Sentences with phrase «linear regression analyses»

As with lower marital satisfaction, based on linear regression analyses, the association between stressful life events and infections showed that prenatal stressful life events predicted both higher frequency and a greater variety of infectious diseases during the first year of life.

Finally, multivariate linear regression analyses showed that prenatal relationship dissatisfaction and stressful life events were significantly associated with the frequency, as well as the variety, of infectious disease in the offspring.

Finally, multiple linear regression analyses were conducted to examine the associations between prenatal relationship satisfaction and stressful life events, and the variety of infectious diseases in the offspring.

Using multiple linear regression analyses, our results suggest that higher levels of childhood depressive symptoms and earlier menarche have independent effects on adolescent depressive symptoms.

A series of multi-level logistic and linear regression analyses were performed using the xtmelogit and xtmixed commands to test for mediation by cognitive factors.

Multiple linear regression analyses were used to determine the relative contribution of FR - EXT, emotional warmth, rejection, overprotection and gender to parents» and teachers» ratings of inattention, hyperactivity / impulsivity, aggression, and delinquency.

In the infertile group we performed two linear regression analyses in order to identify predictors of depressive symptoms (BDI) and trait anxiety (STAI - T).

The fine - grained temperament traits that correlated significantly (with p <.01) with internalizing and externalizing problems across groups, determined by bivariate (Pearson's) correlations, were used in the linear regression analyses.

We conducted multiple linear regression analyses and mediation analysis.

In a second step, the prediction of three ODD dimensions by the same parent rating scales was assessed by backward linear regression analyses.

We conducted multiple linear regression analyses using externalizing symptoms as reported by children through the Dominic questionnaire and multiple child, family and socioeconomic characteristics.

We used hierarchical linear regression analyses to test for program effects on parenting stress, parenting behaviors, mental health, satisfaction with social support, and social support need.

Separate linear regression analyses for the preterm children with regard to mothers» reports of children's total problem behavior showed that gestational age was the most important predictor of children's problem behavior (β =.15, p =.016), accounting for a small but significant percentage of the variance (R 2 =.02 p =.016).

Results of the linear regression analyses predicting SMQ scores (top panel) and number of spoken words during the speech tasks (bottom panel) from behavioral inhibition, social anxiety and non-social anxiety symptoms

Control variables in the multilevel modeling and multiple - linear regression analyses included gender, race, and pretest scores on the outcome being predicted.

Generalized estimating equations extension of multivariable linear regression analyses for repeated measures examined predictors of general and specific adherence.

Conventional linear regression analyses examined the relationships of general adherence with post-treatment FM disability and pain intensity.

Hierarchical multiple linear regression analyses were conducted to test whether the income - to - needs ratio predicted brain volumes.

The EM algorithm for Gaussian data is based on iterated linear regression analyses.

Multivariate linear regression analyses showed that whole plant foods scores were negatively associated with MS scores, even after adjustments for a range of potential confounders (P < 0.01).

We also estimated relative indices of inequality (RII) and slope indices of inequality (SII) as summary measures of relative and absolute inequalities of breastfeeding outcomes, respectively, across the entire distribution of maternal education.24 For child IQ, linear regression analyses using GEEs were performed to estimate mean IQ differences in lower maternal education from the reference category in each intervention group and compared between the groups.

Multiple linear regression analysis was applied to explore these possible differences.

Notably, a positive correlation between the mRNA expression levels of DDX3 and p21waf1 / cip1 in 45 HCC specimens was also found by linear regression analysis (r = 0.501, P < 0.001; Fig. 5D).

Tests for trend with the use of simple linear regression analysis were performed by modeling the median values of each fiber category as a continuous variable.

Multiple linear regression analysis with log - transformed plasma concentrations of inflammatory markers as dependent variables and dietary intakes of PHVOs and non-HVOs as independent variables (all as continuous) were used.

This would allow you to a linear regression analysis to see the quality of the fit.

However, although its simple linear regression analysis facilities (including polynomials) provides automatically the option for plotting the fit with CIs for the fitted line / curve and for future observations from the same population, I am unsure about these intervals for autocorrelated data — typically time series.

