Multiple logistic regression model
Nonparametric bootstrapping for multiple logistic regression model using R
... a regression model is an important way to rep- resent heterogeneity in a ...for multiple logistic regression model associated with Davidson and Hinkley's (1997) “boot” library in ...
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Simulation Program to Determine Sample Size and Power for a Multiple Logistic Regression Model with Unspecified Covariate Distributions
... binary logistic regression model ...a logistic regression model correspond- ing to the actual data ...methods, logistic regression, and other simulations such as ...
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Risk Factors for Hospitalization Due to Hypoglycemia in Diabetic Patients in Northeast Thailand
... final multiple logistic regression model, which included five variables, found that the factors related to a greater likelihood of hospitalization due to hypoglycemia among diabetics were ...
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Predictors of Adherence to Antiretroviral Medications in Children and Adolescents With HIV Infection
... a multiple logistic regression model (see Table 6), factors associated with increased odds of adherence self-assessment included older age (OR: ...multivariate model in Table ...
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Predictive Modeling of the Brand Equity: Analysis Based on Multiple Logistic Regression and Backward Stepwise Model Selection Methods
... the multiple logistic regression model that predicts the probability of Brand Equity (BE) using awareness, recall, relate, purchase, knowledge, trials, association, recommendations, salience, ...
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Probabilistic modelling of rainfall induced landslide hazard assessment
... probabilistic model based on multiple logistic regression ...a multiple logistic regression model is applied and landslide hazard probability maps are produced ...
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A Radiologic Score to Distinguish Autoimmune Hypophysitis from Nonsecreting Pituitary Adenoma Preoperatively
... a multiple logistic regression model, 8 (relation to pregnancy, pituitary mass volume and symmetry, signal intensity and signal intensity homogeneity after gadolin- ium administration, ...
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Modeling Diabetes Risk Factors (A Case Study of Focus Medical Centre in Kiambu, Kenya 2016)
... to model risk factors of diabetes (A case study of Focus Medical Center in Kiambu, Kenya) for the year ...A multiple logistic regression model was fitted and the fitted ...
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Simulation-extrapolation estimation in parametric measurement error models
... Kernel density estimates of the distributions of the regression parameters in the multiple logistic regression model simulation - a-c, normal measurement error; e-f, uniform measurement [r] ...
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A Review of the Logistic Regression Model with Emphasis on Medical Research
... between multiple independent va- riables and a categorical dependent variable, with emphasis on medical ...on logistic regression and 37 research articles published between 2000 and 2018 which ...
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A Logistic Regression Model of Determiner Omission in PPs
... binary logistic regression does not pro- hibit monocausal explanations, typical models for binary logistic regression employ more than one factor, and the value of the coefficients mod- els ...
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Some new proposed ridge parameters for the logistic regression model
... a multiple regression model are ...linear regression models using ordinary least squares(OLS) technique and the LR model using the maximum likelihood estimation (MLE) ...ridge ...
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A Statistical Analysis Of Migration Using Logistic Regression Model
... Multinomial Logistic Regression which has three or more categories without ...Ordinal Logistic Regression which has three or more categories with ...In logistic regression the ...
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ON THE EVALUATION OF NON PROBABILITY SAMPLING AND LOGISTIC REGRESSION MODEL USED IN DRUG ADDICTION
... Logistic regression analysis (LRA) is a popular and widely used ...of multiple regression analysis to research situations in which the outcome variable is ...
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An Investigation of Malaria Predictors Using Logistic Regression Model
... -------- (1.8) To find the values of the parameters that maximize the above function, we differentiate this function with respect to α and β and set the two resulting expressions to zero. An iterative method is used to ...
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A Bayesian hierarchical logistic regression model of multiple informant family health histories
... hierarchical logistic regression model that accounts for variability in outcome arising from both informants and the family members they are reporting on, together with informative ...
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Cyclin alterations in diverse cancers: outcome and co-amplification network
... Cox regression model were fit to assess the association between OS and patients’ characteristics and CCN alteration ...status. Multiple logistic regressions were fit to analyze the association ...
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Analyzing and Predicting outcome of IPL Cricket Data
... linear regression [7]. Akhil Nimmagadda et. Al, proposed a model using Multiple Variable Linear Regression and Logistic regression to predict the score in different innings and ...
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Multivariate logistic regression analysis using multilevel model
... The aim of statistics is the development and application of methods for extraction of valuable results from data sources and experiences. This science is divided into two main statistics groups including descriptive and ...
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Discovering potential blood-based cytokine biomarkers for Alzheimer’s disease using Firth Logistic Regression
... ordinary logistic regression model cannot be obtained when there is complete separation and the alternative is Firth logistic regression which uses a penalised Maximum Likelihood in ...
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