Sentences with phrase «statistical model predicts»

Overbooking flights is a legal and common airline practice; travelers cancel plans at the last minute, they show up late... it's a numbers game: The airline builds in a buffer of oversold seats to offset the number of people their statistical models predict will back out.

Publication (Open access): Aalto, J., Harrison, S., Luoto, M. Statistical modelling predicts almost complete loss of major periglacial processes in Northern Europe by 2100.

Campaigns already used algorithms to infer political and demographic attributes about voters they couldn't contact directly; why couldn't those same statistical models predict innate psychological characteristics, as well?

Not exact matches

Machine translation creates statistical models that try to predict patterns of words and phrases.

Our estimate of the likely success of Remain and Leave in each local authority are the values predicted for that authority by this statistical model.

Two years ago I developed a simple statistical model that tries to predict the outcome of general elections from local election results.

However, the statistical models are no longer predicting that Labour will outperform their polling numbers.

Medicines that are personally tailored to your DNA are becoming a reality, thanks to the work of U.S. and Chinese scientists who developed statistical models to predict which drug is best for a specific individual with a specific disease.

The investigators combined daily averages into two - year averages and used a statistical model to predict concentrations in areas across the United States without a monitor.

Then they mapped out the millions of citations to those papers, and searched for a statistical model that best predicted scientists» future success based on their early publication history.

Through iterative testing and tweaking of their computational tools, the investigators built a statistical model that predicts the postmortem interval of unknown samples to within 55 accumulated degree - days, or about two days in summertime.

Background Often, the frequency of outcomes for an event can be modeled and predicted using statistical analysis.

Using geospatial analysis and statistical modeling, she and Pimm created a new map that predicted the locations within the mountainous region where each endemic species could best survive.

Statistical models analyzing plasma had reasonable ability to predict total disease duration and used seven relevant biomarkers.

Specifically, we ran a statistical model that used the last grade served by the school that a student attended in grade 3 to predict whether the student attended a middle school.

Incorporating rich information on students» high school performance, placement test scores, and demographics, we developed statistical models to predict how remediated students would have performed had they been placed directly into college - level courses.

Her statistical model ultimately predicts that the education quality will be higher in districts with stronger union density than in districts with weak unionism.

The project Achievement Trajectory Tool applies a data - driven statistical model to predict the longitudinal achievement growth in reading comprehension and science across grades 3 - 8 of students receiving Science IDEAS in grades 3 - 5 in comparision with students not receiving Science IDEAS (i.e., receiving traditional reading / language arts instead of Science IDEAS).

This model uses statistical calculations to predict the likelihood that the student's current growth trajectory will result in proficiency by a target date.

A teacher's observation scores are supplemented by a so - called «value - added» rating, which is calculated by determining whether a teacher's students made greater gains on standardized tests than statistical models would have predicted.

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.

Testing experts use statistical models to predict test - score increases.

If the statistical model is based on good background information, such as prior test scores that strongly predict future test scores, this may work very well.

Quantitative Analysis is referred to as the economic, business or financial analysis that aims to predict or understand behaviour or events through the use of mathematical measurements and calculations, statistical modelling and research.

An insurer uses actuarial knowledge (claims history, statistical models) to predict claims, and then prices the policy to ensure premiums exceed risk plus costs and profit.

The Fair Credit Reporting Act (FCRA) defines a «credit score» as «a numerical value or a categorization derived from a statistical tool or modeling system used by a person who makes or arranges a loan to predict the likelihood of certain credit behaviors, including default...»

When it is assumed that the CO2 content of the atmosphere is doubled and statistical thermal equilibrium is achieved, the more realistic of the modeling efforts predict a global surface warming of between 2 °C and 3.5 °C, with greater increases at high latitudes.

The beauty of computer modelling is when real features are predicted, and the combination of empirical - statistical models with physics - based models enhance our confidence of actual predictive skills.

My simple regression - based statistical climate model predicts global carbon dioxide, surface temperature & sea level at yearly time steps.

