Sentences with phrase «linear models testing»

Hierarchical linear models testing the association of nation attachment with heritage culture identification after including the control variables was partially replicated in this study; the lack of an association between dismissive nation attachment and heritage identification is attributed to a lower proportion of migrant participants.

General linear models testing for effects of sex and age (6 - month bands) indicated no age effect for BITSEA / P (Table I).

Not exact matches

Decline in cognitive test scores over 10 years (% change = change / range of text × 100) as function of baseline age cohort in men and women, estimated from linear mixed models.

To test for differences in growth rates between genotypes, we fit the data using linear models regressing larval weight against age and tested for differences in the interaction term between larval age and genotype using ANCOVA and post hoc comparisons of the slopes of fitted lines using lstrends (HH and lsmeans packages).

Using linear regression, we demonstrate that the multivariate pattern of gray matter density within these brain regions significantly predicts individual intelligence scores in the remaining, i.e., independent sample used for model testing (N = 108; correlation between predicted and actual intelligence scores: r =.36).

Almost all statistical tests for comparing control vs condition style experiments (differential expression) use generalized linear models assuming count data with these kinds of distributions.

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.

Tests of linear trend across categories of coffee consumption were performed by assigning participants the midpoint of their coffee - consumption category and entering this new variable into a separate Cox proportional - hazards regression model.

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).

Differences in these subgroups» associations were tested using the Wald statistic of the cross-product term between continuous sex hormones and stratification variables in the fully adjusted linear mixed - effects model.

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.

Growth will be determined using a simple linear regression model in which current test scores are regressed on last year's test scores.

In order to describe the relationship between the Rising Stars PIRA tests and the national test scores, a statistical technique known as linear regression was used to model the relationship between the two variables.

Additionally, the brand new «13 - SKYACTIV» model grade delivers both a linear and pleasing ride and outstanding fuel economy of 30.0 km / L (10 - 15 mode test cycle) or 25.0 km / L (Japan's new JC08 test cycle).

Also, to pass the snicker test, skeptics (particularly Dietze) need to give up the pretense that linear impulse response is the be-all-end-all and stop making silly assertions about Bern and other models that confuse model structure with rough characterizations of behavior for purposes of discourse.

Canadian Ice Service, 4.7, Multiple Methods As with CIS contributions in June 2009, 2010, and 2011, the 2012 forecast was derived using a combination of three methods: 1) a qualitative heuristic method based on observed end - of - winter arctic ice thicknesses and extents, as well as an examination of Surface Air Temperature (SAT), Sea Level Pressure (SLP) and vector wind anomaly patterns and trends; 2) an experimental Optimal Filtering Based (OFB) Model, which uses an optimal linear data filter to extrapolate NSIDC's September Arctic Ice Extent time series into the future; and 3) an experimental Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictors.

Canadian Ice Service, 4.7 (+ / - 0.2), Heuristic / Statistical (same as June) The 2015 forecast was derived by considering a combination of methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic ice thickness extents, as well as winter Surface Air Temperature, Sea Level Pressure and vector wind anomaly patterns and trends; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictors.

And even if the data [i] is [/ i] described well by a linear model with breakpoint, testing only one year doesn't tell you where the breakpoint really is.

We could also test the long - term linear model, and the linear - plus - cyclic model, by fitting them only to part of the data — say, data up to 1975 — then extrapolating that model to see how well is validates what followed.

I work with t tests, Chi square, Z scores, linear regression, multiple regression, multi level modeling, ANOVA, MANOVA, and other statistical techniques.

Canadian Ice Service; 5.0; Statistical As with Canadian Ice Service (CIS) contributions in June 2009 and June 2010, the 2011 forecast was derived using a combination of three methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic Multi-Year Ice (MYI) extents, as well as an examination of Surface Air Temperature (SAT), Sea Level Pressure (SLP) and vector wind anomaly patterns and trends; 2) an experimental Optimal Filtering Based (OFB) Model which uses an optimal linear data filter to extrapolate NSIDC's September Arctic Ice Extent time series into the future; and 3) an experimental Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere, and sea ice predictors.

