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Showing 1 - 20 results of 8,356 for search '(( significant predictor decrease ) OR ( significant differences decrease ))', query time: 0.50s Refine Results
  1. 1
    Logistic regression for influenza results predictors.

    Logistic regression for influenza results predictors. by Reem Eid (20889732)

    Published 2025
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  2. 2
    Logistic regression for RSV results predictors.

    Logistic regression for RSV results predictors. by Reem Eid (20889732)

    Published 2025
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  3. 3
    Analysis of Significantly Different Bacteria.

    Analysis of Significantly Different Bacteria. by Xu Jia (187142)

    Published 2025
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  4. 4
    Multivariate regression analysis to explain the decrease in visibility and palpability.

    Multivariate regression analysis to explain the decrease in visibility and palpability. by Miharu Matsumoto (21304390)

    Published 2025
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  5. 5
    Brain regions with significantly decreased activations for the Raven task are shown in blue.

    Brain regions with significantly decreased activations for the Raven task are shown in blue. by Morteza Fattahi (15892028)

    Published 2025
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  6. 6
    Analysis of Significantly Different ARGs and Resistance Mechanisms.

    Analysis of Significantly Different ARGs and Resistance Mechanisms. by Xu Jia (187142)

    Published 2025
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  7. 7
    TBSS results showing significant AxD and RD changes at baseline and the follow-up.

    TBSS results showing significant AxD and RD changes at baseline and the follow-up. by Sewon Lim (21989785)

    Published 2025
    “…<p>The regions indicated in green represent the mean white matter skeleton, whereas the regions indicated in red represent the regions with increased AxD (first row) and RD (second row). The decreased diffusivity metrics (AxD and RD) exhibited no statistically significant differences at <i><i>P</i></i> < 0.05 after correcting for multiple comparisons (FWE). …”
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  8. 8
    <b>Addition root exudates increase microbial biomass but decreases diversity and richness.</b>

    <b>Addition root exudates increase microbial biomass but decreases diversity and richness.</b> by Xiaochong Zhang (21179348)

    Published 2025
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  9. 9
    Forest plots of APTT for 28-day mortality in different subgroups.

    Forest plots of APTT for 28-day mortality in different subgroups. by Huan Wang (109286)

    Published 2025
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  10. 10
    Identification of ACADM, ANGPTL4, and NFKB2 as significant predictors of OS in the TCGA-KIRC cohort.

    Identification of ACADM, ANGPTL4, and NFKB2 as significant predictors of OS in the TCGA-KIRC cohort. by Yu Zhang (12946)

    Published 2025
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  11. 11
    Analysis of the significantly different metabolomic features.

    Analysis of the significantly different metabolomic features. by Bhuripit Saraphol (20855573)

    Published 2025
    “…<b>(B)</b> Volcano plot highlighting the significantly different metabolomic features. The red dots represent the features with significantly increased intensity, and the purple dots represent the features with significantly decreased intensity. …”
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  12. 12
    The table shows the significant and non-significant altered group with different parameters.

    The table shows the significant and non-significant altered group with different parameters. by Ashraf Attia Mahmoud (21262201)

    Published 2025
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  13. 13
    Hypothesis 3: The mean difference between “Class 1” (a school attack happens within <i>k</i> kms. of a given school) and “Class 0” (no school attack happens within <i>k</i> kms. of a given school) for increases as the Socioeconomic Risk Score of the region of each school decreases (i.e. the school is in a wealthier neighborhood).

    Hypothesis 3: The mean difference between “Class 1” (a school attack happens within <i>k</i> kms. of a given school) and “Class 0” (no school attack happens within <i>k</i> kms. of... by Lirika Sola (21560710)

    Published 2025
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  14. 14
    Graphical presentation of % decrease for insulin from baseline at different conditions.

    Graphical presentation of % decrease for insulin from baseline at different conditions. by Nathan Mubiru (21627195)

    Published 2025
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  15. 15
    Scatter plots of the relationships between age and the proportions of cells which statistically significantly correlated with age.

    Scatter plots of the relationships between age and the proportions of cells which statistically significantly correlated with age. by Arttu Autio-Kimura (20454681)

    Published 2024
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  16. 16
    Generalized estimating equation (GEE) model results on the influence of weekly weather conditions and air quality on weekly visitation to federal parks and protected areas (PPA), including national parks, national monuments, and national forests included in this study. The odds ratio, calculated as exp(Standard Estimate), indicates the magnitude and direction of the predictors’ effect on the response variable (proportional weekly visitation). The visitation effect, calculated as -1*(1-Odds Ratio)*100 indicates the percent change in the response variable when the predictor increases by one unit (for continuous variables) or when the condition is true (for categorical variables). Significant results (p<0.05) in bold. AQI = air quality index; PM 2.5 = particulate matter with a diameter of 2.5 microns.

    Generalized estimating equation (GEE) model results on the influence of weekly weather conditions and air quality on weekly visitation to federal parks and protected areas (PPA), i... by Kira Minehart (21029571)

    Published 2025
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  17. 17
    Generalized estimating equation (GEE) model results on the influence of weekly weather conditions and air quality on weekly visitation to local parks and protected areas (PPA), including city parks, county parks, and regional parks included in this study. The odds ratio, calculated as exp(Standard Estimate), indicates the magnitude and direction of the predictors’ effect on the response variable (proportional weekly visitation). The visitation effect, calculated as -1*(1-Odds Ratio)*100 indicates the percent change in the response variable when the predictor increases by one unit (for continuous variables) or when the condition is true (for categorical variables). Significant results (p<0.05) in bold. AQI = air quality index; PM 2.5 = particulate matter with diameter <2.5 μm.

    Generalized estimating equation (GEE) model results on the influence of weekly weather conditions and air quality on weekly visitation to local parks and protected areas (PPA), inc... by Kira Minehart (21029571)

    Published 2025
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  18. 18
    Generalized estimating equation (GEE) model results on the influence of weekly weather conditions and air quality on weekly visitation to state parks and protected areas (PPA), including state parks and state recreation areas included in this study. The odds ratio, calculated as exp(Standard Estimate), indicates the magnitude and direction of the predictors’ effect on the response variable (proportional weekly visitation). The visitation effect, calculated as -1*(1-Odds Ratio)*100 indicates the percent change in the response variable when the predictor increases by one unit (for continuous variables) or when the condition is true (for categorical variables). Significant results (p<0.05) in bold. AQI = air quality index; PM 2.5 = particulate matter with diameter <2.5 μm.

    Generalized estimating equation (GEE) model results on the influence of weekly weather conditions and air quality on weekly visitation to state parks and protected areas (PPA), inc... by Kira Minehart (21029571)

    Published 2025
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  19. 19
    Correlations between significantly different microbiota and metabolites.

    Correlations between significantly different microbiota and metabolites. by Bhuripit Saraphol (20855573)

    Published 2025
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  20. 20
    Graphical presentation of % decrease for C-peptide from baseline at different conditions.

    Graphical presentation of % decrease for C-peptide from baseline at different conditions. by Nathan Mubiru (21627195)

    Published 2025
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