R] = the estimated residual sum of squares (RSS) of the full (F) and reduced (R) models, respectively (Kimura, 1980; Quinn and Deriso, 1999).
What does RSS stand for?
RSS stands for Residual Sum of Squares (statistics)
This definition appears very frequently and is found in the following Acronym Finder categories:
- Science, medicine, engineering, etc.
See other definitions of RSS
We have 524 other meanings of RSS in our Acronym Attic
- Requirement Spread Sheet
- Requisitions Self Service
- Rescue Swimmer School (Navy and Marine Corps)
- Research Support Services (Vanderbilt Medical Center)
- Réseau Santé Social
- Resident Satisfaction Survey (various locations)
- Resident Set Size (*nix command)
- Resident Site Supervisor
- Residential Subscriber System
- Residential Support Services Inc.
- Resource Description Framework (RDF) Site Summary (lightweight XML format)
- Resource Sharing System (libraries)
- Resources Status Review
- Résumé de Sortie Standardisé
- Retail Store Solutions (IBM)
- Retek Store Systems
- Reusable Space Systems (Boeing)
- Rich Site Summary
- Rich Site Syndication (aka Realtime Site Syndication)
- Rig Site Survey (energy exploration)
Samples in periodicals archive:
Model selection : Model comparison and selection was based on statistics of predicted residual sum of squares (PRESS) because prediction is the most important focus here.
That is, the "V" trend is expected to be the most frequent since that would imply that residual sum of squares is a minimum when correlation of the error term is smallest (negative or positive).
a] will only increase if the residual sum of squares decreases.
The fit of the models was evaluated by comparing the sum of squared residuals (SSR) to the predicted residual sum of squares (PRESS).
Then if a further grouping of (n-p) data points are added, the updated regression parameter estimates are given by (5) [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] with residual sum of squares (RSS) given by RSS = [w.
The test statistic is F= [[(A--B)/(T--1) (K + 1)]/[B/(N--T(K + 1)]], where A = residual sum of squares from the combined sample, B = the residual sum of squares for each firm's sample summed over firms, T = number of subsamples, K = number of independent variables, and N = sample size.
Growth model adjustment to the data was made by using the nonlinear iterative Quasi-Newton method, minimizing the residual sum of squares.