Likelihood (PEL) for input to the Multi-Layer Perceptron, which is a type of neural network. We designed examples to test the wisdom of these encodings

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D’Orazio, Daniel J., Paul C. Duffell, and Christopher Tiede. "Fast Methods for Computing Photometric Variability of Eccentric Binaries: Boosting, Lensing, and Variable Accretion." Astrophysical Journal 977, no. 2 (2024): 244. https://doi.org/10.3847/1538-4357/ad938b.

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Abstract We analyze accretion-rate time series for equal-mass binaries in coplanar gaseous disks spanning a continuous range of orbital eccentricities up to 0.8 for both prograde and retrograde systems. The dominant variability timescales match those of previous investigations; the binary orbital period is dominant for prograde binaries with e ≳ 0.1, with a 5 × longer “lump” period taking over for e ≲ 0.1. This lump period fades and drops from 5 × to 4.5 × the binary period as e approaches 0.1, where it vanishes. For retrograde orbits, the binary orbital period dominates at e ≲ 0.55 and is acc

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Beck, Nathaniel, Jonathan N. Katz, and Richard Tucker. "Taking Time Seriously: Time-Series-Cross-Section Analysis with a Binary Dependent Variable." American Journal of Political Science 42, no. 4 (1998): 1260. http://dx.doi.org/10.2307/2991857.

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Nurhayati, Immas, Maemunah Sa'diah, Dedi Supriadi, and Yuggo Afrianto. "Analisis dampak MBKM terhadap kinerja UIKA Bogor: Pendekatan demografi." Tawazun: Jurnal Pendidikan Islam 15, no. 2 (2022): 209. http://dx.doi.org/10.32832/tawazun.v15i2.8293.

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<p><em>Higher education is the key of the success of the MBKM program because the main goal of this program is to produce superior human resources who can apply their scientific fields and expertise in accordance with the needs of the business world and the industrial world. The main purpose of this study is to analyze in depth the impact of MBKM on the performance of University of Ibn Khaldun Bogor(UIKA) from a demographic perspective which includes gender, age, educational background and occupation/status. The research sample amounted to 219 respondents consisting of 103 students

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LIPOVETSKY, STAN. "CONDITIONAL AND MULTINOMIAL LOGITS AS BINARY LOGIT REGRESSIONS." Advances in Adaptive Data Analysis 03, no. 03 (2011): 309–24. http://dx.doi.org/10.1142/s1793536911000738.

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For a categorical variable with several outcomes, its dependence on the predictors is usually considered in the conditional or multinomial logit models. This work considers elasticity features of the binary and categorical logits and introduces the coefficients individual by observations. The paper shows that by a special rearrangement of data the more complicated conditional and multinomial models can be reduced to binary logistic regression. It suggests the usage of any software widely available for logit modeling to facilitate constructing for complex conditional and multinomial regressions

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Al-Zubaidi, Manal G., Hamsa Zubaidi, and Bassim H. Al-Humeidawi. "Investigating the Risk Factors on Crash Severity for Selected Risky Roads in Al-Diwaniyah City by Utilizing a Binary Probit Model." E3S Web of Conferences 427 (2023): 03037. http://dx.doi.org/10.1051/e3sconf/202342703037.

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Traffic crashes are one of the main reasons for the deaths of many people and the loss of property. Road safety is a crucial aspect of transportation that aims to prevent crashes and injuries on the road, and several contributing factors affect it. In this study, the binary probit model using the N-Logit software was applied to crash-related data to examine the contribution of several variables to severe crash outcomes in Al-Diwaniyah City. Crash severity (the dependent variable) in this study is a dichotomous variable with two categories, severe and non-severe. Because of the binary nature of

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Azhagusundari, Dr B., Dr John Grasias S, G. Sudha,, G. Kanimozhi,, and Dr Radhika. "Linear Regression Model in Student Prediction System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42438.

