research papers using binary logistic regression

Binary logistic regression is one method frequently used in family medicine research to classify, explain or predict the values of some characteristic, behaviour or outcome. ... 35 out of the 142 (24.6%) peer-reviewed published original research papers between 2013 and 2020 reported using binary logistic regression as one of the analytical ...

student academic achievement binary logistic regression model was used. Moreover, the joint impact of all predictor variables on the dependent variables also determine by using the concept of Nagelkerke R2which is explained in the model summary (table 3). Table 3. Model summary Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square 1 111.

In a binary logistic regression, a single dependent variable (categorical: two categories) is predicted from one or more independent variables (metric or non-metric). This chapter also explains ...

2 The Logistic Regression Model. Let X∈Rn×dbe a data matrix where nis the number of instances (examples) and dis the number of features (parameters or attributes), and ybe a binary. outcomes ...

papers between 2013 and 2020 reported using . ... Binary logistic regression is a statistical technique that ... logistic regression in cancer research," "logistic regression analysis," and ...

Logistic regression is a type of generalized linear model, which is a family of models for which key linear assumptions are relaxed. Logistic regression is an excellent tool for modeling relationships with outcomes that are not measured on a continuous scale (a key requirement for linear regression). Logistic regres-sion is often leveraged to ...

Binary Logistic Regression. Binary logistic regression analysis has become increasingly more common. As mentioned earlier, the dependent (criterion) variable in such an analysis is dichotomous (e.g., male/female, controls/patients, old/young, etc.). Similar to linear regression, the predictors can either be continuous or categorical.

Which we can interpret as the log odds of a white boy (EG=0) seen as having a behaviour problem being equal to -1.56, hence the odds of a white boy having a behaviour problem are: exp(-1.56) = 0.21. The log odds of a black boy (EG=1) having a perceived behaviour problem are - 1.56 + 1.65 = 0.09.

After determining the correlation of each diagnostic regressor with COVID-19 using the Chi-square method, the 15 important regressors were obtained at the level of P < 0.05. The experimental results demonstrated that the binary logistic regression model yielded specificity, sensitivity, and accuracy of 97.3%, 98.8%, and 98.2%, respectively.

RESEARCH PAPER APPROVAL PREDICTING STUDENT SUCCESS: A LOGISTIC REGRESSION ANALYSIS OF DATA FROM MULTIPLE SIU-C COURSES By Patrick B. Soule A Research Paper Submitted in Partial Ful llment of the Requirements for the Degree of Master of Science in the eld of Mathematics Approved by: Dr. B. Bhattacharya, Chair Dr. M. Wright Dr. R. Habib Graduate ...

The method used in this paper is logistic regression, which is a supervised learning method. To select or delete a feature, feature weighting is used. In logistic regression [29], the Sigmoid function is used for classification, which ensures that the output is in the range [0-1]: (4) h θ x = g θ T x = 1 1 + e-θ T x

ABSTRACT. This paper presents a new way to assess the environmental impact on historical artifacts using binary logistic regression. The prediction of the impact on the exhibits during certain pollution scenarios (environmental impact) was calculated by a mathematical model based on the binary logistic regression; it allows the identification of those environmental parameters from a multitude ...

Multivariate analysis with binary response is extensively utilized in dental research due to variations in dichotomous outcomes. One of the analyses for binary response variable is binary logistic regression, which explores the associated factors and predicts the response probability of the binary variable. This article aims to explain the statistical concepts of binary logistic regression ...

This presentation examines the appropriate circumstances for utilizing this specific regression technique and provides guidance on assessing the adequacy of the regression model. 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 ...

This study used binary logistic regression to predict social trust with five demographic variables from a national sample of adult individuals who participated in The General Social Survey (GSS) in 2012. The five predictor variables were respondents' highest degree earned, race, sex, general happiness and the importance of personally assisting people in trouble.

The randomized sample included (150) people among the elderly in Al-Hilla city, the research included focusing on (14) independent variables and most of these variables were found to have significance, effect and contribution to the logistic regression - binary response (not sick(0), sick(1)) model are (4) variables (cigarette smoking, exercise ...

Logistic regression was developed in late 1960s and early. 1970s 1,2,3 and became popular among researches in various. fields, particularly among health researchers. Logistic regression can be of ...

medicine research is strengthening the use of appropriate research methods.6 Binary logistic regression is one method that is particularly appropriate for analysing survey data in the widely used cross-sectional and case-control research designs.7-9 In the Family Medicine and Community Health (FMCH) journal, 35 out of the 142 (24.6%)

Traditional logistic regression analysis is widely used in the binary classification problem, but it has many iterations and it takes a long time to train large amounts of data, which is not applicable.

The binary logistic (BL) regression uses the equation: Y [ln (P/1−p)] = b 0 + b 1 X 1. A step up from LR is logistic regression. We do not use a linear model to show the relationship between the independent variable (X) and the likelihood of the outcome, because that could lead to predicted probabilities that are not in the range of 0-1.

In SPSS, select Analyze > Regression > Linear. Pull over dependent variable: course success (GOR of A, B, C or P/CR) Pull over candidate predictor variables. Select "Enter" method. Open Statistics dialog box, check Collinearity diagnostics. Setting Up Multicollinearity Test.

SES was determined using Area Deprivation Index.(3) We roughly divided our population into the top 50% and bottom 50% based on SES. A chi-squared test and binary logistic regression analysis evaluated the association between SES group and follow-up visit occurrence.ResultsThe study included 1,329 pediatric patients with CGI.

The binary logistic regression depicted that a married farmer tends to have a higher production level since they are motivated to work hard and eager to earn more income for their families' needs. In addition, the model shows that farmers with lower monthly incomes and smaller paddy farms tend to have a higher production level.

Image generated using DALL.E. Logistic regression is a statistical model used in binary classification. In a binary classification problem, the target has only two categories, so the machine learning algorithm should classify data into one of these two categories. Logistic regression is named after the logistic function which is used to predict ...

These "AI clusters" were compared against the "expert clusters" - based on the Partial Risk Adjustment in Surgery (PRAiS) v2 protocol - for 1) consistency using adjusted rand index (ARI) between the clusters and 2) predictive performance using area under ROC (AUC) of logistic regression models using cluster memberships as a categorical variable.

Abstract. Medical research workers are making increasing use of logistic regression analysis for binary and ordinal data. The purpose of this paper is to give a non-technical introduction to logistic regression models for ordinal response variables. We address issues such as the global concept and interpetation of logistic models, the model ...

In this paper, the Binary Logistic Regression Analysis BLRA technique has been used and applied for building the best model for. Hepatitis disease data using best subsets regression and stepwise ...

Results are from logistic regression models controlling for age, Hispanic or Latina/x ethnicity, marital status, parity, tobacco use, prenatal visit utilization, stillbirth, and placental abruption. Other race includes Alaska Native, American Indian, Chinese, Filipino, Guam/Chamorro Hawaiian, Indian, Japanese, Korean, Other Asian/Pacific ...