Sentences with phrase «regression coefficient»

b = unstandardized regression coefficient (i.e., simple slope); SD = standard deviation.

B unstandardized regression coefficient, SE B standard error regression coefficient, Exp (Β) expected regression coefficient (odds ratio), YPI Youth Psychopathic Traits Inventory

To examine putative moderators of intervention effects, first the predictors of the family level were allowed to vary between groups by adding a variance component to the regression coefficient of the first level.

When comparing referred children and general population children (n = 230), there was only a significant difference between the groups in the relation between impulsivity and externalizing problems (regression coefficient of the interaction term β = 0.29, p <.001).

Solid lines indicate significant paths, and include the standardized regression coefficient.

(M) = mother report; (A) = adolescent report, β = standardized regression coefficient, DV = dependent variable.

Standardized Probit (for ADHD, ASD / AS Outcomes) and Linear (for PSE Outcome) Regression Coefficient Estimates and SEs at Age 5

In that model, each observation is regressed linearly on the previous observation, and the regression coefficient is a random effect (i.e., it varies between persons).

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.

Results Viewing violent programs (but not nonviolent programs) was negatively related to time children spent with friends (aged 6 - 8 years, unstandardized regression coefficient [β] = − 0.34, 95 % confidence interval [CI], − 0.59 to − 0.08; aged 9 - 12 years, β = − 0.41, 95 % CI, − 0.65 to − 0.18).

β = standardized regression coefficient when the outcome variable is regressed on years (1 — 5).

We were able to estimate a stable model, though, by constraining ORs for particular types / numbers of disorders to be the same in predicting both planned and unplanned attempts unless the interaction of the predictor with plans in the pooled model was significant at the α level of.05 and had an estimated variance inflation factor (a diagnostic test suggesting that a regression coefficient might be affected by multicollinearity) of less than 10.0.

practices and market turbulence achieved a significant level, with a native regression coefficient of interactions (b = − 0.121, p < 0.05).

Land use forcing has a regression coefficient negligibly different from zero, whereas all other forcings have regression coefficients close to one.

The regression coefficient is -0.1, indicating that the projected changes from the Hadley model should to scaled back 90 percent and changed in direction.

In the paper «Evolution of El Nino — Southern Oscillation and global atmospheric surface temperatures» (2000), Trenberth et al state on page 4, «The regression coefficient based on the detrended relationship is 0.094 deg C per N3.4 and is deemed more appropriate.

Fritts: The regression coefficient is multiplied by the weights of eigenvector 1 to obtain a response function that accounts for 36 % of the growth variance (Fig 7.13).

Furthermore, the results suggested a one - to - one correspondence in trends between simulations and observations, but the analysis also gave a regression coefficient of 2 - 4 for natural forcings.

The school characteristics do not achieve a significant regression coefficient, nor does the Principal / Assistant Principal variable.

While the characteristics of the school and its student population, taken together, have a strong association with principals «instructional leadership, the measure of District focus on instruction has a significant regression coefficient.

For a given n (the number of observations) 10,000 simulations were run and the Chi - square goodness of fit test and regression coefficient (Genotype (Postn − / −)-RRB- was calculated for each simulated data set.

Methods used for conducting statistical tests (e.g., t - test, Wilcoxon signed rank test, Wald test of regression coefficient) and for constructing confidence intervals (e.g., normal - based 95 % CI: mean ± 2SD, likelihood ratio - based interval) should be clearly stated.

Weighted meta - regression revealed a significant inverse linear relation between the duration of breastfeeding and the risk of overweight (regression coefficient = 0.94, 95 % confidence interval: 0.89, 0.98).

Variables were retained in the reduced logistic regression model when their presence was determined to confound the association between human milk feeding and infection or sepsis / meningitis, as defined by a change of > 5 % in the regression coefficient for type of feeding when the variable was removed from the full regression model.

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.

Moreover, the signs of some of the regression coefficients actually change direction suggesting that, for some outcomes, breastfed children may actually be worse off than children who were not breastfed.»

