statistical regression


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Noun1.statistical regression - the relation between selected values of x and observed values of y (from which the most probable value of y can be predicted for any value of x)
statistics - a branch of applied mathematics concerned with the collection and interpretation of quantitative data and the use of probability theory to estimate population parameters
statistical method, statistical procedure - a method of analyzing or representing statistical data; a procedure for calculating a statistic
regression analysis - the use of regression to make quantitative predictions of one variable from the values of another
linear regression, rectilinear regression - the relation between variables when the regression equation is linear: e.g., y = ax + b
curvilinear regression - the relation between variables when the regression equation is nonlinear (quadratic or higher order)
References in periodicals archive ?
Of the 75 samples analyzed using the BSIN method, 2 had IgG4 measurements greater than the analytic measuring range of the LC-MS/MS method (20 g/L) and, therefore, were not included in subsequent statistical regression analyses.
It covers the computerized art evaluation of elements of drawings using the elements of the Computer Color-Related Elements Art Therapy Evaluation System (C_CREATES) (provided on the accompanying CD-ROM), and the computerized art interpretation of the drawerAEs psychological state using expert systems, statistical regression models, and Bayesian networks, including application to differentiation and identification of psychological states and estimating levels of psychological disorder.
Key words: Construction site, Fine inert waste material, Suspended particulate matter, Physico-chemical characterization, Estimation, Statistical regression.
Therefore, the article suggests a more precise statistical regression model for wind farm power prediction.
Many ideas have been proposed to monitor dam deformation with statistical regression analysis or mechanical calculation.
Statistical regression models on the other hand particularly aim at describing and explaining the underlying relationship in a structured way.
The investigators conducted statistical regression analyses based on ATP data.
Here are a few surprising take-home points from what is after all just one statistical regression. Nevertheless, the sample is large enough to give any runner pause about plunking down hundreds of dollars on a running shoe.
To this end, we seek to revolutionize scientific and practical modi operandi of data acquisition and learning by developing a new optimization and analysis framework based on the nascent low-dimensional models with broad applications from inverse problems to analog-to-information conversion, and from automated representation learning to statistical regression. We attack the curse of dimensionality in specific ways, not only by relying on the blessing of dimensionality via concentration-of-measures, but also by exploiting geometric topologies and the diminishing returns (i.e., submodularity) within learning objectives.
There exists vast literature about the regression problem in mathematics, statistics and computer science and there are many statistical regression methods for this purpose.
However, all advanced computer-driven methods and statistical regression are firmly within the inferential branch of statistics.

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