scholarly journals Logarithmic transformation and peak-discharge power-law analysis

2019 ◽  
Vol 51 (1) ◽  
pp. 65-76
Author(s):  
Bo Chen ◽  
Chunying Ma ◽  
Witold F. Krajewski ◽  
Pei Wang ◽  
Feipeng Ren
Keyword(s):  
Linear Regression ◽  
Power Law ◽  
Nonlinear Regression ◽  
The United States ◽  
Logarithmic Transformation ◽  
Peak Discharge ◽  
Discharge Power ◽  
Flood Frequency Analysis ◽  
Scaling Parameters ◽  
Log Linear

Abstract The peak-discharge and drainage area power-law relation has been widely used in regional flood frequency analysis for more than a century. The coefficients and can be obtained by nonlinear or log-log linear regression. To illustrate the deficiencies of applying log-transformation in peak-discharge power-law analyses, we studied 52 peak-discharge events observed in the Iowa River Basin in the United States from 2002 to 2013. The results show that: (1) the estimated scaling exponents by the two methods are remarkably different; (2) for more than 80% of the cases, the power-law relationships obtained by log-log linear regression produce larger prediction errors of peak discharge in the arithmetic scale than that predicted by nonlinear regression; and (3) logarithmic transformation often fails to stabilize residuals in the arithmetic domain, it assigns higher weight to data points representing smaller peak discharges and drainage areas, and it alters the visual appearance of the scatter in the data. The notable discrepancies in the scaling parameters estimated by the two methods and the undesirable consequences of logarithmic transformation raise caution. When conducting peak-discharge scaling analysis, especially for prediction purposes, applying nonlinear regression on the arithmetic scale to estimate the scaling parameters is a better alternative.

Swine Finishing Room Air Infiltration: Part 2. Infiltration as Affected by Room Characteristics

2018 ◽  
Vol 34 (4) ◽  
pp. 735-745
Author(s):  
Harishchandra T Jadhav ◽  
Steven J Hoff ◽  
Jay D Harmon ◽  
Daniel S Andersen
Keyword(s):  
Linear Regression ◽  
Power Law ◽  
Infiltration Rate ◽  
The United States ◽  
Design Criterion ◽  
Large Variability ◽  
Infiltration Rates ◽  
Air Infiltration ◽  
Ceiling Height ◽  
The Relationship

Abstract. Data collected on 17 swine finishing rooms from the Midwest region of the United States was used to study the relationship between infiltration rate and selected room characteristics. Effect of individual room characteristics on room infiltration rate were tested by simple linear regression (SLR) while multiple linear regression (MLR) was used to develop models for improved prediction. SLR results revealed that the total (It) and other (Io; non-curtain/fan locations) swine finishing room infiltration rates were inversely related to room width and directly related to room length and ceiling height. As expected, rooms with higher curtain end pocket overlap, curtain closure overlap distance, and in excellent condition had reduced curtain infiltration (Ic). To reduce fan infiltration (If), fan and pump-out cover perimeter and fan area should be minimized. Power law equations fitted for groups of rooms were found ineffective in accounting for the large variability in infiltration rates of swine finishing rooms as compared to MLR models. MLR models developed for It and Io prediction at 10, 20, and 30 Pa pressure differences were found to improve the prediction over power law models for groups of rooms. At 20 Pa, prediction differences compared with individual room measurements for It rate using the suggested MLR model, as compared to power law models for groups of rooms, were less by at least 61%; whereas, in the case of Io rate, prediction differences compared with individual room measurements were less by at least 49%. Recommendations made in this article, with respect to the relationship between a particular room characteristic and room infiltration rate, could be used as guiding principles along with other design criterion to reduce infiltration rates in remodeled and new swine finishing rooms. Keywords: Infiltration, Swine finishing rooms, Ventilation.

scholarly journals Effectiveness analysis of spatially targeted mollusciciding for Oncomelania snail control

2020 ◽  
Vol 148 ◽  
Author(s):  
Y. Xie ◽  
W. Wu ◽  
Y. Zhang ◽  
Y. Zhang ◽  
Z. Peng ◽  
...  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Nonlinear Regression ◽  
Human Infection ◽  
Snail Density ◽  
Nonlinear Regression Models ◽  
Snail Control ◽  
Density Prediction ◽  
Research Site ◽  
Log Linear

