Sentence examples for regression project from inspiring English sources

The historical data for a regression project is typically divided into two data sets: one for building the model, the other for testing the model.

Project name: Periodic regression models Project home page: Operating systems: Web based application Programming language: R, PHP, Javascript Other requirements: Javascript supported and activated on the web browser (tested with Mozilla 5.0 and Internet Explorer 7.0).

In particular, we use linear regression to project the features associated to different poses at different stages of the lipreading system: the images themselves, a low-dimensional and compact representation of the images in the frequency domain or the final LDA features used for classification.

The eukaryotic regression models project that the smallest dinoflagellate genome (3×106 kbp) contains 38,188 protein-coding (40,086 total) genes and the largest (245×106 kbp) 87,688 protein-coding (92,013 total) genes, corresponding to 1.8% and 0.05% gene-coding percentages.

Dengue; disease prediction; logistic regression The project objective was to develop and test a decision support module using the multiple data sources available in the U.S. Department of Defense DoD) version of the Electronic Surveillance System for Early Notification of Community-Based Epidemics (ESSENCE).

For instance, if 150,000 jobs are created in an average month from now through October, the regression equation projects a very close result, showing a victory for Mr. Obama in the popular vote by about 1 percentage point.

PLS obtains a linear regression by projecting the input and output variables to a new space and addressing collinearity by reducing the number of variables, removing those which are least important for prediction.

We estimated the realized Hutchinsonian niche of quaking aspen to potential evapotranspiration (PET) by nonparametric, likelihood-based regression techniques, projected (continuous) values of aspen's niche expectation and uncertainty geographically, and correlated differences between observed and expected aspen cover to remotely sensed conifer cover.

We computed the annual per cent of change and assessed possible changes in trends using joinpoint regression, and projected the consumption up to 2020 using Bayesian methods.

Linear multiple regression analysis on project characteristics shows that project size, number of bidders, location and type of schools affect the bias.

The model we published in [2, 3] is based on Amdahl's law, which is implemented in a regression form to project performance for different workloads.