Multiple linear regression models for predicting chronic aluminum toxicity to freshwater aquatic organisms and developing water quality guidelines

2017 ◽  
Vol 37 (1) ◽  
pp. 80-90 ◽  
Author(s):  
David K. DeForest ◽  
Kevin V. Brix ◽  
Lucinda M. Tear ◽  
William J. Adams
Keyword(s):  
Water Quality ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Regression Models ◽  
Aluminum Toxicity ◽  
Aquatic Organisms ◽  
Linear Regression Models ◽  
Water Quality Guidelines ◽  
Quality Guidelines ◽  
Multiple Linear Regression Models

scholarly journals The effects of precipitation, river discharge, land use and coastal circulation on water quality in coastal Maine

2015 ◽  
Vol 2 (7) ◽  
pp. 140429 ◽  
Author(s):  
Charles E. Tilburg ◽  
Linda M. Jordan ◽  
Amy E. Carlson ◽  
Stephan I. Zeeman ◽  
Philip O. Yund
Keyword(s):  
Water Quality ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
River Discharge ◽  
Regression Models ◽  
Mechanistic Model ◽  
Commercial Fishing ◽  
Coastal Circulation ◽  
Linear Regression Models ◽  
Multiple Linear Regression Models

Faecal pollution in stormwater, wastewater and direct run-off can carry zoonotic pathogens to streams, rivers and the ocean, reduce water quality, and affect both recreational and commercial fishing areas of the coastal ocean. Typically, the closure of beaches and commercial fishing areas is governed by the testing for the presence of faecal bacteria, which requires an 18–24 h period for sample incubation. As water quality can change during this testing period, the need for accurate and timely predictions of coastal water quality has become acute. In this study, we: (i) examine the relationship between water quality, precipitation and river discharge at several locations within the Gulf of Maine, and (ii) use multiple linear regression models based on readily obtainable hydrometeorological measurements to predict water quality events at five coastal locations. Analysis of a 12 year dataset revealed that high river discharge and/or precipitation events can lead to reduced water quality; however, the use of only these two parameters to predict water quality can result in a number of errors. Analysis of a higher frequency, 2 year study using multiple linear regression models revealed that precipitation, salinity, river discharge, winds, seasonality and coastal circulation correlate with variations in water quality. Although there has been extensive development of regression models for freshwater, this is one of the first attempts to create a mechanistic model to predict water quality in coastal marine waters. Model performance is similar to that of efforts in other regions, which have incorporated models into water resource managers' decisions, indicating that the use of a mechanistic model in coastal Maine is feasible.

scholarly journals Isotonic quadriceps endurance is better associated with daily physical activity than quadriceps strength and power in COPD: an international multicentre cross-sectional trial

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Erik Frykholm ◽  
Sarah Gephine ◽  
Didier Saey ◽  
Arthur Lemson ◽  
Peter Klijn ◽  
...  
Keyword(s):  
Physical Activity ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Regression Models ◽  
Sedentary Time ◽  
Quadriceps Strength ◽  
Linear Regression Models ◽  
Cross Sectional ◽  
Quadriceps Function ◽  
Multiple Linear Regression Models

AbstractKnowledge about modifiable determinants of daily physical activity (PA) in patients with chronic obstructive pulmonary disease (COPD) is crucial to design effective PA interventions. The present study aimed to determine the contribution of quadriceps strength, power and endurance to daily PA in COPD. Additionally, for quadriceps endurance, we also aimed to determine to what extent the association varies according to the mode of movement (isotonic, isometric, or isokinetic). Using a multicentre cross-sectional trial design we determined the contribution of quadriceps function to daily PA (steps, sedentary time and time spent doing moderate-to-very-vigorous physical activity [MVPA]) using bivariate and partial Pearson correlation analysis (r) and multiple linear regression models (ΔR2). Pre-determined controlling factors were sex, age, body mass index (BMI), COPD-assessment test, forced expiratory volume in one second in percent of the predicted value (FEV1pred), and distance walked on the 6-minute walk test. Eighty-one patients with COPD (mean ± SD: age 67 ± 8 years, FEV1pred 57 ± 19%, daily steps 4968 ± 3319, daily sedentary time 1016 ± 305 min, and MVPA time 83 ± 45 min) were included. Small to moderate bivariate correlations (r = .225 to .452, p < .05) were found between quadriceps function and measures of PA. The best multiple linear regression models explained 38–49% of the variance in the data. Isotonic endurance was the only muscle contributor that improved all PA models; daily steps (ΔR2 = .04 [relative improvement 13%] p = .026), daily sedentary time (ΔR2 = .07 [23%], p = .005) and MVPA-minutes (ΔR2 = .08 [20%], p = .001). Isotonic endurance was also independently associated with most PA variables, even when controlling for strength, power or isometric-isokinetic endurance properties of the muscle (r = .246 to .384, p < .05). In contrast, neither strength, power, isometric-or isokinetic endurance properties of the muscle was independently associated with PA measures when controlling for isotonic endurance (r = .037 to .219, p > .05). To conclude, strength, power, and endurance properties of the quadriceps were low to moderately associated with PA in patients with COPD. Isotonic quadriceps endurance was the only quadriceps property that was independently associated with the different measures of PA after controlling for a basic set of known determinants of PA, quadriceps strength or power, or isometric or isokinetic quadriceps endurance. Future longitudinal studies should investigate its potential as a modifiable determinant of PA.

scholarly journals FAKTOR-FAKTOR YANG MEMPENGARUHI PRODUKSI SUSU SAPI PERAH KELOMPOK TANI TERNAK SAPI PERAH (KTTSP) KANIA, KABUPATEN BOGOR

2019 ◽  
Vol 11 (2) ◽  
pp. 148-160
Author(s):  
Adam Adinegoro ◽  
Edmon Daris ◽  
Zulmanery Z
Keyword(s):  
Linear Regression ◽  
Dairy Cattle ◽  
Multiple Linear Regression ◽  
Milk Production ◽  
Regression Models ◽  
Linear Regression Models ◽  
Factors Affecting ◽  
Cattle Ranchers ◽  
Multiple Linear Regression Models

The purpose of this study are: (1) to identify and to analyze the factors that influence milk production of dairy cattles, and (2) to determine the elasticity of milk production. This research was conducted at the Dairy cattle group KANIA, Bogor. Data were obtained from interviews and questionnaires with cattle ranchers. Multiple linear regression models and elasticity calculations were employed to analyze the data with the Excel 2007 and software for Statistical Product and Service Solution (SPSS) version 16. Results of the analysis revealed that the factors affecting milk production is labor, forages, and feed concentrates. The result of the calculation of the elasticity indicated that all production variables are elastic variables.

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