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Fahmy, Rifqi Nur. "Determinan Keputusan Melakukan Migrasi Ulang-Alik." Efficient: Indonesian Journal of Development Economics 1, no. 3 (December 23, 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 family and marital status. While the variable level of education, age, and distance have no effect on workforce’s decisions to do the commuter migration.
Tujuan dari penelitian ini adalah untuk menganalisis pengaruh variabel dependen dependen keluarga, tingkat pendidikan, usia, status perkawinan, dan jarak secara parsial terhadap keputusan tenaga kerja untuk bermigrasi dari Surakarta ke Kabupaten Karanganyar. Penelitian ini menggunakan metode analisis regresi logistik biner. Sampel dalam penelitian ini adalah 100 responden. Hasil analisis model regresi logistik biner dalam penelitian ini menunjukkan bahwa dari lima variabel independen, ada dua variabel yang berpengaruh signifikan terhadap keputusan angkatan kerja untuk melakukan migrasi komuter yang merupakan variabel dependen keluarga dan status perkawinan. Sedangkan tingkat variabel pendidikan, usia, dan jarak tidak berpengaruh pada keputusan tenaga kerja untuk melakukan migrasi komuter.
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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 (January 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 (May 1, 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 Angrist and William Evans (1998)) and compare with their LATE estimates.
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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 (December 30, 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 the main objective is to find the probability of something happening (probability model). One of the development methods of the regression model for data with qualitative response variables is Logistic and Probit regression. The purpose of this study was to compare the best model using binary logistic regression with binary probit regression in the case of Labor Force Participation Rate (TPAK) in Gorontalo City. The research method used is quantitative research methods, with binary logistic regression modelling and binary probit regression. The results showed that the variable that has a significant effect on TPAK Gorontalo City is the open unemployment rate, and the best model between the binary logistic regression model with an AIC value of 1.289 is smaller than the AIC value of the binary Probit regression 1.318, likewise from the R2 value the R2 value for regression is obtained. binary logistic of 12.74%, greater than the R2 value of binary probit regression of 10.70%.
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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 (November 1, 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 between the dependent variable and the explanatory variables.
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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 (May 12, 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 lasso and adaptive elastic net methods for classification on high dimensional data. The classification is used to classify the categorical data for dependent variable dependent on the independent variables, which is called the logistic regression model. The categorical data are considered a binary variable, and the independent variables are used as the continuous variable. The high dimensional data are represented when the number of independent variables is higher than the sample sizes. For this research, the simulation of the logistic regression is considered as the binary dependent variable and 20, 30, 40, and 50 as the independent variables when the sample sizes are less than the number of the independent variables. The independent variables are generated from normal distribution on several variances, and the dependent variables are obtained from the probability of logit function and transforming it to predict the binary data. For application in real data, we express the classification of the type of leukemia as the dependent variables and the subset of gene expression as the independent variables. The criterion of these methods is to compare by the average percentage of predicted accuracy value. The results are found that the higher-order of adaptive lasso method is satisfied with large dispersion, but the higher-order of adaptive elastic net method outperforms on small dispersion.
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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 (November 30, 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 qualitative short panel vector autoregression (QSP-VAR). The QSP-VAR combines binary and continuous variables into a single vector of dependent variables, making every variable endogenous a priori. The QSP-VAR identifies causal order, reveals within-subject correlation, and accounts for latent variables. Using a Bayesian approach, the QSP-VAR provides reliable inference for short time dimension longitudinal research. This is demonstrated through analysis of the durability of elite corporate agents, social networks, and firm performance in France. We provide our OpenBUGS code to enable implementation of the QSP-VAR by other researchers.
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Thomas, Jonathan M. "On testing the logistic assumption in binary dependent variable models." Empirical Economics 18, no. 2 (June 1993): 381–92. http://dx.doi.org/10.1007/bf01205409.
