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The dependent variable in functional imaging studies is the hemodynamic response: a local increase in synaptic activity is associated with increased local metabolism coupled to an increase in regional cerebral blood flow (rCBF).
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Nevertheless, owing to the relevance of sociodemographic variables in functional capacity assessment, we recommend considering their distribution when making comparisons with results from studies conducted in different urban contexts.
Multiple linear regression will be used to investigate the associations of the main predictor variable, change in RT3 VM counts/hour/week (ΔRT3) from baseline to 3 months, with the main outcome variable change in functional outcome (ΔRMDQ).
A mathematical model for variable selection in functional linear regression models with scalar response is proposed.
Finally, it compares the results to those obtained by means of two different methods: a nonparametric kernel smoothing for variable selection in functional data and a wavelet-based weighted LASSO functional linear regression.
Moreover, fixing one variable in the functional in (1.1) yielded the following log-majorization: begin{aligned} bigl(AZ^BZA bigr)^{p}prec_{w log} A^{p} Z^B^{p}ZA^{p.
Moreover, we investigated the relative importance of two sets of variables, socio-demographic and physical health status variables, in influencing functional status.
Therefore, the anxiety/depression subscale of the EQ-5D was used as an outcome variable instead of a predictor variable in terms of functional health.
The primary outcome variable was the change in functional status from baseline according to the ICRS form.
The unknown function is the functional variable in equation and inequalities, and the partial derivatives appear in a classical sense.
Changes in functional variables were tested with two-tailed student's paired t-tests.
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