The kernel density estimate (kde) is used here as a quick check of normality for each of the variables of interest in the model. All data here has been nested by subject. To reduce the influence of outliers, 30 (18.0%) participants were excluded due to 'Dotloc' or 'Stimulus Onset Errors' 3 SD above the median.
Figure 1. Kernel Density Estimate for participant.
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Figure 2. Kernel Density Estimate for m_rt.
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Figure 3. Kernel Density Estimate for m_diff_dotloc.
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Figure 4. Kernel Density Estimate for m_diff_stim.
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Figure 5. Kernel Density Estimate for luminance.
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Figure 6. Kernel Density Estimate for rrs_brooding.
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Figure 7. Kernel Density Estimate for cesd_score.
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Figure 8. Kernel Density Estimate for dp_bias.
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Figure 9. Kernel Density Estimate for n_dp_valid.
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Figure 10. Kernel Density Estimate for gaze_bias.
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Figure 11. Kernel Density Estimate for n_gaze_valid.
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Figure 12. Kernel Density Estimate for var_gaze_bias.
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Figure 13. Kernel Density Estimate for final_gaze_bias.
Last Updated: 2019-03-25 09:26:39