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Showing 1 - 20 results of 3,816 for search '(( significant predictors decrease ) OR ( significant ((i.e decrease) OR (mean decrease)) ))', query time: 0.62s Refine Results
  1. 1
    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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  2. 2
    Intervention with MLKL decreased neutrophil death and protected mice against <i>E. coli</i> pneumonia.

    Intervention with MLKL decreased neutrophil death and protected mice against <i>E. coli</i> pneumonia. by Qinyu Luo (13000956)

    Published 2025
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  3. 3
    Descriptive statistics (means) for full sample and by lockdown status.

    Descriptive statistics (means) for full sample and by lockdown status. by Drew B. Cameron (10687420)

    Published 2024
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  4. 4
    Boxplot mean cfPWV comparisons.

    Boxplot mean cfPWV comparisons. by Johannes Nairz (18010746)

    Published 2024
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  5. 5
    Results of multiple logistic regression analysis to identify potential factors associated with the successful reduction of HPC and F+ coliphage in the POU UVC LED effluent. Single predictor p-values <0.05 indicate statistical significance of that variable with the model outcome. Overall model p-values indicate the model’s overall performance compared to a null model where all coefficients are set to 0. The reported models are the result of an automated model selection algorithm which identified the best-fitting model of all possible predictor variables.

    Results of multiple logistic regression analysis to identify potential factors associated with the successful reduction of HPC and F+ coliphage in the POU UVC LED effluent. Single... by Hania Zanib (21864477)

    Published 2025
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  6. 6
    GLM models for predictors of monthly HIV costs.

    GLM models for predictors of monthly HIV costs. by Drew B. Cameron (10687420)

    Published 2024
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  7. 7
    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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  8. 8
    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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  9. 9
    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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  10. 10
    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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  11. 11
    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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  12. 12
    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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  13. 13
    Logistic regression for influenza results predictors.

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

    Published 2025
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  14. 14
    Logistic regression for RSV results predictors.

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

    Published 2025
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  15. 15
    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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  16. 16
    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
    “…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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  17. 17
    Error metric values i.e. MAPE and NMRE in JJA (main rainy season) rainfall between station data and CHIRPS data set before and after bias correction.

    Error metric values i.e. MAPE and NMRE in JJA (main rainy season) rainfall between station data and CHIRPS data set before and after bias correction. by Asnake Adane (19935018)

    Published 2025
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  18. 18
    Predictors in ordinal regression model for GDS.

    Predictors in ordinal regression model for GDS. by Shane Naidoo (20148021)

    Published 2025
    “…In an ordinal regression model BMI was a significant predictor (<i>B</i> = .10, <i>p</i> = .007) for increases in depression. …”
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  19. 19
    Mean scores, standard deviations, and significance on key measures and subscales by gender group.

    Mean scores, standard deviations, and significance on key measures and subscales by gender group. by Silas Manning (22693338)

    Published 2025
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  20. 20
    Tetraspanins variably affect the virulence of <i>E</i>. <i>histolytica</i>.

    Tetraspanins variably affect the virulence of <i>E</i>. <i>histolytica</i>. by Han Jiang (195350)

    Published 2024
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