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Showing 1 - 20 results of 15,286 for search '(( significant mean decrease ) OR ( significant predictors types ))', query time: 0.74s Refine Results
  1. 1
    SHAP scores of significant CA and RT predictors.

    SHAP scores of significant CA and RT predictors. by Oliver Ocsenas (13229013)

    Published 2022
    “…<p>Significant predictors of megabase-scale SNV burden for all cancer types are shown (<i>P</i> < 0.001). …”
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  2. 2
    Significant predictors in ZIB models in Experiment Part 2.

    Significant predictors in ZIB models in Experiment Part 2. by Kuangzhe Xu (17094765)

    Published 2023
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  3. 3
    Predictors of various types of DTP.

    Predictors of various types of DTP. by Muhammad Hayat (4278382)

    Published 2023
    “…Multivariate analysis was conducted to find the predictors of individual types of DTPs. A p-value <0.05 was considered statistically significant.…”
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  4. 4
    SHAP scores of significant CA and RT predictors of SBS burden in breast cancer.

    SHAP scores of significant CA and RT predictors of SBS burden in breast cancer. by Oliver Ocsenas (13229013)

    Published 2022
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  5. 5
    Significant predictors for women sample.

    Significant predictors for women sample. by Ainara Nardi-Rodríguez (11598908)

    Published 2024
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  6. 6
    Significant predictors for men sample.

    Significant predictors for men sample. by Ainara Nardi-Rodríguez (11598908)

    Published 2024
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  7. 7
    All significant predictors of mutational SBS signatures (permutation <i>P</i> < 0.001).

    All significant predictors of mutational SBS signatures (permutation <i>P</i> < 0.001). by Oliver Ocsenas (13229013)

    Published 2022
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  8. 8
    Summary of the effect of MPDD on SDLP across all participants, and also participants categorized by driving styles (“NS” (no significant), “+” (significant increase), and “-” (significant decrease)).

    Summary of the effect of MPDD on SDLP across all participants, and also participants categorized by driving styles (“NS” (no significant), “+” (significant increase), and “-” (sign... by Mobina Faqani (22783963)

    Published 2025
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  9. 9
    African-Caribbean ethnicity is an independent predictor of significant decline in kidney function in people with type 1 diabetes

    African-Caribbean ethnicity is an independent predictor of significant decline in kidney function in people with type 1 diabetes by Anastasios Mangelis (3363932)

    Published 2022
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  10. 10
    Summary of the effect of MPDD on ART and TIBL across all participants, and also participants categorized by driving styles (“NS” (no significant), “+” (significant increase), and “-” (significant decrease).

    Summary of the effect of MPDD on ART and TIBL across all participants, and also participants categorized by driving styles (“NS” (no significant), “+” (significant increase), and “... by Mobina Faqani (22783963)

    Published 2025
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  11. 11
    The significance (<i>p</i>-values) of the predictor variables and their interactions on the different outcome variables.

    The significance (<i>p</i>-values) of the predictor variables and their interactions on the different outcome variables. by Charlotte Vanden Hole (7485560)

    Published 2019
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  12. 12
    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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  13. 13
    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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  14. 14
    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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  15. 15
    Effect of predictor type and data length.

    Effect of predictor type and data length. by Joshua P. Kulasingham (17935949)

    Published 2024
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  16. 16
    The number of gauging cross-sections where a statistically significant decreasing trend was identified.

    The number of gauging cross-sections where a statistically significant decreasing trend was identified. by Krzysztof Kochanek (835532)

    Published 2024
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  17. 17
    Mean decrease Gini score by Tweet and user characteristics.

    Mean decrease Gini score by Tweet and user characteristics. by Gray Babbs (16648894)

    Published 2023
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  18. 18
    Decrease of the mean number of natural teeth and POPs with age.

    Decrease of the mean number of natural teeth and POPs with age. by Sul-Hee Kim (11873853)

    Published 2021
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  19. 19
    Significant correlations between fuel consumption (FC) and pollutant emissions (as dependant variables) and payload (as predictor) for different vehicle types.

    Significant correlations between fuel consumption (FC) and pollutant emissions (as dependant variables) and payload (as predictor) for different vehicle types. by Charles Sassine (21300709)

    Published 2025
    “…<p>Significant correlations between fuel consumption (FC) and pollutant emissions (as dependant variables) and payload (as predictor) for different vehicle types.…”
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  20. 20
    Significant correlations between fuel consumption (FC) and pollutant emissions (as dependant variables) and speed (as predictor) for different vehicle types.

    Significant correlations between fuel consumption (FC) and pollutant emissions (as dependant variables) and speed (as predictor) for different vehicle types. by Charles Sassine (21300709)

    Published 2025
    “…<p>Significant correlations between fuel consumption (FC) and pollutant emissions (as dependant variables) and speed (as predictor) for different vehicle types.…”
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