Results from the binary logistic regression
Validation and Performance Analysis of Binary Logistic Regression Model
... fitting results that inaccurately predict outcomes on new ...the logistic regression coefficients, the plausibility and usability of the fitted logistic regression function, and the ...
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factors for temporomandibular disorder: Binary logistic regression analysis.
... The results of the present study and a brief review of the literature demonstrate the influence of socioeconomic/ demographic factors on ...the results probably will be the ...
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Waiting Time Factor In Public Transport By Binary Logistic Regression
... obtained from a single linear regression conducted by employing the Microsoft Excel analysis tools pack resulted in the estimation of intercept constant and the regression coefficient, α ...
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Application of Binary Logistic Regression Model to Assess the Likelihood of Overweight
... Overweight and obesity has become a major public health problem from the last two decades in the world. The worldwide problem of overweight and obesity has affected the individual, family, society, and the nation. ...
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Binary logistic regression analysis on addmiting students using jamb score
... data results in two parallel Jodhpur National University, Narnadi, Jhanwar Road, Boranada, lines, each corresponding to a value of the dichotomous outcome or dependent ...variable. Logistic ...
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Oversampling Method To Handling Imbalanced Datasets Problem In Binary Logistic Regression Algorithm
... the results obtained, then got the following conclusion: Based on the comparison of RWO-Sampling method with roulette approach, RWO-Sampling with random replicate and Random Oversampling (ROS) approach proved that ...
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COVARIATES AND SAMPLE SIZE EFFECTS ON PARAMETER ESTIMATION FOR BINARY LOGISTIC REGRESSION MODEL
... in binary logistic regression model. The results of this simulation study show that the estimation of parameters is severely affected by small sample ...the results of the simulation ...
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Sparse Bayesian binary logistic regression using the split-and-augmented Gibbs sampler
... The main differences between the methods implemented are twofold. First, the number of iterations and thereby the computa- tional time associated to each method can widely differ, see Table 3. Second, the results ...
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Prediction of Antibiotics Residues in Raw Milk by Using Binary Logistic Regression Model
... selected from 10 collection centers and farms Industrial in the Province of Khorasan Razavi ..., binary logistic regression model to predict the presence or absence of antibiotic residues in ...
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Sparse Bayesian binary logistic regression using the split-and-augmented Gibbs sampler
... The main differences between the methods implemented are twofold. First, the number of iterations and thereby the computa- tional time associated to each method can widely differ, see Table 3. Second, the results ...
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On the Comparison of Deep Learning Neural Network and Binary Logistic Regression for Classifying the Acceptance Status of
... 4 Conclusion Based on the research that has been done, the average results of the sensitivity values of the three methods produce a high percentage of value. It means that almost all positive classes can be ...
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Machine Learning: An Approach in Identifying Risk Factors for Coercion Compared to Binary Logistic Regression
... these small groups in analysis. Further studies should re-evaluate our results with a bigger sample size. The analysis was based on retrospectively collected data, and it was not possible to assess the subjective ...
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Applying Penalized Binary Logistic Regression with Correlation Based Elastic Net for Variables Selection
... . Results To examine the performance of the correlation based elastic penalty we compare it with three well-known penalization methods; elastic net, LASSO, and ...
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A Multivariate Binary Logistic Regression Modeling For Assessing Various Risk Factors That Affect Diabetes
... There are many factors which may influence the prevalence of diabetes throughout the country and the identification of these factors is very crucial in order to facilitate change or to make necessary measures to control ...
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No rationale for 1 variable per 10 events criterion for binary logistic regression analysis
... Simulation results will therefore reflect the net effect of inaccurate estimation and handling of separated data ...simulation results due to different approaches of detecting and handling separated data ...
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Binary Logistic Regression
... Stress does not contribute to the model -- though it contributed to the main effect model The Gender * Loneliness interaction does not contribute to the model. The Gender * Stress inter[r] ...
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Binary logistic regression modeling with TensorFlow™
... Abstract: Logistic regression model is one of the most widely used modeling techniques in clinical medicine, owing to the widely available statistical packages for its implementation, and the ease of ...
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Application of binary logistic regression in credit scoring
... collected from 3985 loan applicants both “good” and ...conducted binary logistic regression in IBM SPSS software, which calculated the predicted probability of the ...variables from the ...
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The Application of Binary Logistic Regression Analysis on Staff Performance Appraisal
... Linear logistic regression is one of the many special cases of generalized linear ...Linear logistic regression technique fits the model for binary or ordinal response data using the ...
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Random Forest vs Logistic Regression: Binary Classification for Heterogeneous Datasets
... and logistic regression for datasets com- prised of various underlying structures: (1) increasing the variance in the explanatory and noise variables, (2) increasing the number of noise variables, (3) ...
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