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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 between a dependent binary variable and independent variables. Discriminant analysis is a powerful classification based on linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and regularized discriminant analysis (RDA). These methods consider the decision boundaries by building a classifier model on the multivariate normal distribution. LDA defines the standard covariance matrix, but QDA has an individual covariance matrix. RDA extends from QDA by setting the regularized parameter to estimate the covariance matrix. In the case of the simulation study, the independent variables are generated by defining the constant correlation on the multivariate normal distribution that made the multicollinearity problem. Then the binary response variable can be approximated from the logit function. For application to actual data, we expressed the classification of type liver and non-liver patients as the dependent variables and obtained patient personal information on the nine independent variables. The highest average percentage of accuracy determines the performance of these methods. The results have shown that the logistic regression was successful when using small independent variables, but the RDA performed when using large independent variables.
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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 continuous dependent variables in the diagnostic data set. This indicates that some binary dependent variables are more predictable using a reduced set of uncorrelated independent variables, and continuous dependent variables demand more complexity.
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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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de Jong, Robert M., and Tiemen Woutersen. "DYNAMIC TIME SERIES BINARY CHOICE." Econometric Theory 27, no. 4 (March 3, 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 correlated. It turns out that no long-run variance estimator is needed for the validity of the smoothed maximum score procedure in the dynamic time series framework.
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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 (March 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 require a correctly specified probability model. We show that the proposed methods have near optimal performance relative to a class of linear classification rules with selected variables. Such results in classification are obtained in a framework of dependent data with strong mixing.
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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 (December 23, 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). This method is an iterative method to get global optimum. The results of the classification accuracy of Logistic Regression in the case of septictank data in East Surabaya with 11 independent variables and binary dependent variable is Logistic Regression accuracy of 54.55%. However when selected with GA, the classification accuracy of Binary Logistic Regression is 90.91%.
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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 (December 14, 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 kasus kesehatan, khususnya pada penelitian ini yaitu mengenai kanker payudara. Sesuai uraian diatas maka penulis bermaksud untuk mengkaji dan melakukan penelitian tentang Pemodelan Faktor-Faktor yang Mempengaruhi Jenis Kanker Payudara Menggunakan Regresi Logistik Biner (Kasus : Pasien Penderita Kanker Payudara di Rumah Sakit Umum Pusat Dr. Wahidin Sudirohusodo). Dari hasil analisis didapatkan bahwa peubah penjelas yang berpengaruh nyata terhadap jenis keganasan kanker terhadap pasien penderita kanker payudara adalah peubah Kemoterapi (X2) dan peubah Metastase (X5) yang masing-masing memiliki nilai odds rasio sebesar 0,17 dan 6,16. Kata kunci : Kanker Payudara, Regresi Logistik, Regresi Logistik Biner. Abstract Logistic regression is a method of statistical analysis that is applied to model the dependent variable which has two or more categories with one or more independent variables. Binary Logistic Regression is a statistical analysis that is used to analyze the relationship between one or more independent variables with variable binary or dichotomous responses. The free variables in logistic regression can be either categorical scale or continuous scale variables while the response variables are categorical scale variables. Binary Logistic Regression can be applied to health cases, especially in this study, namely breast cancer. In accordance with the description above, the author intends to study and conduct research on Modeling Factors Affecting Types of Breast Cancer Using Binary Logistic Regression (Case: Patients with Breast Cancer Patients at Dr. Wahidin Sudirohusodo Central General Hospital). From the results of the analysis it was found that the explanatory variables that significantly affected the type of cancer malignancy in patients with breast cancer were Chemotherapy variables (X2) and Metastase variables (X5), each of which had odds ratio values of 0.17 and 6.16. Keywords: Breast Cancer, Logistic Regression, Binary Logistic Regression.
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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 (September 25, 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 (November 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 by the fit statistics most commonly used in political science.
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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 Monte Carlo simulation, I demonstrate that a model proposed by Hausman, Abrevaya, and Scott-Morton (1998, Misclassification of the dependent variable in a discrete-response setting. Journal of Econometrics 87:239–69) allows for correcting these biases in binary probit models. Empirical examples based on reanalyses of models explaining the occurrence of rebellions and civil wars demonstrate the problem that comes from neglecting these misclassifications.
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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 resistance. This paper starts by explaining the concepts of linear and quadratic discriminant analysis and then presents the four other methods used to create the decision boundary. Our simulation study generated the independent variables by setting the coefficient correlation via multivariate normal distribution or multicollinearity, often through basic logistic regression used to construct the binary dependent variable. For application to Pima Indian diabetic dataset, we expressed the classification of diabetes as the dependent variable and used a dataset of eight independent variables. This paper aimed to determine the highest average percentage of accuracy. Our results showed that the MM and ML methods successfully used large independent variables for linear discriminant analysis (LDA). However, the t-distribution method of quadratic discriminant analysis (QDA) performed better when using small independent variables.
