Sentences with phrase «effect model regressions»

A suite of generalized linear mixed - effect model regressions were fitted to the data.

Not exact matches

, Lingjie Ma and George Patterson apply a quantile regression model (considering effects across the distribution) to investigate long - run relationships between the price of gold and various economic and financial variables.

They identify idiosyncratic volatility shocks as large deviations from the volatility predicted out - of - sample by a regression model that accounts for market, size and book - to - market effects.

Using these variables, a fixed effects regression model is employed.

When categories of human milk were entered into the logistic regression models, a dose - response effect was not observed.

The effect of the timing of pacifier introduction (≤ 2 weeks and ≤ 6 weeks) on breastfeeding duration at 2 and 3 months was evaluated using logistic regression modeling.

Kaplan - Meier and Cox proportional hazards survival analyses were used in unadjusted and adjusted analyses of the effect of pacifier use on breastfeeding duration.19 Logistic regression modeling was used to evaluate the effect of pacifier timing on breastfeeding duration.20 Significance levels were not adjusted for multiple comparisons.

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.

They then conducted a series of logistic regression models that included fixed effects for year and state, and adjusted for demographic characteristics, school characteristics, and other state alcohol policies.

In multivariable regression models, effects of prenatal exposure were examined on placental size, structure, and presence of infections and meconium.

Multilevel logistic regression was used to estimate the odds ratios (ORs) for conversion to laparotomy, CRM +, intraoperative complications, and postoperative complications between treatment groups, adjusting for the stratification factors, where operating surgeon was modeled as a random effect.

To investigate to what extent these effects can be controlled for, we used a linear regression model that allows incorporating additional covariates.

To examine the prospective association of sugar intake from sweet food and beverages, a random effects logistic regression model (REM) was performed using the STATA command xtlogit 48, with exposures at phases 3, 5, 7 and 9 for GHQ caseness, and at phases 7 and 9 for CES - D caseness.

A test for linear trend of effects across coffee consumption categories was performed by regressing each log RR on the ordered categorical variable for coffee in 5 levels using a random - effect meta - regression model.

Multiple - linear mixed - effect regression models were used to evaluate the association of sex hormones and sex hormone — binding globulin (SHBG) with % DBV, ADBV, and ANDBV.

A logistic regression model was fit to evaluate the effects of each sociodemographic variable level on odds of screening positive for depression controlling for each of the other sociodemographic variables.

Estimates from regressions with detailed controls, nearest - neighbor models, and propensity score models all indicate large, positive, and statistically significant relationships between computer ownership and earnings and employment, in sharp contrast to the null effects of our experiment.

In regression models incorporating teacher fixed effects, absences are associated with lower student achievement in elementary grades.

To be sure, statewide analyses can provide accurate estimates of the impact of school resources — but only if the analyst includes within the statistical model all the factors that affect student performance and, in the standard linear regression model generally favored by RAND, if these factors have a constant, additive effect on student achievement.

To examine the effects of these variables on collective sense of efficacy, we used a regression model, entering the key variables identified above in a first step, and then entering potential mediators: school size, the school level (elementary / secondary), percentage of non-white students, percentage of students in poverty, and the individual «s position (principal or assistant principal).

They use multiple regression with and without school fixed effects to model which teachers are most likely to be tapped and which principals are most likely to tap teachers.

The basic idea is to estimate the trend component, by smoothing the data or by fitting a regression model, and then estimate the seasonal component, by averaging the de-trended seasonal data points (e.g., the December seasonal effect comes from the data points for all Decembers in the series).

A multivariable random effects logistic regression model was used to identify risk factors significantly associated with seropositivity while accounting for clinic - to - clinic (or shelter) variability.

A final simple linear regression model was fit and concluded that the number of searches a dog performs has a statistically significant effect on a dog's energy level (t = − 3.01, d.f. = 1, p - value = 0.0033).

A nested negative binomial regression model was created with intervention or control as the main effect, and type of pet (Cat or Dog) and age of pet (under two years, 2 - 7 years, or over 7 years) as covariates.

A nested logistic regression model was created with group (intervention or control) as the main effect, and type of pet (Cat or Dog) and age of pet (under two years, 2 - 7 years, or over 7 years) as the covariates.

I would also be interested to hear more about the regression model used for the iRF efficacy estimates potentially creating the seemingly un-physical situation where a zero - forcing causes a non-zero effect on temperature.

We concluded that the original regression model was relatively insensitive to the direct effects of forest treatments, including time since treatment, on runoff.

A fairer comparsion would involve also adjusting the observations to account for the effects of internal variablity (e.g. by regression analysis to remove the effects of ENSO and volcanic forcings which the models do not include).

The transformation effected by the IPCC, by recasting Forster / Gregory 06 in Bayesian terms and then restating its results using a prior distribution that is inconsistent with the regression model and error distributions used in the study, appears unjustifiable.

