Logistic Regression

Variables with a significant univariate relationship were loaded into a series of binary logistic regression models.

By our logistic regression analysis, the age of pregnancy might be associated with type 1 diabetes.

Multivariate logistic regression analysis revealed that hyperuricemia was an independent risk factor of CAD.

Multiple logistic regression analysis was used to identify factors associated with a good outcome.

With binary logistic regression analysis to CHD, to evaluate whether serum uric acid is an independent risk factor of CHD or not.

The result of logistic regression analysis indicated that serum bilirubin, uric acid were the independent risk factors.

A preference index and logistic regression were used to determine the important habitat variables for each species.

Methods: Cluster randomized sampling method for selecting subjects and multinomial logistic regression were used to analyze these data.

Logistic regression showed pulmonary artery velocity to be independently associated with thrombosis.

You can use logistic regression to describe which input columns are important in determining the state of the predictable column.

Descriptive Statistics, Chi-Square, Independent T Test, ANOVA , and Logistic Regression were used to analyze the data.

Multiple logistic regression was used to identify predictors from self-reports and clinical examination.

Multivariate logistic regression and odds ratio assessment were performed as well.

Logistic regression was used to identify factors associated with antibiotic misuse.

A logistic regression test of the distribution of different blood types found no significant difference between patients and controls.

We used Chi-square test, analysis of variance, and logistic regression to determine the factors that affect the choice of delivery mode.

The clinical information were compared and logistic regression analysis was performed in two groups.

The Microsoft Logistic Regression algorithm provides logistic regression support for more business flexibility.

Linear and logistic regression models were constructed examining the relationship between urbanicity and chronic disease risk.

Logistic regression was applied to explore whether social capital was associated with self-rated health.

Logistic regression analysis indicated that female was a significant risk factor associated with perifoveal PVD.

Logistic regression models were used to compute adjusted odds ratios.

We identified predictors for completion through multivariate logistic regression.

Finally, in order to understand this issue more deeply, we also build churn prediction model with Logistic regression model.

This survival difference persisted after multiple logistic regression analysis.

Under these basic financial indicators, we using logistic regression to generate regression model, and verified.

higher blood urea nitrogen and lower serum creatinine were also associated with mortality in logistic regression models.

Association between variables was examined by multiple logistic regression analysis and odds ratios.

However, the Microsoft Logistic Regression algorithm supports additional functions, listed in the following table.

Analyses were adjusted for confounding variables using logistic regression.

Each factor's contribution to the CFR was determined through multivariate logistic regression.

Logistic regression test: Elevated CK is an independent risk factor.

The team used logistic regression analysis to test the influence of the season (spring) in the results.

Both logistic regression and artificial neural network (ANN) models were developed.

conditional logistic regression models.

Instead of creating a straight line, logistic regression analysis creates an "S" shaped curve that contains maximum and minimum constraints.

The relation between FMD and coronary lesions was analyzed by Logistic regression analysis.

The correlation of the Lequesne index and X-ray manifestation was analyzed by logistic regression analysis.

Comparing Variable Selection Methods for Microarray Classification Models Based on Logistic Regression.

We used logistic regression analyses to estimate the relative risk of birth outcomes with 95% confidence intervals.

Effects of possible risk factors for UI were estimated by multiple logistic regression.

Date were drawn from the '2001 Taiwan Election and Democratization Survey' and analyzed in a 2-level logistic regression model.

In chapter three, the focus is on the model construction for personal credit scoring by logistic regression.

A logistic regression model must contain a key column, one or more input columns, and one or more predictable columns.

Based on a descriptive analysis, this paper adopts the 2-Logistic regression model for regression statistical analysis.

A way to solve this problem is to perform logistic regression.

The risk for influenza A (H5N1) infection was assessed by using adjusted odds ratios based on multivariate logistic regression analysis.

Association of fatty infiltrations with clinical and sonographic features were evaluated by a stepwise logistic regression analysis.

the veracity of model No. 2 based on LOGISTIC regression analysis reaches 86. 2%.

A logistic regression model was then used to study factors predicting severity of foot injury.

Analysis on the Influential Factors for the Quality of Life of Patients with Gastric Carcinoma by Cumulative Odds Logistic Regression

Logistic regression model for the relationship between serum homocysteine and coronary atherosclerosis

Logistic regression analysis for risk factors of intermediate syndrome after acute organophosphorus pesticides poisoning

Logistic regression analysis of factors associated with hypertension after operation of aldosterone producing adenoma

Forecasting Research on Listed Companies Acquired by State-Owned Enterprises and Private Enterprises Using Logistic Regression Model

Multi-factor Logistic Regression Analysis on Occupational Injuries in Workers of an Iron and Steel Enterprise

Regression Modeling Strategies With Applications to Linear Models, Logistic Regression, and Survival Analysis

Demonstrational Research on Investors'Motivations on Dualistic Logistic Regression Model

Multivariate Logistic Regression Analysis on Risk Factors of Hepatic Encephalopathy in Patients with Acute Sever Hepatitis

The multivariate logistic regression analysis in patients with extracorporeal shock wave lithotripsy

Logistic Regression Analysis on Willing Pay Proportion of Medical Expenditure by Workers about Medical Insurance in Shanghai

The morbidity of chronic diabetic complication with logistic regression analysis of related potential risk factors

Antibody selection and combination in benign and malignant body cavity effusions with logistic regression

Non-conditional Logistic regression analysis on erythrocyte membrane MDA burden in workers exposed to quartz dust

Logistic regression analysis of hyaline membrane disease related factors in preterm infants

Multinomial logistic regression for affecting factors of cervical erosion

Logistic Regression Analysis on Genetic and Environmental Factors of Children's Simple Obesity

Determinants of Hospital Delivery in Rural China--A Multiple Logistic Regression Analysis

Logistic Regression Analysis for Early Dislocation after Total Hip Replacement

binary logistic regression analysis on the dangerous syndrome - type of chest stuffiness and pains

Analyze of Unconditional Logistic Regression on Prostate Diseases and Related Factors of Ji'nan City

In their logistic regression models to establish odds ratios (OR), the authors adjusted for demographics;

descriptive statistics and t -tests; linear and logistic regression;

Logistic regression analysis on the risk factors of radiation pneumonitis

Logistic regression analysis on payment ability and payment willing of demand side

disease; risk factors; logistic regression;

Prediction Model of Financial Failure Based on the Principle Components-logistic Regression Method

Logistic regression analysis of related factors of diabetic nephropathy in type 2 diabetes

Zonation of Landslide Hazards Based on Logistic Regression