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Logistic Regression is a widely used statistical method for predicting a categorical dependent variable based on a set of independent variables. Recognized for its adaptability and frequent application, logistic regression is particularly effective in modeling binary and multinomial outcomes. This paper provides a clear and detailed exploration of the fundamental concepts of logistic regression and demonstrates its application in predictive analysis using student data. Through this practical example, the paper highlights the method's utility in identifying relationships and making informed pre

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Zakiyah, Tuti, and Wahyuni Windasari. "Implementasi Model Finansial Distres Pada Perusahaan Manufaktur Yang Terdaftar di IDX Tahun 2015-2017." Jurnal Ilmiah Akuntansi dan Keuangan 9, no. 1 (2020): 61–74. http://dx.doi.org/10.32639/jiak.v9i1.330.

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Manufacturing Company is a sample of this study, the dependent variable used in this study is a binary variable, namely whether the company is in financial distress or non-financial distress. Hypothesis testing uses binary logistic regression (Binary Logistic Regression) because the dependent variable is a combination of metric and non-metric (nominal). The model used is the Altman Z-Score model, Springate S-Score, Grover G-Score, Zmijewski X-Score, and univariate models. Of the five models, the best model is the Springate S-Score with a Nagelkerke R2 value of 0.582. the second is, Zmijewski X

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Samsul Pahmi, Ade Hudiana, Lesri, Santi Laswati Suryadi, Lucas Cramer, and Bahadir Ozsut. "Decision Supporting System of Granting Loans with Binary Logistic Regression." INTERNATIONAL JOURNAL ENGINEERING AND APPLIED TECHNOLOGY (IJEAT) 4, no. 2 (2021): 93–100. http://dx.doi.org/10.52005/ijeat.v4i2.53.

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This research aims to identify the existing problems on the Koperasi Karya Usaha Mandiri as skim credit for poor families by granting credit in groups. The issue raised, namely the influence of Decision Supporting System of Granting Loans against credit jam. This research is a type of quantitative research which uses 8 independent variables and 1 dependent variable. Method of data collection in this research is the observation, interviews, literature studies and documentation. This research method uses Binary Logistic Regression to analyze the determination decision granting loans to prospecti

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Editor, IJSMI. "CART and CHAID ANALYSIS." International Journal of Statistics and Medical Informatics 15, no. 1 (2021): 1–6. https://doi.org/10.5281/zenodo.4672067.

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Classification and Regression Trees (CART) and Chi Square Automatic Interaction Detector (CHAID) works on principles of decision tree analysis. Classification and Regression (CART) classifies the data based on the categorical outcome variable (Classification) and also uses continuous outcome variable for regression problem. Chi Square Automatic Interaction Detector (CHAID) is similar to CART which uses classifies the data into multiple class labels not only binary classification. In CHAID both dependent variable and independent variables will be categorical. This paper provides an overview and

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Kokkinou, Alinda, and David A. Cranage. "Why wait? Impact of waiting lines on self-service technology use." International Journal of Contemporary Hospitality Management 27, no. 6 (2015): 1181–97. http://dx.doi.org/10.1108/ijchm-12-2013-0578.

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Purpose – The purpose of the present study is to examine the effect of waiting lines on customers’ decisions between using a self-service alternative and using a service employee. As self-service technologies are expensive and time-consuming to design and implement, service providers need to understand what drives customers to use them. Service operators have the most control over waiting lines and flexibility in expanding capacity, either by adding service employees or by adding self-service kiosks. Design/methodology/approach – The study used online scenario-based surveys following a 4 (numb

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Academic, Journal of International University of Erbil, and Shakhawan Saeed Sangawi. "Applying the logistic regression to the effect of the Profitability ratio on the quality of financial reporting in the stock exchange for the Period 2016-2022." Academic Journal of International University of Erbil 01, no. 02 (2024): 64–76. https://doi.org/10.5281/zenodo.15257683.

