Sentences with phrase «statistical modelling techniques»

Profile Result oriented analytics professional with over 5 years of experience in designing & developing advanced analytics solutions using statistical modelling techniques in R and Python leveraging various open source technologies and Tibco Spotfire Analytics platform.

In addition, increasingly sophisticated statistical modeling techniques were applied to correlational studies to aid understanding of the results.

The Child Outcomes and Volunteer Effectiveness (COVE) evaluation is using sophisticated statistical modeling techniques to determine the extent to which Texas CASA's advocates are effectively serving the needs of children.

Not exact matches

It speaks of operations research, systems analysis, technological forecasting, information theory, game theory, simulation techniques, decision theory, Delphi method, cross-impact matrix analysis, statistical time - series, stochastic models, linear programming, input - output economics, computer based command and control systems, and so on.

With the aid of complex statistical techniques, he and his collaborators were able to identify the optimal evolutionary model, given the nature of the available data, and they employed a new method to correct for systematic errors.

In order to better understand how soil microbes respond to the changing atmosphere, the study's authors utilized statistical techniques that compare data to models and test for general patterns across studies.

Through statistical modeling and visualization techniques, the researchers organized the emotional responses to each video into a semantic atlas of human emotions.

In the new study, Professor Pisani and colleagues used cutting edge statistical techniques (Posterior Predictive Analyses) to test whether the evolutionary models routinely used in phylogenetics can adequately describe the genomic datasets used to study early animal evolution.

Then, we compared the results from these models with the existing genetic data, and used statistical techniques to identify the scenario that best explained the current genetic diversity of the elephant population in Borneo,» explains Lounès Chikhi.

By using a statistical technique known as Bayesian analysis — which combines probability with archaeological information to improve precision for groups of radiocarbon dates — the study authors were able to produce a high - precision chronology model for early domestic horse use in Mongolia.

Their model, presented recently in the journal Current Biology, gives researchers a renewed opportunity to trace words and languages back to their earliest common ancestor or ancestors - potentially thousands of years further into prehistory than previous techniques can do with any statistical rigor.

They will exploit a combination of state - of - the - art climate model experiments, advanced statistical techniques and idealised dynamical frameworks to accomplish the project.

«In order to do this, I collected data from friend dyads and used a statistical technique called the «actor partner interdependence model,» or APIM.

The researchers used a mathematical method based on statistical modeling, known as a polynomial transformation technique - based recursive least - squares algorithm, to first generate a mixed - rate model using fast sample rate inputs and slow - sample rate outputs that identifies the mechanical resonances beyond the Nyquist frequency, and then to derive a fast - rate model involving fast sample - rate inputs and fast sample - rate outputs from which the unwanted frequencies can be extracted.

His DOE Early Career project is looking at applying statistical and machine learning techniques to exascale performance modeling.

Statistical Techniques Explore the looks, models, and beauty from the Givenchy Spring / Summer 2018 Ready - To - Wear show in Paris on 1 October 2017, with show report by Anders

The most sophisticated approach uses a statistical technique known as a value - added model, which attempts to filter out sources of bias in the test - score growth so as to arrive at an estimate of how much each teacher contributed to student learning.

According to the report, «value - added models» refer to a variety of sophisticated statistical techniques that measure student growth and use one or more years of prior student test scores, as well as other background data, to adjust for pre-existing differences among students when calculating contributions to student test performance.

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.

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.

Using a statistical technique called value - added modeling, the Teacher Data Reports compare how students are predicted to perform on the state ELA and math tests, based on their prior year's performance, with their actual performance.

Both the SGP model and value - added models use a statistical technique called regression to analyze the test score histories of students.

The primary requirements of the work are (a) professional competence in applying the theoretical foundations of computer science, including computer system architecture and system software organization, the representation and transformation of information structures, and the theoretical models for such representations and transformations; (b) specialized knowledge of the design characteristics, limitations, and potential applications of systems having the ability to transform information, and of broad areas of applications of computing which have common structures, processes, and techniques; and (c) knowledge of relevant mathematical and statistical sciences.

We are focused on U.S recession forecasting, dating and probability estimation using quantitative econometric models and statistical techniques.

