Sentences with phrase «regression analysis models»

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.

«Machine learning offers new way of designing chiral crystals: Logistic regression analysis model predicts ideal chiral crystal.»

Not exact matches

Customize your risk analysis using tailor - made factor models, risk budgeting, multi-factor regression and user - defined stress tests to create a comprehensive, easy - to - interpret report that breaks down your portfolio's risk and return components.

To assess the robustness of the results of our regression analysis, we performed covariate adjustment with derived propensity scores to calculate the absolute risk difference (details are provided in the Supplementary Appendix, available with the full text of this article at NEJM.org).14, 15 To calculate the adjusted absolute risk difference, we used predictive margins and G - computation (i.e., regression - model — based outcome prediction in both exposure settings: planned in - hospital and planned out - of - hospital birth).16, 17 Finally, we conducted post hoc analyses to assess associations between planned out - of - hospital birth and outcomes (cesarean delivery and a composite of perinatal morbidity and mortality), which were stratified according to parity, maternal age, maternal education, and risk level.

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.

The analysis was carried out using a logistic binary regression model, with PPH as the outcome variable and built using manual forward selection (with p < 0.05 as the cut - off).

It is an observational study involving secondary analysis of maternity records, using binary logistic regression modelling.

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.

We modeled the association between early breastfeeding experiences and postpartum depression as a complete case analysis using logistic regression in SAS 9.1.

In the analysis, we considered multiple covariates that may confound the association between early breastfeeding experience and postpartum depression based on the published literature and included these covariates in our multivariable logistic regression models.

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 research applied Multivariate Regression Analysis combined with a Logit Model to the real data to identify statistically significant factors that have influenced voting preference simultaneously as well as the odds ratio in favour of Leave.

Burton and Dickey then developed logistic regression and random forest models using the ArmChair Analysis play - by - play data seasons to predict future play types.

As described in the main text, ordered logistic regression analyses were carried out for each brain region in which social network distances were modeled as a function of local neural response similarities and dyadic dissimilarities in control variables (gender, ethnicity, nationality, age, and handedness).

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

They used a statistical analysis known as mixed model regression to analyze the results.

Next, to determine whether risk of non-affective psychotic disorder in refugees relative to migrants differed by region of origin, we fitted a Cox regression model to a subset of the cohort, excluding the Swedish - born group who did not contribute information to these analyses.

By longitudinal mixed - model regression analysis, bone mineral density increased 0.26 % per 1 mg of isoflavone intake per year.

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.

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).

The analyses were first conducted in each cohort separately, and because no appreciable difference was detected by cohort (eTable 1 in the Supplement), we then conducted the pooled analysis using the sex - stratified Cox proportional hazards regression model in the combined data set.

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.

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.

Analyses reported below include a series of correlations and regressions followed by a path model.

Specific statistical areas of expertise include factor and cluster analysis, basic bivariate analyses, repeated measures analyses, linear and hierarchical / mixed models, structural equation modeling, and nonparametric analyses including logistic regression techniques.

Inter-correlations among the intersections between teacher and student outcome variables were also subjected to factor analysis achieved through step-wise regression modeling techniques to determine the most potent predictors of student arts and academic learning outcomes.

I also taught myself a fair bit of statistics along the way including logistic regressions and discriminant analysis in order to backtest different models for identifying outperformers, dividend growth / cuts etc..

The second half of Henning's hybrid model involves using fundamental variables that have performed well as indicators of future price growth, based on Henning's own multiple regression analysis.

You can also run market model regression for beta analysis based on selected assets or imported benchmarks.

This online Fama - French factor regression analysis tool supports regression analysis for individual assets or a portfolio of assets using the capital asset pricing model (CAPM), Fama - French three - factor model, the Carhart four - factor model, or the new Fama - French five - factor model.

Factor Regression» Market Model Regression» Principal Component Analysis» Match Factor Exposures» Fund Factor Regressions» Fund Performance Attribution» Factor Statistics»

Regression analysis is a fine tool for many things, but it's of limited value to determine a relevant value of ESS, compared to GCM models.

Test of the Hasselmann model through a regression analysis, where the coloured curves are the best - fit modelled values for Q based on the Hasselmann model and global mean temperatures (PDF).

46 — of course one must always be careful, whether modeling or doing regression analysis.

MKL present a flawed SST — intensity regression analysis comparing correlations of real - world intensities versus SST with idealized model correlations where no synoptic weather variability is present.

sea ice, arctic, antarctic, climate change, global warming, general linear model, dummy variable, regression, deseasonalized trend, trend analysis

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.

However, when a validation was performed on a similar analysis for which the regression model was calibrated with a subset of the data, and the remaining data were used for validation, it became apparent that models based on the factors that McKitrick & Michaels used had no skill (i.e. were not able to reproduce the independent data).

All statistical analyses and graphics were completed using the statistical software SigmaPlot 11 (Systat software Inc, San Jose, CA), except for regression model fitting which was performed using Matlab 2013 (Mathworks, Natick, MA).

Further quantitative analyses of species environment relationships suggested the use of linear regression models.

The obvious thing to do is to use regression analysis to calibrate the climate models forecasts.

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).

«A strong warming and severe drought predicted on the basis of the ensemble mean of the CMIP climate models simulations is supported by our regression analysis only in a very unlikely case of the continually increasing AMO at a rate similar to its 1970 — 2010 increase» 7

The solid black line is the global annual mean and the solid red line is the five - year lowess smooth, i.e. a non-parametric regression analysis that relies on a k - nearest - neighbor model.

Courses included Statistical modeling, Spreadsheet modeling, Reliability theory, Probability models, Decision Analysis, Regression Analysis

Determined appropriate multiple linear regression model areas from analysis of surface hydrography, topography, and the location of monitoring wells

Performed advanced statistical analysis (univariate and multivariate analysis of variance, cluster and path analysis, principle component and factor analysis, analysis of covariance, survival & longitudinal analysis, logistic and linear regression modeling), created customized reports and presentation quality data summary tables and figures.

Executed inferential statistical analysis through constructing regression models such as multivariate linear

Tags for this Online Resume: SPSS, SAS, MPlus, Design, Analysis, Evaluation, Research, Psychology, Quantitative, Qualitative, Statistics, Measurement, Reports, Publication, PhD, Implementation, Modeling, Regression, Decision Making, Intelligence, Assessment, Monitoring, Operations Research, Sampling, Policy, Survey, Analytics, Reliability, Psychometrics, Government, Technical, Mixed - Methods