Abstract A new developed spatially targeted mollusciciding technology for snail control was utilised in a research site. This study aims to analyse whether this technology can achieve rational effectiveness compared with the routine method. Snail density was monitored every spring and autumn from 2010 to 2017 at the research site and routine mollusciciding for snail control was then performed. After snail density monitoring in spring 2018, spatially targeted mollusciciding technology was adopted. Log-linear regression and nonlinear regression models were used for snail density prediction in autumn 2018 and the predicted value was compared with the actual snail density in autumn 2018 to verify the effectiveness of the spatially targeted mollusciciding. Monitoring results showed that overall snail density in the research site decreased from 2010 to 2018. The monitored snail density in autumn 2018 was 0.014/0.1 m2. Predicted by the log-linear regression model, the snail density in autumn 2018 would be 0.028 (95% CI 0.11–0.072)/0.1 m2. Predicted by the nonlinear regression model, the snail density growth in autumn 2018 in contrast to spring 2018 would be 79.79% (95% CI 54.81%–104.77%) and the actual value was 55.56%. Therefore, the effectiveness of the first application of spatially targeted mollusciciding was acceptable. However, the validation of its sustainable effectiveness still needs a replicated study comparing areas where targeted and untargeted methods are applied simultaneously and both snail abundance and human infection are monitored.

scholarly journals CO<sub>2</sub> flux determination by closed-chamber methods can be seriously biased by inappropriate application of linear regression

2007 ◽  
Vol 4 (6) ◽  
pp. 1005-1025 ◽  
Author(s):  
L. Kutzbach ◽  
J. Schneider ◽  
T. Sachs ◽  
M. Giebels ◽  
H. Nykänen ◽  
...  
Keyword(s):  
Linear Regression ◽  
Nonlinear Regression ◽  
Co2 Flux ◽  
Exponential Model ◽  
Co2 Uptake ◽  
Co2 Fluxes ◽  
Closed Chamber ◽  
Exponential Regression ◽  
Closure Time ◽  
Over Time

Abstract. Closed (non-steady state) chambers are widely used for quantifying carbon dioxide (CO2) fluxes between soils or low-stature canopies and the atmosphere. It is well recognised that covering a soil or vegetation by a closed chamber inherently disturbs the natural CO2 fluxes by altering the concentration gradients between the soil, the vegetation and the overlying air. Thus, the driving factors of CO2 fluxes are not constant during the closed chamber experiment, and no linear increase or decrease of CO2 concentration over time within the chamber headspace can be expected. Nevertheless, linear regression has been applied for calculating CO2 fluxes in many recent, partly influential, studies. This approach has been justified by keeping the closure time short and assuming the concentration change over time to be in the linear range. Here, we test if the application of linear regression is really appropriate for estimating CO2 fluxes using closed chambers over short closure times and if the application of nonlinear regression is necessary. We developed a nonlinear exponential regression model from diffusion and photosynthesis theory. This exponential model was tested with four different datasets of CO2 flux measurements (total number: 1764) conducted at three peatlands sites in Finland and a tundra site in Siberia. Thorough analyses of residuals demonstrated that linear regression was frequently not appropriate for the determination of CO2 fluxes by closed-chamber methods, even if closure times were kept short. The developed exponential model was well suited for nonlinear regression of the concentration over time c(t) evolution in the chamber headspace and estimation of the initial CO2 fluxes at closure time for the majority of experiments. However, a rather large percentage of the exponential regression functions showed curvatures not consistent with the theoretical model which is considered to be caused by violations of the underlying model assumptions. Especially the effects of turbulence and pressure disturbances by the chamber deployment are suspected to have caused unexplainable curvatures. CO2 flux estimates by linear regression can be as low as 40% of the flux estimates of exponential regression for closure times of only two minutes. The degree of underestimation increased with increasing CO2 flux strength and was dependent on soil and vegetation conditions which can disturb not only the quantitative but also the qualitative evaluation of CO2 flux dynamics. The underestimation effect by linear regression was observed to be different for CO2 uptake and release situations which can lead to stronger bias in the daily, seasonal and annual CO2 balances than in the individual fluxes. To avoid serious bias of CO2 flux estimates based on closed chamber experiments, we suggest further tests using published datasets and recommend the use of nonlinear regression models for future closed chamber studies.