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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 (December 4, 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 the dependent variable financial distress. Meanwhile, simultaneously the independent variables of financial independence, local revenue, regional expenditure and solvency have a positive effect on the variables tied to financial distress
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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 (February 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, thermal anisotropy, Soret, and Dufour parameters on the onset of stationary convection are discussed and shown graphically.
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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 (October 1998): 1260. http://dx.doi.org/10.2307/2991857.
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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 (October 12, 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. The results show that encoding a categorical variable based on each category’s Sample Empirical Probability (SEP) produces results similar to binary encoding and superior to PEL encoding. The Multi-Layer Perceptron’s sigmoidal smoothing function can cause PEL encoding to produce nonsensical results, while SEP encoding produces straightforward results. We reveal the encoding methods by illustrating how a dependent variable gains across an independent variable that has four categories. The results show that PEL can differ substantially from SEP in ways that have important implications for practical extrapolations. If users must encode a categorical variable for input to a neural network, then we recommend SEP encoding, because SEP efficiently produces outputs that make sense.
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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 (November 1, 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, 75 education staff and 41 lecturers. The results of the questionnaire will be processed using IBM SPSS Statistics 25 using a binary logistic linear regression model. The research variables consist of the dependent variable and the independent variable. The dependent variable or Y is the public's perception of the impact of MBKM on the performance of UIKA Bogor, while the independent variable or variable X are several factors that influence the perceptions of lecturers, education staff and students about the impact of MBKM on UIKA performance like gender, age, education and occupation. Based on the descriptive analysis, the majority of respondents, as many as 214 respondents or about 97.8% stated that there was an impact of MBKM on improving performance and 5 respondents or about 2.2% stated that there was no impact of MBKM on the performance of UIKA Bogor. The individual coefficient test conducted with the Wald test shows that all independent variables consisting of various demographic factors such as gender, age, education and occupation are not significant at alpha 5% meaning that there is no difference in respondents' perceptions about the impact of MBKM on the performance UIKA Bogor.</em></p><p><strong>Abstrak </strong></p><p>Perguruan Tinggi merupakan ujung tombak keberhasilan program MBKM karena sasaran utama dari program ini adalah menghasilkan sumber daya manusia yang unggul yang dapat menerapkan bidang keilmuan serta keahlian yang dimilikinya sesuai dengan kebutuhan dunia usaha dan dunia industri. Penelitian ini bertujuan untuk menganalisis secara mendalam dampak MBKM terhadap kinerja Universitas Ibn Khaldun Bogor ditinjau dari pesrpektif demografi yang meliputi jenis kelamin, umur, latar belakang pendidikan dan pekerjaan/status. Sampel penelitian berjumlah 219 responden yang terdiri dari 103 mahasiswa, 75 tenaga pendidikan dan 41 orang dosen. Hasil kuesioner akan diolah menggunakan IBM SPSS Statistics 25 menggunakan model regresi linier binary logistic. Variabel penelitian terdiri dari variabel terikat (independent variable) dan variabel bebas (dependent variable). Variabel terikat (independent variable) atau variable Y merupakan persepsi masyarakat tentang dampak MBKM terhadap kinerja Universitas Ibn Khaldun Bogor, sedangkan variabel bebas (dependent variable) atau variabel X merupakan beberapa faktor yang mempengaruhi persepsi dosen, tenaga kependidikan dan mahasiswa tentang dampak MBKM terhadap kinerja Universitas Ibn Khaldun Bogor seperti jenis kelamin, usia, pendidikan dan pekerjaan. Berdasarkan pada analisis deskriptif, mayoritas responden yaitu sebanyak 214 responden atau sekitar 97,8% menyatakan adanya dampak MBKM terhadap peningkatan kinerja dan 5 responden atau sekitar 2,2% menyatakan tidak ada dampak MBKM terhadap kinerja UIKA Bogor. Uji koefisien secara individu yang dilakukan dengan uji wald menunjukkan bahwa seluruh variabel bebas yang terdiri dari berbagai faktor demografi seperti jenis kelamin, umur, pendidikan dan pekerjaan tidak signifikan pada alfa 5% artinya tidak ada perbedaan persepsi responden tentang dampak MBKM terhadap kinerja Universitas Ibn Khaldun Bogor.</p>
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LIPOVETSKY, STAN. "CONDITIONAL AND MULTINOMIAL LOGITS AS BINARY LOGIT REGRESSIONS." Advances in Adaptive Data Analysis 03, no. 03 (July 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. In addition, for binary logit, it is possible to obtain meaningful coefficients of regression by transforming data to the linear link function, which opens a possibility to obtain meaningful parameters of the complicated models with categorical dependent variables.