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This study aims to determine the effect of profit margin on the quality of financial reports using a binary logistic regression model. Therefore, in the study, an attempt was made to provide a conceptual aspect for each of the profitability ratio and quality of financial reports applicable aspects using the model.The study was obtained based on purposeful sampling. This is the audited financial list of (11) companies in the manufacturing sector out of (28) companies registered on the Iraqi Stock Exchange for the period (2016-2022). In the study, the quality of financial reports in manufacturin

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Rais, Zulkifli, Ruliana, and Indrayasaro. "Implementation of Binary Logistic Regression and Chi-Squared Automatic Interaction Detection (CHAID) to Recipients of the Prosper Family Card Program in Makassar City." Quantitative Economics and Management Studies 6, no. 1 (2025): 130–43. https://doi.org/10.35877/454ri.qems3981.

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The binary logistic regression analysis method is a classification method that forms a relationship between a dichotomous dependent variable and an independent variable, while the chi-squared automatic interaction detection (CHAID) analysis method is a decision tree classification method for studying the relationship between independent variables and variables. bound by using the chi-square test statistic as the main tool. This research aims to determine the magnitude of the resulting accuracy value and what factors influence recipients of the Prosperous Family Card program in Makassar City ba

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Mohamad, Aditya, Agus Setiyanto, Bangkit Widhi Raharjo, and Jerry Heikal. "Analisis Faktor yang Mempengaruhi Kepuasan Pembeli di Kopi Kenangan Menggunakan Metode Regresi Logistik Biner." Jurnal Syntax Admiration 5, no. 8 (2024): 2964–72. http://dx.doi.org/10.46799/jsa.v5i8.1376.

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This study aims to analyze various factors that affect buyer satisfaction with Kopi Kenangan. The method used in this study is the binary logistic regression method using SPSS. The number of samples was 68 consumers who came directly to Kopi Kenangan, with the variables used being product, packaging, price, service quality, cleaness/comfort, waiting time, discount, and interior/exterior design. Sample collection was carried out through filling out a survey form filled out by 68 buyer consumers who came directly to Kopi Kenangan. The results of the survey are outlined in Microsoft Excel and the

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Et. al., Mahdi Wahhab Neamah,. "Utilizing the Logistic Regression Model in Analyzing the Categorical Data of Economic Effects." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 4 (2021): 638–46. http://dx.doi.org/10.17762/turcomat.v12i4.547.

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The categorical data has a significant role in representing statistical binary variables, and they are analyzed by means of grouping the response variable into ordered categories. Thereby, the dependent variable becomes of type binary qualitative variable. The data related to the financial position of world countries is classified within the categorical data. This work is to study the economic effects of an individual's different factors on determining the richness or poorness levels of a selected population of countries. Moreover, a logistic regression model is to be created to estimate these

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Breunig, Christoph, Michael Kummer, Joerg Ohnemus, and Steffen Viete. "Information technology outsourcing and firm productivity: eliminating bias from selective missingness in the dependent variable." Econometrics Journal 23, no. 1 (2019): 88–114. http://dx.doi.org/10.1093/ectj/utz016.

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Summary Missing values are a major problem in all econometric applications based on survey data. A standard approach assumes data are missing at random and uses imputation methods or even listwise deletion. This approach is justified if item nonresponse does not depend on the potentially missing variables’ realization. However, assuming missingness at random may introduce bias if nonresponse is, in fact, selective. Relevant applications range from financial or strategic firm-level data to individual-level data on income or privacy-sensitive behaviors. In this paper, we propose a novel approach

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Lewbel, Arthur. "IDENTIFICATION OF THE BINARY CHOICE MODEL WITH MISCLASSIFICATION." Econometric Theory 16, no. 4 (2000): 603–9. http://dx.doi.org/10.1017/s0266466600164060.