Are seasonal lakes as productive as permanent lakes? A case study from Ireland

2010 ◽  
Vol 67 (8) ◽  
pp. 1291-1302 ◽  
Author(s):  
Helder Cunha Pereira ◽  
Norman Allott ◽  
Catherine Coxon
Keyword(s):  
Seasonal Variation ◽  
Linear Regression ◽  
Chlorophyll A ◽  
Algal Biomass ◽  
P Limitation ◽  
Underlying Causes ◽  
Western Ireland ◽  
Log Linear ◽  
First Time

This paper compares, for the first time, nutrient levels and chlorophyll a measured in a set of seasonal lakes with those reported for permanent lakes in the literature. Twenty-two turloughs (karstic seasonal lakes) in western Ireland were sampled monthly from the onset of flooding (October) until they dried out (6 to 9 months). The turloughs showed similar levels of nutrients and chlorophyll a to those reported for Irish and international lakes. Chlorophyll a peaked between November and February in the majority of turloughs, sometimes with values higher than those measured in mesotrophic lakes in summer. A significant log-linear regression was found between total phosphorus and chlorophyll a, which suggests P limitation of algal biomass in the majority of the turloughs. The regression characteristics were not significantly different than those described in similar studies of permanent lakes. Patterns in seasonal variation of nutrients are also presented, their underlying causes being discussed in relation to their transport within catchments. Our results show that despite being predominantly winter phenomena, turloughs can be as productive as permanent lakes.

scholarly journals How Does Antimicrobial Resistance Increase Medical Costs in Community-Acquired Acute Pyelonephritis?

2021 ◽  
Vol 1 (S1) ◽  
pp. s23-s23
Author(s):  
Bongyoung Kim ◽  
Taul Cheong ◽  
Jungmo Ahn
Keyword(s):  
Antimicrobial Resistance ◽  
Linear Regression ◽  
Acute Pyelonephritis ◽  
Tertiary Care ◽  
Medical Costs ◽  
Hospitalized Patients ◽  
Care Hospital ◽  
Causative Pathogen ◽  
Significant Difference ◽  
Log Linear

Background: The proportion of antimicrobial-resistant Enterobacterales that are causative pathogens for community-acquired acute pyelonephritis (CA-APN) has been increasing. We examined the effect of antimicrobial resistance on medical costs in CA-APN. Methods: A single-center retrospective cohort study was conducted at a tertiary-care hospital in Korea between January 2018 to December 2019. All hospitalized patients aged ≥19 years who were diagnosed with CA-APN were recruited, and those with Enterobacterales as a causative pathogen were included. Comparisons between CA-APN caused by extended-spectrum β-lactamase (ESBL)–producing pathogens (ESBL+ group) and those by non–ESBL-producing organisms (ESBL– group) as well as CA-APN caused by ciprofloxacin-resistant pathogens (CIP-R group) and those by ciprofloxacin-sensitive pathogens (CIP-S group) were performed. Log-linear regression was performed to determine the risk factors for medical costs. Results: In total, 241 patients were included in this study. Of these, 75 (31.1%) had an ESBL-producing pathogen and 87 (36.1%) had a ciprofloxacin-resistant pathogen. The overall medical costs were significantly higher in the ESBL+ group compared with the ESBL− group (US$3,730.18 vs US$3,119.32) P <0.001) as well as in CIP-R group compared with CIP-S group (3,730.18 USD vs. 3,119.32 USD, P =0.005). In addition, length of stay was longer in ESBL+ group compared with ESBL-group (11 vs. 8 days, P <0.001) as well as in CIP-R group compared with CIP-S group (11 vs. 8 days, P <0.001). There were no significant difference in the proportion of clinical failure between ESBL+ and ESBL- groups; CIP-R and CIP-S groups. Based on the log-linear regression model, the costs associated with ESBL-producing Enterobacterales as the causative pathogen would be, on average, 27% higher or US$1,211 higher than its counterpart (P = .026). By the same token, a patient who is a year older would incur US$23 higher cost (P = .040). Having any structural problem in urinary tract would incur US$1,231 higher cost (P = .015). A unit increase in Pitt score would incur US$767 USD higher cost (P < 0.001) higher cost, all other things constant. Conclusions: Medical costs for hospitalized patients with CA-APN are increased by the existence of ESBL-producing Enterobacterales but not by the existence of ciprofloxacin-resistant Enterobacterales.Funding: NoDisclosures: None

