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Values represent slopes from the regression of response to selection on cumulative selection differential, shown for each target eyespot in the color composition and size experiments.
In line with a mental rotation explanation, a linear regression of response time on angle of rotation can be used to differentiate the two.
Analysis included comparisons of response rates; estimation of means and proportions of respondents by age, gender, doctor type, and geographic location compared to the doctor population; logistic regression of response bias; and comparisons of the proportion of missing values (item non-response).
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For the fitted experimental results of RSM, regression method of response surface was used.
Furthermore, the data were evaluated against storage time by regression analysis of response vs. time of storage.
The C118T in the ERCC1 gene maintained its predictive value in a logistic regression model of response (P=0.008).
Linear regression analysis of response revealed a significant association of the percentage reduction of clinical dimensions with percentage reduction of PET dimension and SUV.
The difference between global vs. decomposed processing was analysed by comparing the individual regression slopes of response time over string length for all three visual matching paradigms.
Logistic regression models of response in the MAQC2 neoadjuvant breast cancer dataset (n = 226) using the PAM50 subtype calls obtained after median centering the dataset as recommended in Perou et al. [ 31 ] and Lusa et al. [ 32 ].
Concentration response curves were analyzed by the non-linear regression of sigmoid response curve (variable slope).
The simplest form of a reaction norm is described by a linear regression of the response phenotype on the environmental variable known or thought to affect the analysed trait.
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