Linear Regression Analysis

Seber, G. A. F.; Lee, Alan J.

doi:10.1002/9780471722199

Cited by 1,831 publications

(1,481 citation statements)

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“…The model includes an estimate of temporal autocorrelation. The effective degrees of freedom were estimated as described in Worsley and Friston (1995) and in Seber (1977). In the following, contrast maps, i.e., estimates of the raw scores, were generated for each subject.…”

Section: Resultsmentioning

confidence: 99%

Predicting events of varying probability: uncertainty investigated by fMRI

Volz¹,

Schubotz²,

Cramon³

2003

NeuroImage

193

161

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Many everyday life predictions rely on the experience and memory of event frequencies, i.e., natural samplings. We used functional magnetic resonance imaging (fMRI) to investigate the neural substrates of prediction under varying uncertainty based on a natural sampling approach. The study focused particularly on a comparison with other types of externally attributed uncertainty, such as guessing, and on the frontomedian cortex, which is known to be engaged in many types of decisions under uncertainty. On the basis of preceding stimulus cues, participants predicted events that occurred with probabilities ranging from p ϭ 0.6 to p ϭ 1.0. In contrast to certain predictions in a control task, predictions under uncertainty elicited activations within a posterior frontomedian area (mesial BA 8) and within a set of subcortical areas which are known to subserve dopaminergic modulations. The parametric analysis revealed that activation within the mesial BA 8 significantly increased with increasing uncertainty. A comparison with other types of uncertainty indicates that frontomedian correlates of frequency-based prediction appear to be comparable with those induced in long-term stimulus-response adaptation processes such as hypothesis testing, in contrast to those engaged in short-term error processing such as guessing.

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Section: Resultsmentioning

confidence: 99%

Predicting events of varying probability: uncertainty investigated by fMRI

Volz¹,

Schubotz²,

Cramon³

2003

NeuroImage

193

161

View full text Add to dashboard Cite

show abstract

“…Hence, U G will generally consist of 50% drug-dose combinations from each X G and V G . In the last step, which is the selection step, the new combinations (U G where β 0 , β i , β ii and β ij represent the coefficients of the intercept, linear, quadratic and bilinear (or interaction) terms, respectively, y represents the response variable (the output), x i and x j are independent variables (the inputs, which represent the drugs) and ε is an error term with a mean close to zero 26 .…”

Section: Experimental Design Input Definitionmentioning

confidence: 99%

Optimization of drug combinations using Feedback System Control

Nowak-Śliwińska¹,

Weiss²,

et al. 2016

Nat Protoc

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“…The logarithmic transformation of the function CV(x)= A/X B that resulted in the model for both replications of k, varying from 1 to 5 clones, was used to test parallelism, same origin and coincidence among the five clones (Seber, 1976). The approximate statistic of the hypothesis test for H 01 and H 02 , and for H 03 , where was the combined sum square error; was the sum of the sum square error of each clone; K was the number of compared clones (K= 5); and n was the number of total observations (plot sizes and replications).…”

Section: Palavras-chavementioning

confidence: 99%

Plot size variation to quantify yield of potato clones

et al. 2006

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The objective of this paper was to study plot size variation among potato clones to increase experimental precision of yield performance trials. The experiment was carried out at the experimental area of the Horticulture Dept., UFSM, Santa Maria, Rio Grande do Sul State, Brazil. Tubers of five potato clones were planted in two rows of 60 hills in August 2004. For all ten experimental rows, combined plots were formed adding a different number of adjacent hills of the same row. Soil heterogeneity index and optimum plot size were estimated for each row. Real differences between treatment means for each clone and all clones were estimated using the Hatheway technique. The experimental precision of potato yield trials varied with the evaluated clone because of different optimum plot sizes. Eight-hill plots are the optimum size to quantify yield of potato clones, but experimental precision depends upon adjustments of the number of treatments and replications to the availability of experimental area. Genetic diversity indeed decreases experimental precision and justifies the use of different plot sizes. Practical applicattions and strategies to increase experimental precision of yields performance trials of potato clones are discussed.

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Linear Regression Analysis

Cited by 1,831 publications

References 0 publications

Predicting events of varying probability: uncertainty investigated by fMRI

Predicting events of varying probability: uncertainty investigated by fMRI

Optimization of drug combinations using Feedback System Control

Plot size variation to quantify yield of potato clones

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