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When determining the model for each response, a regression analysis is performed on the basis of a partial model containing only the terms which are statistically significant at a 0.05 level of significance.
In order to quantify this response, a regression analysis of flowering time over the generation of selection (3) was performed separately in each population derived from each line.
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In metamodelling of the dynamic models, the parameter values and initial conditions were then used as regressors (X) and the state variable time series were used as response variables (Y) in a regression analysis, in order to predict the state variable time series directly from the parameters and initial conditions.
To calculate the dependence of the neural responses on morph level, we performed a regression analysis for each cell for each epoch.
We then performed a regression analysis predicting antibody response from gene expression and we used the residuals from that analysis to perform another association analysis.
A regression analysis of the dose response of CCE were performed.
The experimental data on cumulative hydrogen production and COD reduction were fitted into a quadratic polynomial model using response surface regression analysis.
Also, anti-TNF treatment was not identified as a significant predictor of impaired antibody response in regression analysis.
Four input parameters were used for the development of response surface regression analysis.
Response surface regression analysis and analysis of variance (ANOVA) were performed by Minitab software (Version 16, Minitab Inc, State College, PA, USA).
These values indicated that model equation was highly significant and fitted the response surface regression analysis of the data.
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