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10.7554/eLife.08825.009 Figure 5. Triangles task choice data pooled across all 48 subjects (top row), plus predictions from fits to the normative model allowing a different subjective hazard rate to be assigned to each block (middle row) and from fits to a model with subjective hazard rates randomly assigned across trials (bottom row).

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Using 2004 data from the SOEP merged with regional and neighbourhood information, we fitted multilevel linear regression models with subjective physical health as dependent variable.

Using 2004 data from the German Socio-Economic Panel Study (SOEP) merged with regional and neighbourhood characteristics, we fitted multilevel linear regression models with subjective physical health, as measured by the SF-12, as the dependent variable.

Many of the "objective" landscape characteristics found to significantly influence nearby recreation in the model match with "subjective" preferences, i.e. distance to residence, open water, forests, summits with overview and avoidance of major roads.

10.7554/eLife.08825.016 Figure 8. Dots-reversal task choice dynamics comparing the pooled data from 13 subjects (top row) with predictions from fits to the normative model (middle row) and to the normative model with the subjective hazard rates shuffled across trials and blocks for each subject and session (bottom row).

In order to better understand the observed gap between QoE models found with subjective and with aBCI features, Pearson correlations were calculated between AI and MBP and the subjective valence and arousal ratings.

The objective weights concurrently were determined by solving mathematical models and were amalgamated with subjective weights in the FMCDM model.

Confirmatory factor analysis, CFA, with AMOS 16 was employed to test the hypothesized measurement model with respect to intention, attitudes, subjective norms and moral norms, specifying the relationship between the observed variables (indicators) and the underlying latent variables (concepts).

Most validation studies have compared food classifications from a nutrient profile model with 'a standard derived from subjective decisions from nutritionists and dietitians', or have examined the proportions of foods classified as healthy/unhealthy by a model that make up healthy/unhealthy diets assessed with varying degrees of subjectivity.

A hybrid rating system combined with the Kano model in subjective data collection can provide a much needed objectivity and un-biased rating in the usability study.

Furthermore, the logistic regression model including subjective ultrasound assessment with the addition of HE4 and CA125 was conducted.

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