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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 (July 1999): 978. http://dx.doi.org/10.2307/2991844.
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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 (November 29, 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 prospective members and cause bottlenecks in financing micro-credits in Koperasi Karya Usaha Mandiri Syariah.
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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 (September 20, 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 to deal with selective item nonresponse in the model’s dependent variable. Our approach is based on instrumental variables that affect selection only through a partially observed outcome variable. In addition, we allow for endogenous regressors. We establish identification of the structural parameter and propose a simple two-step estimation procedure for it. Our estimator is consistent and robust against biases that would prevail when assuming missingness at random. We implement the estimation procedure using firm-level survey data and a binary instrumental variable to estimate the effect of outsourcing on productivity.
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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 (January 24, 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-Score with a value of 0.227 and the third best is the Univariate Model with a value of 0.042. Of the three best models, namely the Springate S-Score, Zmijewski X-Score and the Univariate Model. The implementation is that the ratios in these models are very important to be considered by companies as a sensitivity tool so that companies do not experience financial distress. ratios that are often used are ratios related to the company's ability to manage and produce net working capital, sales, debt and ability to generate profits from sales and profits from assets.
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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 (April 10, 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 levels. As a sample of research, the categorical data relevant to the financial status of 20 Arabic countries were drawn from the website of the World Bank, WB. In addition, for comparison purpose, another similar set of categorical data was generated by MATLAB too. The paper has been based on two hypotheses, first is the well-known regression models, like the ordinary least squares or maximum likelihood, are not accurate in case of binary qualitative variables. Second, is utilizing the logistic regression model as an alternative model to achieve the paper goal. The paper results, for both WB data and MATLAB data, have successfully proved the ability of the logistic regression model in manipulating the categorical data and predicting the coefficients of the corresponding regression models.
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Lewbel, Arthur. "IDENTIFICATION OF THE BINARY CHOICE MODEL WITH MISCLASSIFICATION." Econometric Theory 16, no. 4 (August 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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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 (August 10, 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 (number of customers waiting for the self-service technology) × 4 (number of customers waiting for the service employee) design. A binary dependent variable was used to record participants’ choice of service delivery alternative. Findings – Using logistic regression, the authors found that customers are increasingly motivated to use self-service technology as the waiting line for the service employee grows longer. This effect is influenced by perceived usefulness, anticipated quality of the self-service technology, need for interaction and technology anxiety. Research limitations/implications – This study should be replicated in a real-world setting where actual behavior, and not only intention, can be measured. Practical implications – The study provides guidance on how service providers can design their service to take advantage of the motivating effect of waiting lines on usage of self-service technology. Originality/value – The present study is the first to combine a scenario-based experiment with a binary dependent variable to isolate the impact of waiting lines on the choice between using a self-service technology and using a service employee. The use of the binary dependent variable overcomes the ambiguity of extrapolating from a continuous measure of intention to draw conclusions about behavior, a binary variable.
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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 (January 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 dependent variable is a zero/one indicator variable for condition switching state. The binary probit models developed considered the discreteness of condition states and they explicitly linked deterioration to relevant explanatory variables. This approach also incorporated heterogeneity and state dependence due to the use of panel data. An application of these methodologies was demonstrated for substructure element using the Indiana bridge data base.
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Zgurić, Borna. "A comparison of democratic transformations of Tunisia and Indonesia." Politička misao 58, no. 2 (May 5, 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 – democratic transformation was successful. The aim of the paper is to isolate the independent variables which should be considered as the necessary prerequisites for the democratic transformation in both cases. However, the paper emphasises that further testing and more cases are needed.
