Sentences with phrase «regression models estimated»

Logistic regression models estimated the adjusted association between correlates and the four indicators of limited social participation among the adolescents with ASD (Table 5).

Logistic regression models estimated the strength of association between SDP and co-occurring risk factors.

Not exact matches

This adjustment has historically been important, as adjusting for that embedded profit margin significantly improves the relationship between the CAPE and actual subsequent market returns (something we can demonstrate both with algebraic return estimates and regression models — see Margins, Multiples, and the Iron Law of Valuation).

We used multiple regression to estimate the differences in total cost between the settings for birth and to adjust for potential confounders, including maternal age, parity, ethnicity, understanding of English, marital status, BMI, index of multiple deprivation score, parity, and gestational age at birth, which could each be associated with planned place of birth and with adverse outcomes.12 For the generalised linear model on costs, we selected a γ distribution and identity link function in preference to alternative distributional forms and link functions on the basis of its low Akaike's information criterion (AIC) statistic.

A confounding variable was defined for analysis as one for which there was at least a 5 % difference in the regression coefficient estimates for type of feeding in regression models with and without the potential confounding variable.

Reduced logistic regression models with birth weight substituted for gestational age were also estimated.

After examining the unadjusted, bivariate associations with delayed OL, we used logistic regression analysis to estimate the adjusted odds ratio (OR) and 95 % CI in multiple variable models.

The former built a seat and vote share prediction model based on huge quantities of fieldwork (7000 interviews per week) plus the now - famous Multilevel Regression and Post-stratification (or MRP) that converted that data into seat - by - seat estimates.

The annual number of CRT and CRT payments per child between the ages of three to five years of age were estimated with a zero - inflated negative binomial regression and a hurdle model, respectively.

The ~ 6 - million - km2 Amazonian lowlands were divided into 1 ° cells, and mean tree density was estimated for each cell by using a loess regression model that included no environmental data but had its basis exclusively in the geographic location of tree plots.

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 estimate the proportion of each racial disparity attributable to within - plan differences and to determine the correlation between the outcome measure results and racial disparities in the results, we fitted multilevel linear regression models predicting the result of each HEDIS indicator.

Logistic regression models were used to estimate the association between presence of FMc and disease status, while adjusting for age and other confounding factors.

To compare our results with the published data and estimate the magnitude of the association between fiber and elevated CRP, we used logistic regression models to compute the odds ratios (ORs) and 95 % CIs for the probability of having an elevated CRP (21).

Hazard ratios and 95 % confidence intervals for mortality associated with coffee consumption were estimated with the use of Cox proportional - hazards regression models, with person - years as the underlying time metric; results calculated with age as the underlying time metric were similar.

We used time - varying Cox proportional hazards regression models with age as the time scale to estimate the hazard ratio (HR) and 95 % CI for mortality associated with animal and plant protein intake.

We estimated hazard ratios (HRs) and 95 % confidence intervals (CIs) with time since entry into the study as the underlying time metric using Cox proportional hazards regression models.

Relative risks of clinical depression were estimated using Cox proportional hazards regression models.

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.

This estimate comes from a regression model that parallels the reduced forms reported in Table 4, where the dependent variable is an indicator variable equal to 1 if a student switched schools and the instrument is ever offer.

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.

Using regression and multi-level path models, I will estimate the impact of practices on transfer and degree attainment and examine which students benefit the most from various practices.

In order to estimate the contribution of student SES (calculated as the percentage of students in a school eligible for free or reduced lunch) to relationships described in the path model between the three teacher variables and student achievement, we computed three hierarchical regressions.

The second set of regressions focused on the betas» temporal stability by estimating the Market Model over thirty - four month subperiods.

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).

«The estimates are derived from a statistical model using multilevel regression with post-stratification (MRP) on a large national survey dataset (n > 18,000), along with demographic and geographic population characteristics.

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.

What this model shows is that if orbital variations in insolation impact ice sheets directly in any significant way (which evidence suggests they do Roe (2006)-RRB-, then the regression between CO2 and temperature over the glacial - interglacial cycles (which was used in Snyder (2016)-RRB- is a very biased (over) estimate of ESS.

Uncertainties in the regression models and fits used to distinguish between periodic variations and trends in the different databases appear to be a significant source of uncertainty in the estimates of longterm trends.

The most likely ECS value according to this analysis is 4.0 K — shifted upward relative to the regression estimate, toward the values in the cluster of models (around numbers 25 and 26) with relatively high ECS that are consistent with the observations.

What many previous emergent - constraint studies have done is to take such a band of observations and project it onto the vertical ECS axis using the estimated regression line between ECS and the natural fluctuations, taking into account uncertainties in the estimated regression model.

All calculations (i.e. here or here) using the regression method - observed GMST vs. the total forcings - come to TCR estimates which are well below the mean of the CMIP5 models of 1.8 K / doubling CO2.

Solid red lines are estimates of cumulative runoff under current forest conditions using original Baker - Kovner regression model [20].

Runoff from thinned forests was approximately 20 % greater than unthinned forests (as estimated using original Baker - Kovner regression model) in both droughts and pluvials (data not shown).

We developed a range of estimates for the extent, pace, and intensity of forest thinning that could be conducted over this larger geography, grouped these estimates into runoff scenarios, and ran the scenarios using the revised and original regression models to estimate additional runoff from treatments and total watershed runoff.

Then we add / subtract this scaled interannual regression map to / from the anthropogenically - forced component of the trend over the next 30 years, the latter estimated from the ensemble - mean of the CESM - LE (Fig. 8) or the ensemble - mean of the 38 CMIP5 models (Fig. 9).

To estimate the future US SW climate evolution using the regression model we need to make an assumption concerning the future AMO behavior.

For a climate model that has some correlation with the past data the model estimates should be converted into a recalibrated estimate using the regression equation.

As I wrote originally, Marvel et al. used an unphysical regression model to estimate iRF efficacies.

For all the ensemble members, we used one regression model using 27 years of past model data and NSIDC Merged SMMR and SSM / I sea ice concentration data to estimate and correct for systematic model bias.

All CMIP5 models with such climate sensitivities of 2.7 K or below have an ECS value estimated from regression over years 21 - 150 of their abrupt4xCO2 simulation data of 3 K or below.

In physical sciences, where an OLS regression model with normally distributed errors is validly used to estimate a slope parameter between two variables with observational data, errors in the regressor variable contributing a small part of the total uncertainty, it is usual to accept the uniform prior in the slope parameter (here Y) implied by the regression model.

According to the estimates made with a simple regression model, we can expect a seasonally ice ‐ free Arctic Ocean * as early as in the mid ‐ 2030s *.»

The second method uses a optimal filtering based statistical model, and the third estimate is based on regression models relating September sea ice extent to spring atmospheric and oceanic conditions.

We calculate the length of time each individual is exposed to different temperatures in utero and in early childhood, and we estimate flexible regression models that allow for nonlinearities in the relationship between temperature and long - run outcomes.

Matthews, E., 1997: Global litter production, pools, and turnover times: Estimates from measurement data and regression models.

The 95 % confidence intervals in Figs. 2 and 3 represent uncertainty in the statistical estimates of the regression model for observed paths of forcings, SOI, and volcanic sulfates.

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.»

[12] Otto et al. used regression - based estimates of ERF in multiple CMIP5 models.

Using logistic regression analysis, odds ratios, 95 % confidence intervals and significance p values were estimated for association between each outcome and each childhood measure individually and in models including all childhood measures, each adjusted for cohort and gender.