Yuan et al. (LDEO Columbia University), 5.08 (+ / - 0.51), Statistical The prediction is made by statistical models, which are capable to predict Arctic sea ice concentrations at grid points 3 - month in advance with reasonaStatistical The prediction is made by statistical models, which are capable to predict Arctic sea ice concentrations at grid points 3 - month in advance with reasonastatistical models, which are capable to predict Arctic sea ice concentrations at grid points 3 - month in advance with reasonable skills.

Since its inception 8 years ago, the NCAR / CU sea ice pool has easily rivaled much more sophisticated efforts based on statistical methods and physical models to predict the September monthly mean Arctic sea ice extent (e.g. see appendix of Stroeve et al. 2014 in GRL doi: 10.1002 / 2014GL059388; Witness the Arctic article by Hamilton et al. 2014 http://www.arcus.org/witness-the-arctic/2014/2/article/21066).

Lobell, D. B. and M. B. Burke, 2010: On the use of statistical models to predict crop yield responses to climate change.

Klazes (Public), 3.6 (95 % confidence interval of + / - 0.9), Statistical September extent is predicted using an estimated minimum value of the PIOMAS arctic sea ice volume and a simple model for volume - extent relationship.

If Hansen had employed this strategy, his model would have predicted the band of temperatures in which the observed temperature would lie in each statistical event and not the temperature.

This is in agreement with the statistical model of Tivy (see June report) that predicts below - normal ice concentrations, comparable to 2009.

This is consistent with both the June and July (Figure 3) ensemble predictions from a coupled ice - ocean model submitted by Zhang, which show considerably more ice in the East Siberian Sea compared to 2009, and it is consistent with the June statistical forecasts submitted by Tivy, which also predict a greater ice area than in 2009 and above - normal ice concentrations along the coasts.

Why should you trust any statistical model (by «any» I mean «any») unless it can skillfully predict new data?

Hamilton, 4.0 + / - 0.3, Statistical A simple regression model for NSIDC mean September extent as a function of mean daily sea ice area from August 1 to 5, 2012 (and a quadratic function of time) predicts a mean September 2012 extent of 4.02 million km2, with a confidence interval of plus or minus.32.

â $ œWhy should you trust any statistical model (by â $ œanyâ $ I mean â $ œanyâ $) unless it can skillfully predict new data?â $

Constructing a statistical model of the ozone variability, we have been able to predict the tendency in the land air T evolution till the end of the current decade.

If there is a match between the predicted and observed relative frequencies with respect to each possible outcome, the model is said to be «validated» by the statistical evidence.

If the relation between extent and volume is known, the extent may be predicted with better accuracy than a statistical model based on extent alone.

The statistical testing of a predictive model features a comparison of predicted to the observed relative frequencies of the various possible outcomes in the observed events that belong to a statistical sample which was drawn from the underlying population.

Going forward, if we stick with climatology and its 30 year averaging period then in order to provide policy makers with information about the outcomes from their policy decisions we need to come up with independent variable and dependent variable time series that are of much greater duration than the HADCRUT3, for 150 observed events is about the bare minimum for a statistically validated model that predicts with statistical significance.

Subsequently, however, based on statistical models that employ semi-empirical relationships between past and predicted future increases in global temperature, Vermeer and Rahmsdorf (2009), Jevrejeva et al. (2010) and Grinsted et al. (2010) derived much greater increases on the order of 60 to 190 cm over the same time interval.

For the first time, scientists have compared the latest predictions for global warming with a range of statistical models, commonly used to predict the spread of malaria.

To this aim they study all available historical information in a statistical manner, rather than attempt to build deterministic models predicting the far future.

It is by comparison of the predicted to the observed outcomes of statistical events that a model is statistically validated.

Centre for Polar Observation and Modelling (CPOM) / Schroeder et al, 5.40 (4.90 - 5.90), Statistical We predict the September ice extent 2014 to be similar to last year.

Reynolds (Public), 4.06 (3.49 - 4.63), Statistical / Heuristic Because the decline in extent is due to increasing ease with which open water can be revealed by declining volume, a simple method is used to predict September sea ice extent based on May sea ice volume for the Arctic Ocean from the PIOMAS model.