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Sartori, Anne E. "An Estimator for Some Binary-Outcome Selection Models Without Exclusion Restrictions." Political Analysis 11, no. 2 (2003): 111–38. http://dx.doi.org/10.1093/pan/mpg001.
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This article provides a new maximum-likelihood estimator for selection models with dichotomous dependent variables when identical factors affect the selection equation and the equation of interest. Such situations arise naturally in game-theoretic models where selection is typically nonrandom and identical explanatory variables influence all decisions under investigation. When identical explanatory variables influence selection and a subsequent outcome of interest, the commonly used Heckman-type estimators identify from distributional assumptions about the residuals alone. When its own identifying assumption is reasonable, the new estimator allows the researcher to avoid the painful choice between identifying from distributional assumptions alone and adding a theoretically unjustified variable to the selection equation in a mistaken attempt to “boost” identification. The article uses Monte Carlo methods to compare the small-sample properties of the estimator with those of the Heckman-type estimator and ordinary probit.
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Falaris, Evangelos M. "Misclassification of the dependent variable in binary choice models: evidence from five Latin American countries." Applied Economics 43, no. 11 (September 29, 2009): 1315–27. http://dx.doi.org/10.1080/00036840802600483.
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Schildcrout, Jonathan S., Sunni L. Mumford, Zhen Chen, Patrick J. Heagerty, and Paul J. Rathouz. "Outcome-dependent sampling for longitudinal binary response data based on a time-varying auxiliary variable." Statistics in Medicine 31, no. 22 (November 16, 2011): 2441–56. http://dx.doi.org/10.1002/sim.4359.
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Telles, Charles Roberto. "Measuring nonlinearity by means of static parameters in Bernoulli binary sequences distribution: A brief approach." International Journal of Modeling, Simulation, and Scientific Computing 11, no. 03 (June 2020): 2050021. http://dx.doi.org/10.1142/s179396232050021x.
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This paper analyzes Bernoulli’s binary sequences in the representation of empirical nonlinear events, analyzing the distribution of natural resources, population sizes and other variables that influence the possible outcomes of resource’s usage. Consider the event as a nonlinear system and the metrics of analysis consisting of two dependent random variables 0 and 1, with memory and probabilities in maximum finite or infinite lengths, constant and equal to 1/2 for both variables (stationary process). The expressions of the possible trajectories of metric space represented by each binary parameter remain constant in sequences that are repeated alternating the presence or absence of one of the binary variables at each iteration (symmetric or asymmetric). It was observed that the binary variables [Formula: see text] and [Formula: see text] assume on time [Formula: see text] specific behaviors (geometric variable) that can be used as management tools in discrete and continuous nonlinear systems aiming at the optimization of resource’s usage, nonlinearity analysis and probabilistic distribution of trajectories occurring about random events. In this way, the paper presents a model of detecting fixed-point attractions and its probabilistic distributions at a given population-resource dynamic. This means that coupling oscillations in the event occur when the binary variables [Formula: see text] and [Formula: see text] are limited as a function of time [Formula: see text].
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Luque-Fernandez, Miguel Angel, Daniel Redondo-Sánchez, and Camille Maringe. "cvauroc: Command to compute cross-validated area under the curve for ROC analysis after predictive modeling for binary outcomes." Stata Journal: Promoting communications on statistics and Stata 19, no. 3 (September 2019): 615–25. http://dx.doi.org/10.1177/1536867x19874237.
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Receiver operating characteristic (ROC) analysis is used for comparing predictive models in both model selection and model evaluation. ROC analysis is often applied in clinical medicine and social science to assess the tradeoff between model sensitivity and specificity. After fitting a binary logistic or probit regression model with a set of independent variables, the predictive performance of this set of variables can be assessed by the area under the curve (AUC) from an ROC curve. An important aspect of predictive modeling (regardless of model type) is the ability of a model to generalize to new cases. Evaluating the predictive performance (AUC) of a set of independent variables using all cases from the original analysis sample often results in an overly optimistic estimate of predictive performance. One can use K-fold cross-validation to generate a more realistic estimate of predictive performance in situations with a small number of observations. AUC is estimated iteratively for k samples (the “test” samples) that are independent of the sample used to predict the dependent variable (the “training” sample). cvauroc implements k-fold cross-validation for the AUC for a binary outcome after fitting a logit or probit regression model, averaging the AUCs corresponding to each fold, and bootstrapping the cross-validated AUC to obtain statistical inference and 95% confidence intervals. Furthermore, cvauroc optionally provides the cross-validated fitted probabilities for the dependent variable or outcome, contained in a new variable named _fit; the sensitivity and specificity for each of the levels of the predicted outcome, contained in two new variables named _sen and _spe; and the plot of the mean cross-validated AUC and k-fold ROC curves.