Canadian Ice Service, 4.7 (± 0.2), Heuristic / Statistical (same as June) The 2015 forecast was derived by considering a combination of methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic ice thickness extents, as well as winter Surface Air Temperature, Sea Level Pressure and vector wind anomaly patterns and trends; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictors.

A linear mixed effect model using these three independently significant variables to explain the decline in δ13C was favored in model selection over any single variable model with a Likelihood Ratio Test (130.4 vs 139.8 — 147.6, Table 2) and thus strongly suggests that shells are responsive to carbon chemistry.

As with previous CIS contributions, the 2016 forecast was derived by considering a combination of methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic ice thickness / extent, as well as winter surface air temperature, spring ice conditions and the summer temperature forecast; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent time - series into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictors.

It looked like it worked until I tested it on a truncated data set against the lm (linear models) regression procedure and found a slight glitch.

The UBC is tested for computational problems initially in the linear PE model, and subsequently in a forced, damped nonlinear quasi-geostrophic model.

Test statistics for the linear mixed effects (LME) model were calculated using S - PLUS version 7.0 statistical software (Insightful Corp, Seattle, Washington).

We added quadratic and cubic terms for Time to the model to determine the functional form of the growth trajectory and tested the difference in model fit between the linear, quadratic, and cubic models.

Trends in rates of child diagnoses by mother's response level in children with a baseline diagnosis and in rates of incidence or relapse in children without a baseline diagnoses were examined separately using the Cochran - Armitage test for trend.29 Low event rates precluded fitting regression models adjusting for potential confounders, such as age and sex of child, using generalized linear models with an identity - link function, to estimate parameters for adjusted trends.

We test our hypothesis about the effects of human and social capital on student achievement using social network analysis and hierarchical linear modeling.

In addition, for 2 variables, the core log - linear model produced unstable variance estimates for the tests of treatment main effects.

Multivariate hypothesis testing was conducted in the hierarchical linear modeling (HLM) context to examine differences in patient and spouse initial status and rates of change over time on self - reported depressive symptomatology.

She has technical expertise in a wide range of statistical techniques used in the social sciences, including structural equation modeling, confirmatory factor analysis and MIMIC approaches to measurement, path modeling, regression analysis (e.g., linear, logistic, Poisson), latent class analysis, hierarchical linear models (including growth curve modeling), latent transition analysis, mixture modeling, item response theory, as well as more commonly used techniques drawing from classical test theory (e.g., reliability analysis through Cronbach's alpha, exploratory factor analysis, uni - and multivariate regression, correlation, ANOVA, etc).

In order to assess the replicability of results from Study 1, the indirect effect of secure - preoccupied nation attachment on SWB through heritage identification was tested with a Sobel test; all values entered to the test were derived from hierarchical linear models.

The unstandardized coefficients and standard errors entered into the Sobel test were derived from hierarchical linear models.

The effects of the experimental manipulation on ST - IAT and SD was tested with linear mixed models (LMMs).

Given poor robustness of t - tests with very different group sizes, we used t ′ assuming lack of homogeneity of variance; control analysis was tested with general linear model (GLM) controlling for age, depressive symptoms, and self - rated health (df = 1).

A multilevel mixed - effects linear regression model with an unstructured covariance matrix was used to test whether different patterns of financial difficulty were associated with subsequent changes in ADHD symptoms.

Likelihood ratio testing of variance components in the linear mixed ‐ effects model using restricted maximum likelihood

lmerTest: tests for random and fixed effects for linear mixed effect models (lmer objects of lme4 package).

We tested a mediation model in which detachment was predicted by the linear and squared effect of workload and marital satisfaction was predicted by the linear effect of detachment while controlling for the direct effect of the linear and squared effect of workload on marital satisfaction.

After combining the two samples, we then extended the ESEM model to test measurement invariance across several group configurations (gender, age, and gender × age), evaluated the potential linear and quadratic effects of age through MIMIC models, and then combined the two methods by adding the MIMIC age effects to the gender × age invariance model.

We use linear structural equation causal modeling to test this hypothesis for the case of global self - esteem (Rosenberg 1979) and specific (academic) self - esteem.

Simple effects of callous - unemotional traits were tested in a general linear model (GLM) with age, IQ, ADHD symptoms, and PDS and PESQ scores added as covariates of no interest.