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Misclassification in binary choice (binomial response) models occurs when the dependent variable is measured with error, that is, when an actual “one” response is sometimes recorded as a zero and vice versa. This paper shows that binary response models with misclassification are semiparametrically identified, even when the probabilities of misclassification depend in unknown ways on model covariates and the distribution of the errors is unknown.

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Sofiah, Alma Mariana, and Nusar Hajarisman. "Regresi Logistik Dua Level Respon Biner untuk Pemodelan Data Status Kelulusan Mata Kuliah Statistika Mahasiswa Fakultas X, Y dan Z Universitas Islam Bandung Angkatan 2019." Bandung Conference Series: Statistics 3, no. 2 (2023): 386–95. http://dx.doi.org/10.29313/bcss.v3i2.8228.

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Abstract. Two-level binary logistic regression is a multilevel analysis used to analyze data that has a two-level hierarchical structure with binary response data. Hierarchical data structure is data with observation units nested in higher units. Two-level regression is the simplest multilevel model where the first level is individual data and the second level is group data. In this study, we will discuss the use of two-level binary logistic regression on data on the Passing Status of Statistics Courses of Bandung Islamic University Students Class of 2019. The data used is secondary data obtai

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Beck, Nathaniel, Jonathan N. Katz, and Richard Tucker. "Erratum: Taking Time Seriously: Time-Series-Cross-Section Analysis with a Binary Dependent Variable." American Journal of Political Science 43, no. 3 (1999): 978. http://dx.doi.org/10.2307/2991844.

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Bulusu, Srinivas, and Kumares C. Sinha. "Comparison of Methodologies to Predict Bridge Deterioration." Transportation Research Record: Journal of the Transportation Research Board 1597, no. 1 (1997): 34–42. http://dx.doi.org/10.3141/1597-05.

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Two methods for the estimation of bridge condition states were examined, one based on the Bayesian approach and the other using a binary probit model. The Bayesian approach was considered so that experts' opinions could be combined with observed data. Prior transition probabilities, based on bridge inspectors' experiences, were assumed to follow Dirichlet distribution. Observed data followed a multinomial distribution. The updated transition probabilities were used to predict bridge condition states. In the second approach, deterioration models were developed for each condition state. The depe

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Zgurić, Borna. "A comparison of democratic transformations of Tunisia and Indonesia." Politička misao 58, no. 2 (2021): 70–91. http://dx.doi.org/10.20901/pm.58.2.03.

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The question this paper tries to provide an answer to is, why democratic transformation‎ was successful in Tunisia and Indonesia? The theoretical approach‎ is primarily rooted in descriptive-empirical actor theories, although cultural‎ theories were used as well, as to better understand the political ideas and‎ stances of Islamist actors. The research strategy is a binary comparative study‎ with the same outcome on the dependent variable. Furthermore, the paper‎ utilizes the Most Different Systems Design (MDSD) since both countries are‎ quite different, but the dependent variable is the same –

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Reza, Reza, Mirza Antoni, and Desi Aryani. "FAKTOR-FAKTOR YANG MEMPENGARUHINYA POLA KONVERSI LAHAN KARET MENJADI KELAPA SAWIT DI KABUPATEN BANYUASIN." AGROTEKSOS 34, no. 3 (2025): 1199. https://doi.org/10.29303/agroteksos.v34i3.1316.

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Konversi lahan dari tanaman karet ke kelapa sawit menjadi fenomena yang semakin meningkat di Kabupaten Banyuasin. Beberapa faktor mampu mempengaruhi pola konversi lahan. Penelitian ini bertujuan untuk menganalisis pola konversi lahan karet menjadi kelapa sawit serta faktor-faktor yang mempengaruhinya. Metode yang digunakan adalah penarikan contoh secara acak berlapis berimbang (propotionated stratified random sampling), dengan sistem undian. Data dikumpulkan melalui wawancara langsung menggunakan kuesioner serta data sekunder dari instansi terkait. Data yang digunakan dalam mengolah data dari

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