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Verma, Mahendra K. "Variable energy flux in turbulence." Journal of Physics A: Mathematical and Theoretical 55, no. 1 (December 9, 2021): 013002. http://dx.doi.org/10.1088/1751-8121/ac354e.
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Abstract In three-dimensional hydrodynamic turbulence forced at large length scales, a constant energy flux Π u flows from large scales to intermediate scales, and then to small scales. It is well known that for multiscale energy injection and dissipation, the energy flux Π u varies with scales. In this review we describe this principle and show how this general framework is useful for describing a variety of turbulent phenomena. Compared to Kolmogorov’s spectrum, the energy spectrum steepens in turbulence involving quasi-static magnetofluid, Ekman friction, stable stratification, magnetohydrodynamics, and solution with dilute polymer. However, in turbulent thermal convection, in unstably stratified turbulence such as Rayleigh–Taylor turbulence, and in shear turbulence, the energy spectrum has an opposite behaviour due to an increase of energy flux with wavenumber. In addition, we briefly describe the role of variable energy flux in quantum turbulence, in binary-fluid turbulence including time-dependent Landau–Ginzburg and Cahn–Hillianrd equations, and in Euler turbulence. We also discuss energy transfers in anisotropic turbulence.
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Seo, Hye Jin, and Jung A. Park. "A Study on the Improvement Plans through Determinants Analysis of Dispute Mediation for Commercial Lease in Seoul Metropolitan." Korea Real Estate Academy 88 (December 31, 2022): 70–82. http://dx.doi.org/10.31303/krear.2022.88.70.
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1. CONTENTS (1) RESEARCH OBJECTIVES The purpose of this study is to provide the measures and implications to increase the adjustment establishment rate by identifying the status of commercial real estate lease disputes and deriving the factors affecting the establishment of adjustment in Seoul Metropolitan. (2) RESEARCH METHOD The analysis method used binary logistic regression analysis that can be used when the dependent variable has binary characteristics among regression analysis, and the analysis tool used SPSS statistics 21 (3) RESEARCH FINDINGS As a result of using the binary logistic regression analysis method with the ultimate goal of dispute settlement as a dependent variable, six variables, 'Applicant Age', 'Applicant Gender', 'Processing Period', 'Rental Increase or Decrease', and 'Contract Renewal', were found to affect the establishment of adjustment. 2. RESULTS If we derive implications, the rate of mediation application was high in the order of disputes related to maintenance and repair following the increase or decrease in rent, so disputes related to maintenance can be prevented in advance and on-site mediation or insurance using expert members considering the urgency. In order to reduce the dismissal rate for each type of mediation case and increase the mediation establishment rate, it is necessary to find a pilot introduction of mediation preposition by specifying mediation cases, and the use of face-to-face meetings using the advantages of mediation is expected to play a positive role in mediation.
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Aprilia, Aprilia, Nursalam Nursalam, and Candra Panji Asmoro. "RIGHT MEDICATION RELATED TO DRUG CENTRALIZED IN RSUD SIDOARJO." INDONESIAN NURSING JOURNAL OF EDUCATION AND CLINIC (INJEC) 1, no. 2 (February 8, 2017): 187. http://dx.doi.org/10.24990/injec.v1i2.112.