Now you just need to do a «multiple regression mixed model», using also solar inputs, compensation for urban effects / % forested / % agriculture within buffer areas, and whatever other variables are appropriate in addition to the CO2 variable for predicting tempertature.

McKitrick and Michaels show «Using the regression model to filter the extraneous, nonclimatic effects reduces the estimated 1980 — 2002 global average temperature trend over land by about half.»

Subgroup analyses: We will examine whether there is evidence that the intervention effect is modified for subgroups within the trial participants using tests of interaction between intervention and child and family factors as follows: parity (first - born vs other), antenatal risks (2 vs 3 or more risk factors at screening), maternal mental health at baseline (high vs low score) 18, 62, 63 and self - efficacy at baseline (poor vs normal mastery) 35 using the regression models described above with additional terms for interaction between subgroup and trial arm.

The regression models were then expanded to test the independence of the effects of adverse childhood experiences while controlling for established predictors of age - related - disease risks.

This interpretation is strengthened by the observation that the associations among television and children's consumption of fruits, vegetables, and juices; all meats; and pizza, salty snacks, and soda remained statistically significant in the full regression models, where the effects of socioeconomic and other confounding factors were controlled.

Testing statistical assumption for regression coefficients in the models reveals that some hypotheses of this research on the effects of variables is confirmed (β ≠ 0).

When exposure to movie smoking was added to a regression model containing parental R - rated movie restriction, the estimates for the effect of movie restrictions on adolescent smoking dropped substantially.

Maternal age at delivery, ethnicity, smoking during pregnancy, parity, paternal diabetes status at follow - up, family social class, sex, offspring physical activity, and offspring smoking habits were not found to be confounders and had no effect on offspring risk of type 2 diabetes / pre-diabetes when entered in multiple logistic regression models.

Generalized mixed - model regressions were used to estimate the differences in covariates between groups and to test direct intervention effects on primary and secondary outcomes.

Two separate moderated regression models were tested for each indicator of quality of life using the SPSS 17.0 statistical software package; one model tested the moderating role of marital quality in the effects of self - reported (subjective) vision controlling for visual acuity (objective vision) and the second tested the same in the effects of objective vision controlling for subjective vision.

We implemented unadjusted and adjusted analyses (potential prognostic factors listed in table 2) of the outcomes (all quantitative) by using random effects linear regression models fitted by maximum likelihood estimation to allow for the correlation between the responses of participants from the same maternal and child health centre.29 We present means and standard deviations for each trial arm, along with the mean difference between arms, 95 % confidence intervals, and P values.

Multiple regression analysis models with dummy variables assessed the effects of IPPE, MSPSS, TAS - 20, Social Sharing, and Mental Rumination on GDS across the subgroups of participants.

To put the effect sizes for the hypothesized associations on wave 6 reckless driving into perspective, we re-ran the final model using logistic regressions (for the connections between the wave 6 indicators and the wave 6 latent variables) to obtain odds ratios (OR) for the indirect effects of wave 1 predictors on the individual wave 6 reckless driving items.

Generalized regression models (logistic regression for dichotomous outcomes, linear regression for continuous outcomes) were used to estimate the overall adjusted effects of Healthy Steps.26, 27 These models included site variables to account for the fact that families within sites tend to respond more similarly than those at different sites.

The following level 1 within - individual model was specified: log (cortisolti) = π0i + π1i (sample) + π2i (time) + eti, with log - transformed cortisol values as the dependent variable, π0i as the estimated intercept of cortisol at waking, π1i as the estimated slope of cortisol from waking to bedtime (with «sample» representing whether the sample was collected at waking [0] or bedtime [1]-RRB-, π2i as the regression coefficient representing the effect of the time - varying covariate (with «time» representing the sample collection time), and eti as the within - individual error.

Fact: «Using data from subsets ranging in size from 777 to 1,501 children from the child supplement to the National Longitudinal Survey of Youth (NLSY), a series of multivariate regression models were tested to determine whether the effects of nonresident father involvement on child well - being vary by race, mother's education, or whether the child was born within or outside of marriage.

Using publicly available community - level AEDI data, 62, 63 we ran a two - level multilevel logistic regression model for one aggregate developmental outcome measure (ie, risk of developmental vulnerability; figure 3A) and an example simulation (figure 3B) using a total sample of 181 500, with the proportion of Aboriginal children in each LGA derived from ABS estimates.64, 65 Binomial outcome data were simulated assuming a baseline risk of being vulnerable of 21 % and a community - level random effect based on the actual variation in the published data (figure 3A).

2 Second, those variables found to be significantly associated with the dependent variable in the forward stepwise model were entered into a forced entry regression which was able to account for the survey's complex sample design (in particular, the effects of clustering and associated weighting) when calculating odds ratios and determining significance values.

Odds ratios estimate the effect of each individual independent variable on the outcome variable, adjusted for all other independent variables in the regression model.

Initial modelling revealed the effects of job loss were not different for the two family types and the job loss event for any of the drivers so for each driver both family types were included in the same regression model.