We tested the regression coefficients in the model using the likelihood ratio test and the Wald statistic setting significance at α = 0.05.

The regression coefficients or slopes that describe the linear relationship between average adipocyte cross-sectional area and percentage of F4 / 80 - expressing cells were comparable across mesenteric, perigonadal, and perirenal depots (Table 1).

Negative regression coefficients for neural response similarity indicate that greater neural response similarity was associated with decreased social distance.

Error bars indicate cluster - robust standard errors of the regression coefficients

Regression coefficients for the effects of neural response similarity on social distance for each cortical ROI are shown overlaid on a lateral, b medial, and c ventral views of the cortical surface.

Regression coefficients for each ROI are shown in Fig. 6, and further details for ROIs that met the significance threshold of p < 0.05, FDR - corrected (two - tailed) are provided in Table 2.

Linear regression coefficients for the log - transformed biomarkers in relation to a 100 mg / d increase in dietary magnesium intake are shown in Table 4.

Regression coefficients were based on per - unit increase per day: per 100 - g increment in whole grain and per 1 - serving increment in green leafy vegetables, nuts (without peanut butter), and legumes.

Multiple linear regressions were used to calculate regression coefficients for the relation between magnesium intake and biomarker concentrations.

Regression coefficients (β) of dietary total fiber and soluble and insoluble fiber for predicting log C - reactive protein (CRP) according to a linear mixed model in the Seasonal Variation of Blood Cholesterol Study, Worcester, MA (1994 — 1998)

The regression coefficients of dietary total fiber and soluble and insoluble fiber for predicting log CRP from a linear mixed model are shown in Table 3.

Similarly, there were 5 studies (4, 9, 29, 31, 33) in which the regression coefficients of saturated fat could be interpreted as the effects of isocalorically replacing polyunsaturated fat intake with saturated fat.

The HRs and 95 % CIs for the isoprotein substitution effect were derived from the difference between the regression coefficients, variance, and covariance.19

Both trust in leadership and instructional leadership exhibit significant regression coefficients, while building level and shared leadership are insignificant.

As their Table 6 shows (p. 20), the regression coefficients related to these three areas of «statistically significant» influence on teachers» students» test - based performance on the new PARCC and SBAC mathematics tests (i.e., more professional development days, more classroom observations with explicit feedback tied to the Common Core, and the inclusion of Common Core - aligned student outcomes in teacher evaluations) yielded the following coefficients, respectively: 0.045 (p < 0.01), 0.044 (p < 0.05), and 0.054 (p < 0.01).

We show four relevant empirical facts: i) the striking ability of the logarithmic averaged earning over price ratio to predict returns of the index, with an R squared which increases with the time horizon, ii) how this evidence increases switching from returns to gross returns, iii) moving over different time horizons, the regression coefficients are constant in a statistically robust way, and iv) the poorness of the prediction when the precursor is adjusted with long term interest rate.

In addition, and are the regression coefficients to be estimated, and is a constant noise term (more details in the R - script used to do this demonstration).

Furthermore, the regression coefficients on the data for the same and earlier decades suggest that the SFZ 2008 surface data is delayed by several years relative to CSF 2005 data.

There are important implications in this observation not least the possibility of biased regression coefficients in attempts to reconstruct past low - frequency temperature change based on long density series calibrated against recent temperatures.

This is invariably necessary b / c multivariate regression outputs (typically reported in tables of regression coefficients and associated precision measures such as t - statistics, standard errors, and stupefying «p - values») invariably defy meaningful interpretation by even stats - sophisticated readers.

Here's the sort of analysis that I'd like to do if we can get regression coefficients from RegEM TTLS.

Tingley notes that the LASSO is equivalent to putting a double - exponential prior on the regression coefficients and that this does not make sense from a scientific perspective.

the model may be «correct,» but you have gone overboard by adding predictors that are redundant leading to problems such as inflated standard errors for the regression coefficients» (i.e., overconfidence in the prediction algorithm).

The regression coefficients don't change through time once you have a few decades of data.