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significantly better » significantly greater (Expand Search), significantly higher (Expand Search), significantly lower (Expand Search)
significant decrease » significant increase (Expand Search), significantly increased (Expand Search)
better decrease » greater decrease (Expand Search), teer decrease (Expand Search), between decreased (Expand Search)
Showing 2,501 - 2,520 results of 21,342 for search '(( significantly better decrease ) OR ( significant decrease decrease ))', query time: 0.42s Refine Results
  1. 2501
    Cephalometric measurement points.

    Cephalometric measurement points. by Lijuan Liu (136685)

    Published 2025
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  2. 2502
    Posttreatment facial, intraoral photographs, and radiographs of the case with premolar tooth extraction.

    Posttreatment facial, intraoral photographs, and radiographs of the case with premolar tooth extraction. by Lijuan Liu (136685)

    Published 2025
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  3. 2503
    Anonymized raw cephalometric data.

    Anonymized raw cephalometric data. by Lijuan Liu (136685)

    Published 2025
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  4. 2504
    Posttreatment facial, intraoral photographs, and radiographs of the case with mandibular third molar extraction.

    Posttreatment facial, intraoral photographs, and radiographs of the case with mandibular third molar extraction. by Lijuan Liu (136685)

    Published 2025
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  5. 2505
    Comparison of pretreatment cephalometric variables between premolar extraction and mandibular third molar extraction groups.

    Comparison of pretreatment cephalometric variables between premolar extraction and mandibular third molar extraction groups. by Lijuan Liu (136685)

    Published 2025
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  6. 2506
    Demographic characteristics of study participants.

    Demographic characteristics of study participants. by Lijuan Liu (136685)

    Published 2025
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  7. 2507
    Results of Analysis of Co-variance (ANCOVA) for change in parameter predicted by treatment group with pre-treatment value as a covariate. * p < 0.05; ** p < 0.01.

    Results of Analysis of Co-variance (ANCOVA) for change in parameter predicted by treatment group with pre-treatment value as a covariate. * p < 0.05; ** p < 0.01. by Lijuan Liu (136685)

    Published 2025
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  8. 2508
    Comparison of treatment effects between premolar extraction and mandibular third molar extraction groups. * p < 0.05; ** p < 0.01.

    Comparison of treatment effects between premolar extraction and mandibular third molar extraction groups. * p < 0.05; ** p < 0.01. by Lijuan Liu (136685)

    Published 2025
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  9. 2509
    Comparison of upper and lower arch crowding between premolar extraction group and the mandibular third molar extraction groups.

    Comparison of upper and lower arch crowding between premolar extraction group and the mandibular third molar extraction groups. by Lijuan Liu (136685)

    Published 2025
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  10. 2510
    Intraoperative facial and intraoral photographs of the case with mandibular third molar extraction.

    Intraoperative facial and intraoral photographs of the case with mandibular third molar extraction. by Lijuan Liu (136685)

    Published 2025
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  11. 2511
    Pretreatment facial, intraoral photographs, and radiographs of the case with premolar tooth extraction.

    Pretreatment facial, intraoral photographs, and radiographs of the case with premolar tooth extraction. by Lijuan Liu (136685)

    Published 2025
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  12. 2512
    Pretreatment facial, intraoral photographs, and radiographs of the case with mandibular third molar extraction.

    Pretreatment facial, intraoral photographs, and radiographs of the case with mandibular third molar extraction. by Lijuan Liu (136685)

    Published 2025
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  13. 2513
    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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  14. 2514
    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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  15. 2515
    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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  16. 2516
    Office characteristics and RW adoption.

    Office characteristics and RW adoption. by Yasuhiko Deguchi (3167955)

    Published 2025
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  17. 2517
    An infographic outlining the data used for each analysis.

    An infographic outlining the data used for each analysis. by Shelby L. Sturrock (10751675)

    Published 2025
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  18. 2518
    Average difference in mean daily exercise minutes in the week before and after the specified software transition, using different minimum data thresholds.

    Average difference in mean daily exercise minutes in the week before and after the specified software transition, using different minimum data thresholds. by Shelby L. Sturrock (10751675)

    Published 2025
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  19. 2519
    Effect of each software transition and any hardware transition on daily active calories (negative binomial models with a random intercept per participant) among participants with activity data for all 7 days before and all 7 days after transitioning between the indicated software versions, or between two different hardware versions.

    Effect of each software transition and any hardware transition on daily active calories (negative binomial models with a random intercept per participant) among participants with a... by Shelby L. Sturrock (10751675)

    Published 2025
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  20. 2520
    Effect of each software transition and any hardware transition on daily minutes of exercise (negative binomial models with a random intercept per participant) among participants with activity data for all 7 days before and all 7 days after transitioning between the indicated software versions or between two different hardware versions.

    Effect of each software transition and any hardware transition on daily minutes of exercise (negative binomial models with a random intercept per participant) among participants wi... by Shelby L. Sturrock (10751675)

    Published 2025
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