Showing 61 - 80 results of 9,423 for search '(( significant linear decrease ) OR ( significantly ((we decrease) OR (mean decrease)) ))', query time: 0.38s Refine Results
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    Univariate linear regression analysis of scales. by Xiangyu Wang (341093)

    Published 2025
    “…<div><p>Objective</p><p>Body image perception significantly impacts university students’ well-being and potentially their creativity. …”
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    Overview of the WeARTolerance program. by Ana Beato (20489933)

    Published 2024
    “…<div><p>The stigma surrounding mental health remains a significant barrier to help-seeking and well-being in youth populations. …”
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    Significantly Enriched Pathways. by Jiyong Zhang (2498740)

    Published 2025
    “…By comparing samples from NAFLD patients and healthy controls, we identified 1,770 significant DEGs, with 1,073 being upregulated and 697 downregulated. …”
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    Study-related adverse events. by Benjamin R. Lewis (22279166)

    Published 2025
    “…In a linear mixed model analysis (LMM), the MBSR + PAP arm evidenced a significantly larger decrease in QIDS-SR-16 score than the MBSR-only arm from baseline to 2-weeks post-intervention (between-groups effect = 4.6, 95% CI [1.51, 7.70]; <i>p</i> = 0.008). …”
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    Study flow chart. by Benjamin R. Lewis (22279166)

    Published 2025
    “…In a linear mixed model analysis (LMM), the MBSR + PAP arm evidenced a significantly larger decrease in QIDS-SR-16 score than the MBSR-only arm from baseline to 2-weeks post-intervention (between-groups effect = 4.6, 95% CI [1.51, 7.70]; <i>p</i> = 0.008). …”
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    Study CONSORT diagram. by Benjamin R. Lewis (22279166)

    Published 2025
    “…In a linear mixed model analysis (LMM), the MBSR + PAP arm evidenced a significantly larger decrease in QIDS-SR-16 score than the MBSR-only arm from baseline to 2-weeks post-intervention (between-groups effect = 4.6, 95% CI [1.51, 7.70]; <i>p</i> = 0.008). …”
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    Mean parameter values for the selected crops. by Gourab Saha (8987405)

    Published 2025
    “…Multi-spectral band images from Landsat-8 satellite images of a chosen land are employed from USGS Earth Resources Observation and Science (EROS) Center for extracting indices that are used for agricultural analysis, determining the vegetation index, water index, and salinity index of that land using K-means. Furthermore, crop yield is predicted using Linear Regression and Random Forest, achieving accuracies of 93.49% and 95.87%, respectively, while using RMSE (Root Mean Squared Error) as the loss function. …”
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