Sentences with phrase «logistic regression»

«Machine learning offers new way of designing chiral crystals: Logistic regression analysis model predicts ideal chiral crystal.»

Odds ratios (OR) and 95 % confidence limits (95 % CL) were estimated using logistic regression models for survey data.

The effects of relationship dissatisfaction, life events, emotional distress, and demographic variables on the risk of relationship dissolution were examined using logistic regression analyses.

Second, we conducted multivariate logistic regression analyses in which the association between attachment and mental health symptoms including depression, anxiety and posttraumatic stress symptoms was examined, adjusting for age, gender, orphan status and adverse childhood experiences.

Multiple logistic regression model of client characteristics as predictors of MNCH — FP integration

To probe these findings further, we conducted a set of logistic regression analyses using teacher retention through Year 2 as our binary outcome.

Presence of FMc was treated as a binary outcome in logistic regression models, with age at diagnosis and various disease characteristics as the predictors.

To check for reverse causation, that depressive symptoms may affect subsequent sugar intake from sweet food / beverages, linear regression models of 5 - year change and multinomial logistic regression for change groups were fitted for each cycle, from phases 3 to 5, 5 to 7 and 7 to 9, with CMD at phases 3, 5, 7 respectively, and for change from phase 7 to 9 with depression at phase 7.

Research question 1 was investigated by multinomial logistic regression analyses with bootstrapping.

* p < 0.05 Results from logistic regression models with the presence of depressive symptoms as the dependant variable and physical aggressive behaviors as the independent variable.

We examined independent associations by using multivariable logistic regression analysis.

Binary logistic regression with the enter method was used to find out the significant variables at level of 0.05 % and the CI of odds ratio (OR) was calculated.

Coefficients from binary logistic regression models predicting «abnormal» emotional outcomes at preschool

Results from hierarchical logistic regression analyses indicate that caregiver need for the intervention and family systems variables significantly predicted initial engagement in the intervention, while demographic variables, stressful life circumstances, and family stress failed to significantly influence engagement.

Multivariate stepwise logistic regression was used to investigate the potential risk role of intended place of birth.

Mixed effect logistic regression analyses revealed that adolescent volunteering was associated with an increased likelihood of volunteering in young adulthood (Odds Ratio [OR] 1.29; 95 % Confidence Interval [CI] 1.20 — 1.39; N = 2,648) and of Grade 12 completion (OR 1.14; CI 1.03 — 1.28; N = 2,648), after controlling for family socioeconomic status and adolescent school adjustment.

Analysis included logistic regression adjusted for predictor variables and standardized perinatal mortality ratios.

Moreover, multinomial logistic regressions revealed a profile of children at risk of developing high anxiety symptoms (i.e., high group), characterized by sociofamily adversity, inattention, and low prosociality in the classroom.

Specific statistical areas of expertise include factor and cluster analysis, basic bivariate analyses, repeated measures analyses, linear and hierarchical / mixed models, structural equation modeling, and nonparametric analyses including logistic regression techniques.

In the Black Women's Health Study (N = 59,001), we conducted a nested case control analysis using unconditional logistic regression to estimate the association between breastfeeding and incident hypertension at ages 40 - 65.

However, only in a few of the studies was matching statistically treated through conditional logistic regression or McNemar test.

A particularly nice thing with linear model such as logistic regression is how interpretable they are.

As shown in Tables 1 and 2, the results from the bivariate logistic regression analyses showed a significant association with all eight infectious diseases in both age groups of infants.

Multilevel Binomial Logistic Regression Predicting Within - Family Variation in Sibling Closeness (N = 2,067 dyads)

Univariate logistic regression analyses showed that the incidence of MDD was associated with family cohesion in the previous year and with the total CES - D scores at baseline.

Using logistic regression controlling for zygosity, sex, age at interview, interview form, and correlations within families, the level of standardized neuroticism strongly predicted risk of phobia (OR, 1.67; 95 % CI, 1.58 - 1.77; z = 18.55; P <.001).

Cox survival and logistic regression techniques examine risk and protective factors for survival and links between adverse childhood experiences and cause of death, respectively.

Stepwise backward logistic regression analyses were performed with dichotomised FSS score at contact 2 as dependent variable.

Multivariate logistic regressions compared suicides to MVC deaths that adjusted for age, sex, and race / ethnicity.

This is the first study reporting the use of this technology, called logistic regression analysis, to predict which chemical groups are best for making chiral molecules.

Burton and Dickey then developed logistic regression and random forest models using the ArmChair Analysis play - by - play data seasons to predict future play types.

The ecological analyses employed logistic regression because sexual risk behavior was operationalized as a binary outcome.

Because it was not possible to examine the independent effects of BMI and edema in the same logistic regression model, these variables were examined in separate models.

We examined early life factors associated with grade repetition through logistic regression and explored reasons for repeating a grade through parent report.

Bioinformatic approaches to the analysis of genetic variability and complex genotype - phenotype relationships will moreover include gene sequence and database analyses, measures of association of haplotypes / genotypes with phenotype, clustering procedures, neuronal networks, fuzzy and other techniques in pattern recognition, similarity measures for discrete patterns (e.g., gene sequences, structures, functions), logistic regression methods, and a spectrum of other techniques.