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Introduction. Centralized drug is a management of the entire drug which is entirely done by nurses to administration to patients. Right medication is the process of right drug administration which is done by nurses based on 6 rights of medication, and wary of side effects. The purpose of this study was to analyze the corelation between centralized drug, team leadership, and nurse`s knowledge with right medication among nurses. Methods.The design of the study was descriptive corelational with cross-sectional approach. The population was inpatient nurses in RSUD Sidoarjo. Total sample was 114 respondents was selected by purposive sampling. The independent variables in this study: centralized drug, team leadership, and nurse`s knowledge. The dependent variable was right medication. Data were collected by using questionnares for independent variables and dependent variable. Data were analyzed by using Binary Logistic Regression with degree of significance α>0,05. Results. Binary Logistic Regression test showed non significance level between centralized drug with right medication (P=0.501), team leadership with right medication (P=0.874), and nurses`s knowledge with right medication (P=0.243). Discussion. This study concluded centralized drug, team leadership, and nurse`s knowledge were good. But, there are nurses that have negative value at right medication, however right medication in RSUD Sidoarjo has majority positive value. Keywords: centralized drug, right medication
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Bajai, Mátyás, Attila A. Víg, and Olivér Hortay. "Electricity Market Liquidity and Price Spikes: Evidence from Hungary." Periodica Polytechnica Social and Management Sciences 30, no. 1 (January 3, 2022): 49–56. http://dx.doi.org/10.3311/ppso.16857.
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This article examines how electricity market liquidity, renewable production and cross-border activity together in combination explain price spikes in the Hungarian Power Exchange day-ahead auctions. In the applied logit model, the dependent variable representing the price spike is binary, and the key explanatory variable is a modified bid-ask spread depicting liquidity. Weather-dependent renewable production and the difference between exports and imports appear as control variables in the model. The empirical analysis was based on data from 2017 and 2018. The results show that the control variables have no effect on the bid-ask spread and that the model explains 96 per cent of the spikes well, with an AUC-ROC of 0.75 and a Gini coefficient of 0.5. Based on the results, it may be worthwhile for traders to incorporate their data from sales and purchase curves into their forecasts, as this will improve their chances of successfully predicting extreme prices.
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Parsaulian, Agustinus Salomo, Tarno Tarno, and Dwi Ispriyanti. "ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI PENERIMA BERAS RASKIN MENGGUNAKAN REGRESI LOGISTIK BINER DENGAN GUI R." Jurnal Gaussian 10, no. 1 (February 28, 2021): 31–43. http://dx.doi.org/10.14710/j.gauss.v10i1.30934.
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The Rice Subsidy Program for Low-Income Communities or the Raskin Program is one of the government's programs to eradicate poverty. However, in practice, determining the criteria for Raskin recipients is a complicated problem. The Raskin program is a cross-sectoral national program both horizontally and vertically, to help meet the rice needs of low-income citizens. Determining the criteria for Raskin recipients is often a complicated issue. This study aims to analyze the classification of the Target Households (RTS) for the Raskin Program. The method used is binary logistic regression by utilizing R GUI. Binary logistic regression method is a method to find the relationship between independent and dependent variables, with a binary or dichotomous dependent variable. The data used is the March 2018 National Socio-Economic Survey (Susenas) data for Brebes Regency. The independent variables used in this study are the criteria for determining poor households, namely the area of the house, floor type of the house, wall type of the house, defecation facilities, lighting used, fuel used, ability to buy meat/milk, education level of the head of the household, and the capacity of installed electricity in the main residence. The results of the analysis show that in the final model, the variables that significantly affect the classification of RTS are the ability to eat healthy food, the capacity of installed electricity in the main residence, the education level of the head of the household, and defecation facilities with an accuracy value of 85.4%.Keywords: Raskin Program, Binary Logistic Regression, R GUI
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Díaz-Pérez, Manuel, Ángel Carreño-Ortega, José-Antonio Salinas-Andújar, and Ángel-Jesús Callejón-Ferre. "Application of Logistic Regression Models for the Marketability of Cucumber Cultivars." Agronomy 9, no. 1 (January 3, 2019): 17. http://dx.doi.org/10.3390/agronomy9010017.
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The aim of this study is to establish a binary logistic regression method to evaluate and select cucumber cultivars (Cucumis sativus L.) with a longer postharvest shelf life. Each sample was evaluated for commercial quality (fruit aging, weight loss, wilting, yellowing, chilling injury, and rotting) every 7 days of storage. Simple and multiple binary logistic regression models were applied in which the dependent variable was the probability of marketability and the independent variables were the days of storage, cultivars, fruit weight loss, and months of evaluation. The results showed that cucumber cultivars with a longer shelf life can be selected by a simple and multiple binary logistic regression analysis. Storage time was the main determinant of fruit marketability. Fruit weight loss strongly influenced the probability of marketability. The logistic model allowed us to determine the cucumber weight loss percentage over which a fruit would be rejected in the market.