In May 2005, a second algorithm based on linear regression analysis (Version 2.0) was implemented and all the fields available through this website were updated accordingly.

«A multiple linear regression analysis of global annual mean near - surface air temperature (1900 — 2012) using the known radiative forcing and the El Niño — Southern Oscillation index as explanatory variables account for 89 % of the observed temperature variance.

In Fig. 5B, we add the AMO Index (16) to the multiple linear regression analysis.

This will be tested using linear regression analysis.

Analyses of CBCL change scores were conducted using linear regression analysis.

Effect of GDM exposure on mean levels of childhood adiposity outcomes in multivariate linear regression analysis

However, linear regression analysis showed that gender had no significant effect on level of somatic symptoms, when the effects of centre and emotional distress were controlled for.

Application of multiple linear regression analysis has provided us the following results (Table 5).

For the primary aim, differences in the changes in maternal weight and the EPDS symptoms score between enrolment after GDM diagnosis and 1 year postpartum at the end of the study between the intervention and the control group will be analysed using linear regression analysis.

The results of the multiple linear regression analysis of the associations of marital status and social ties with depressive symptoms according to sex are shown in Tables 4 and 5.

To further understand the association of key variables in predicting romantic inclination, linear regression analysis was carried out with romantic inclination as the outcome variable.

Eight significant predictors for psychological distress were retained with hierarchical multivariate linear regression analysis after controlling for gender: seven predictors (Passive Coping, Active Coping and Social Support — UCL), Self - criticism and Dependency (DEQ), Intrusiveness (IES) and Attachment Anxiety (ECR - R) were general psychological characteristics whereas only one infertility - specific characteristics (Need for Parenthood; FPI) had predictive value.

We conducted multiple linear regression analysis to predict depressive symptoms of middle - aged offspring.

Using stepwise linear regression analysis, the most powerful single predictor was the ADHD T - score, accounting for 12 % of the variance (R =.35, t = 7.8, p <.0001).

Based on a linear regression analysis we compared the specific associations between psychopathology and psychopathy in both male and female delinquent juveniles.

The prediction of children's teacher - rated social skills at 8 y of age from their attachment security at 42 mo of age and the moderating influence of EEG activity was examined for the institutionalized groups (CAUG and FCG) using hierarchical linear regression analysis (see SI Text for further details).

Not exact matches

Many statistical techniques, including analysis of variance and linear regression, were developed by evolutionary biologists, especially Ronald Fisher and Karl Pearson.

Population structure was evaluated by principal component analysis to infer continuous axes of genetic variation, and single linear regression models were applied.

Methods: Here we genotype 276 single nucleotide polymorphisms (SNPs) in 5199 P. falciparum isolates from two Kenyan sites and one Gambian site to determine the spatio - temporal extent of parasite mixing, and use Principal Component Analysis (PCA) and linear regression to examine the relationship between genetic relatedness and relatedness in space and time for parasite pairs.

The ultrasonographic half emptying times were identified by linear regression and used for the statistical analysis.

The relationship between an athlete personal best in competition and back squat, bench press and power clean 1RM was determined via general linear model polynomial contrast analysis and regression for a group of 53 collegiate elite level throwers (24 males and 29 females); data analysis showed significant linear and quadratic trends for distance and 1RM power clean for both male (linear: p ≤ 0.001, quadratic: p ≤ 0.003) and female (linear: p ≤ 0.001, quadratic: p = 0.001) suggesting how the use of Olympic - style weightlifting movements — the clean, in this particular case, but more in general explosive, fast, athletic - like movements — can be a much better alternative for sport - specific testing for shot putters (Judge, et al, 2013).

In this course, students will learn how to use a set of quantitative methods referred to as the general linear model — regression, correlation, analysis of variance, and analysis of covariance — to address these and other questions that arise in educational, psychological, and social research.

In February of 2011, CUNY's Office of Institutional Research and Assessment, headed by University Dean David Crook, released critical data (obtained by Director of Policy Analysis Colin Chellman using linear probability models and logistic regression) demonstrating that, all else being equal (i.e., taking into account all measurable demographic and performance characteristics), CUNY's transfer students were at a disadvantage in terms of graduation compared to native students.