scholarly journals COVID-19 Incidence and Hospitalization Rates are Inversely Related to Vaccination Coverage Among the 112 Most Populous Counties in the United States

2021 ◽  
Author(s):  
Jeffrey E. Harris
Keyword(s):  
United States ◽  
Vaccination Coverage ◽  
Hospital Admissions ◽  
The United States ◽  
Lower Half ◽  
Linear Regression Models ◽  
Percentage Point Increase ◽  
Hospitalization Rates ◽  
Point Increase ◽  
Log Linear

AbstractWe tested whether COVID-19 incidence and hospitalization rates were inversely related to vaccination coverage among the 112 most populous counties in the United States, each with a population exceeding 600,000. We measured vaccination coverage as the percent of the total population fully vaccinated as of July 15, 2021, with the exception of 11 Texas counties, where the cutoff date was July 14, 2021. We measured COVID-19 incidence as the number of confirmed cases per 100,000 population during the 14-day period ending August 12, 2021. We measured hospitalization rates as the number of confirmed COVID-19 admissions per 100,000 population during the same 14-day period. COVID-19 incidence was significantly higher among counties in the lower half of the distribution of vaccination coverage (incidence 543.8 per 100,000 among 56 counties with mean coverage 42.61%) than among counties in the lower half of the distribution of coverage (incidence 280.7 per 100,000 among 56 counties with mean coverage 57.37%, p < 0.0001). Hospital admissions were also significantly higher among counties in the lower half of the distribution (55.37 per 100,000) than in the upper half of the distribution (20.48 per 100,000, p < 0.0001). In log-linear regression models, a 10-percentage-point increase in vaccination coverage was associated with a 28.3% decrease in COVID-19 incidence (95% confidence interval, 16.8 – 39.7%), a 44.9 percent increase in the rate of COVID-19 hospitalization (95% CI, 28.8 – 61.0%), and a 16.6% decrease in COVID-19 hospitalizations per 100 cases (95% CI, 8.4 – 24.8%). Higher vaccination coverage is associated not only with significantly lower COVID-19 incidence, but also significantly less severe cases of the disease.

Statistical Techniques for Interpreting Body Size–Related Exercise Performance during Growth

2000 ◽  
Vol 12 (2) ◽  
pp. 112-127 ◽  
Author(s):  
Joanne R. Welsman ◽  
Neil Armstrong
Keyword(s):  
Body Size ◽  
Linear Regression ◽  
Body Mass ◽  
Exercise Performance ◽  
Multilevel Regression ◽  
Cross Sectional ◽  
Regression Techniques ◽  
Ontogenetic Allometry ◽  
Log Linear ◽  
Individual Size

This paper reviews some of the statistical methods available for controlling for body size differences in the interpretation of developmental changes in exercise performance. For cross-sectional data analysis simple per body mass ratio scaling continues to be widely used, but is frequently ineffective as the computed ratio remains correlated with body mass. Linear regression techniques may distinguish group differences more appropriately but, as illustrated, only allometric (log-linear regression) scaling appropriately removes body size differences while accommodating the heteroscedasticity common in exercise performance data. The analysis and interpretation of longitudinal data within an allometric framework is complex. More established methods such as ontogenetic allometry allow insights into individual size-function relationships but are unable to describe adequately population effects or changes in the magnitude of the response. The recently developed multilevel regression modeling technique represents a flexible and sensitive solution to such problems allowing both individual and group responses to be modeled concurrently.

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