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Valsamis, Epaminondas Markos, Henry Husband, and Gareth Ka-Wai Chan. "Segmented Linear Regression Modelling of Time-Series of Binary Variables in Healthcare." Computational and Mathematical Methods in Medicine 2019 (December 6, 2019): 1–7. http://dx.doi.org/10.1155/2019/3478598.
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Introduction. In healthcare, change is usually detected by statistical techniques comparing outcomes before and after an intervention. A common problem faced by researchers is distinguishing change due to secular trends from change due to an intervention. Interrupted time-series analysis has been shown to be effective in describing trends in retrospective time-series and in detecting change, but methods are often biased towards the point of the intervention. Binary outcomes are typically modelled by logistic regression where the log-odds of the binary event is expressed as a function of covariates such as time, making model parameters difficult to interpret. The aim of this study was to present a technique that directly models the probability of binary events to describe change patterns using linear sections. Methods. We describe a modelling method that fits progressively more complex linear sections to the time-series of binary variables. Model fitting uses maximum likelihood optimisation and models are compared for goodness of fit using Akaike’s Information Criterion. The best model describes the most likely change scenario. We applied this modelling technique to evaluate hip fracture patient mortality rate for a total of 2777 patients over a 6-year period, before and after the introduction of a dedicated hip fracture unit (HFU) at a Level 1, Major Trauma Centre. Results. The proposed modelling technique revealed time-dependent trends that explained how the implementation of the HFU influenced mortality rate in patients sustaining proximal femoral fragility fractures. The technique allowed modelling of the entire time-series without bias to the point of intervention. Modelling the binary variable of interest directly, as opposed to a transformed variable, improved the interpretability of the results. Conclusion. The proposed segmented linear regression modelling technique using maximum likelihood estimation can be employed to effectively detect trends in time-series of binary variables in retrospective studies.
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Toyibah, Dzuriyatun. "The Gender Gap and Career Path of the Academic Profession Under the Civil Service System at a Religious University in Jakarta, Indonesia." KOMUNITAS: International Journal of Indonesian Society and Culture 10, no. 1 (September 4, 2018): 1–13. http://dx.doi.org/10.15294/komunitas.v10i1.12228.
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In this article I argue that male academics under civil servant system in a religious university still dominate the highest academic positions. This study applies logistic regression (binary and ordinal regression) since the available data, especially for dependent variable, is categorical and it does not fulfil the assumption of ordinary least square. By applying ordinal regression, gender is found to be undetected compared to other variables (age, length of tenure, and educational qualifications). Nevertheless, a statistical analysis utilising binary regression indicates that gender is a significant factor along with length of tenure and educational qualifications. The data obtained from the religious university is made up of the curriculum vitae of 749 academics in 2012 who are nearly all civil servants at the university.
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Jarvise, Richard A., and Herbert E. Huppert. "Solidification of a binary alloy of variable viscosity from a vertical boundary." Journal of Fluid Mechanics 303 (November 25, 1995): 103–32. http://dx.doi.org/10.1017/s0022112095004198.
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We analyse the complete solidification from a side boundary of a finite volume of a binary alloy. Particular emphasis is placed upon the compositional stratification produced in the solid, the structure of which is determined by the competition between the rates of solidification and of laminar box filling by the fractionated fluid released at the solid/liquid interface. It is demonstrated by scaling arguments that numerical calculations performed at relatively low values of the Rayleigh and Lewis numbers may be used to describe equally well laboratory experiments previously performed at moderate Rayleigh and Lewis numbers and the high-Rayleigh-number, high-lewis-number convective regime expected during the solidification of a large magmatic body, provided that the balance between solidification and laminar box filling is maintained. This balance can be represented by a single dimensionless group of parameters. The boundary-layer analysis is extended to fluids whose viscosity is strongly dependent upon temperature and composition, and an effective viscosity is derived which may be used to describe both the magnitude and pattern of compositional stratification in the solid.