statistical inference

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Related to statistical inference: Statistical hypothesis

statistical inference

[stə′tis·tə·kəl ′in·frəns]
(statistics)
The process of reaching conclusions concerning a population upon the basis of random samplings.

statistical inference

see PROBABILITY, STATISTICS.
References in periodicals archive ?
Dworsky is an electrical engineer who has worked in corporate, government, and academic positions presents a book mostly about probability, with a chapter introducing statistical inference. His goal is to split the difference between textbooks that load on the technical matters on the assumption that an instructor will be on hand, and popular accounts that avoid mathematics and any other dimension that might frighten general readers.
The project follows a bottom-up approach from experimental assessments of the uptake of single cells attached to rotifers to the statistical inference on the effects of rotifer and bacterial communities.
An Introduction to Probability and Statistical Inference, 2nd Edition
Models for probability and statistical inference; theory and applications.
Objective: ResMet is dedicated to applications of resampling methods in statistical inference for the processes with periodic and almost periodic structures.
Statistical Inference: An Integrated Approach, 2nd Edition
Subsequent sections cover probability, statistical inference, regression and time series, and methods and applications.
Though probability is fascinating in its own right, says Meyer, as she introduces it, she keeps always in mind the statistician's point of view, which sees probability as a tool for building models to do statistical inference. Her topics include discrete random variables and expected values, moments and the moment-generating function, jointly continuously distributed random variables, hypothesis tests for a normal population parameter, quantifying uncertainty: standard error and confidence intervals, and information and maximum likelihood estimation.
Tender issuemachine learning as a field of computer science applies statistical inference techniques to the task of complex data mining prediction and decision making.
The training covered concepts ranging from Introduction to SPSS and STATA; Principles of Statistical Inference; Parametric and Non-Parametric Tests; Simple and Multiple Linear Regression, ANOVA, ANCOVA, MANOVA, MANCOVA, Survival Analysis and Factor Analysis with hands-on training and practical case studies.
e., Balluerka, Vergara, & Arnau, 2009; Gallistel, 2009; Wasserstein & Lazar, 2016; Wilkinson & Task Force on Statistical Inference, 1999).
Investment decisions are driven by statistical inference based on the empirical analysis of data, rather than instinct or intuition.

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