Your English writing platform
Discover LudwigSuggestions(2)
Exact(3)
The materials and procedure were identical to Experiment 1 except that there was an additional between-subjects factor of training.
In order to maintain the RD performance of CU splitting early termination algorithm, RD loss due to misclassification is introduced as weighting factor of training samples in the offline training procedure, with which the training method pays special attention to CUs which are prone to degrade RD performance when using a suboptimal partition.
Change in anxiety over time was assessed using a (2 × 2) split-plot analysis of variance (ANOVA) with the between-subject factor of training group and the within-subject factor of time of assessment (i.e., before or after training).
Similar(57)
Univariate linear regression analyses were performed to delineate predictive factors of training response (relative pre- to post changes in gait speed) for the IG.
ANOVA of individual peak amplitude measurements with factors of training, stimulus, and frontocentral electrode sites as a repeated measure revealed a trend for an effect of training on P2 peak amplitude (F1,48 = 3.57, P < 0.065).
Mean activation levels relative to the rest baseline within the left mSTS and left pSTS were computed and entered into analyses of variance (ANOVAs) with factors of training, stimulus and region of interest (ROI).
The purpose of the ROI analysis was to assess the direction and size of main effects and interactions of the factors of training and stimulus on the level of activation in each region.
Two-way ANOVA with factors of training and ROI showed an effect of ROI on mean activation for NP whereby the activation was stronger in left pSTS compared with left mSTS (F1,72 = 5.82, P < 0.02).
The data were submitted to ANOVA for repeated measures, the independent factor being group (trained, pseudo-trained or control groups); the repeated factor, day of training (n = 10).
Criterion validity was also good indicating that the instrument was valid, while the exploratory factor analysis of training needs revealed the shared variance of 7 separate factors.
It implies better loading factors of trains, 10%% more TEU per length on fewer axles, and thus lower energy consumption, less maintenance and lower cost per transported unit (VEL wagon 2012).
Write better and faster with AI suggestions while staying true to your unique style.
Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com