It's sole purpose is to serve as a comparison to your hypothesized model / hypothesis, because statistical techniques are inherently comparative rather than absolute.

The UK Met Office forecast uses a very different methodology than Gray, based upon climate models rather than a statistical technique.

Techniques from statistical mechanics have been wedded to biogeochemistry and population ecology, yielding new vegetation dynamic models.

Cohen received his Ph.D. in Atmospheric Sciences from Columbia University in 1994 and has since focused on conducting numerical experiments with global climate models and advanced statistical techniques to better understand climate variability and to improve climate prediction.

Statistical techniques generally use a mathematical method to form a relationship between the modelled climate and observed climate at an observation station.

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

In her talk, she examined how the choice of downscaling technique affects the uncertainty in projections of annual extremes in the Peace River Basin, using four reanalysis products and downscaling with six different statistical models.

What produced Lewandowsky's result is a statistical technique called structural - equation modelling (SEM).

In the last 10 years, downscaling techniques, both dynamical (i.e. Regional Climate Model) and statistical methods, have been developed to obtain fine resolution climate change scenarios.

Independently of climate models, the statistical forecast technique used by Sanchez - Sesma provides the basis for creating alternative scenarios of the 21st century climate.

Overpeck is part of a research team that is using statistical techniques to narrow down divergent model projections of how much average water flow in the Colorado River will decrease by 2050.

It's pretty funny that the GWPF is predicting no warming based on statistical analyses that can't agree whether warming stopped in 1998 or 2002, and pretty funny that the two modeling techniques used deliver quite different forecasts, making not one, not two, but four different forecasts — all of which we are apparently supposed to take more seriously than anything that is based on, you know, physics.

One technique to demonstrate credibility is by assessing how well the statistical model does on data that was not used in the calibration.

The researchers used statistical models and techniques from a field of mathematics called information theory to determine factors contributing to hurricane strength from 1970 to 2004 in six of the world's ocean basins, including the North Atlantic, Pacific and Indian oceans.

Using CE to judge the merits of a reconstruction is known as cross-validation and is a common statistical technique for selecting among competing models and subsets of data.

To form such an envelope, the intervals would have to be inflated further with a factor computed from a statistical model for the autocorrelation, typically using Monte Carlo techniques.

I have experience as a statistical modeler and analyst developing risk models using multivariate techniques, marketing segmentation using clustering, process analysis using decision tree machine learning techniques, and time series analysis for...

Wikipedia says «Predictive analytics encompasses a variety of statistical techniques from modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future, or otherwise unknown, events».

You need not to look further if your present search is for an individual who can effectively apply mathematical theories and techniques to practical problems, develop statistical models and create engaging computer simulations.

• Hands - on experience in developing and implementing analytic and mathematical models for testing supply chain sequences • Highly skilled in designing, developing and adapting statistical and econometric techniques to analyze supply chain management problems and roadblocks • Effectively able to determine and implement strategic plans to ensure prompt problem resolution • Skilled in performing researching activities to and economic analysis and initiating new studies • Proven ability to develop and implement risk mitigation plans to ensure smooth supply chain operations • Track record of defining and implementing metrics to enable effective sourcing and supplier performance management • Deep insight into key performance indicators (KPIs) that measure and improve sourcing and supply chain performance • Competent at utilizing influence management skills to negotiate movement of products in order to meet bulk deal demands • Proficient in reporting n field cycle count processes in sync with regulatory requirements of the company • Proven ability to manage established inventory levels in accordance to inventory levels dictated by set business models

using mathematical modelling techniques and statistical concepts to determine probability and assess risks, such as analysing pension scheme liabilities, to price commercial insurance

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Wellington City, New Zealand About Blog I write about applications of data and analytical techniques like statistical modelling and simulation to real - world situations.

Growth Mixture Modeling: A statistical technique used to model individuals» growth over time on a given variable or set of variables classify individuals according to those trajectories.

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).

Current areas of study are techniques for estimating effect sizes in small samples, statistical models and graphic techniques for analyzing data, and techniques appropriate for behavior genetics study.