multiple linear regression (MLR) modeling has been successfully used to predict how water chemistry variables influence the toxicity of cationic metals to aquatic organisms, but no MLR model exists for vanadium (V). R...
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multiple linear regression (MLR) modeling has been successfully used to predict how water chemistry variables influence the toxicity of cationic metals to aquatic organisms, but no MLR model exists for vanadium (V). Recent research has indicated that an increase in pH (from 6 to 9), or high concentrations of sodium (473 mg Na+/L), increase V toxicity toDaphnia pulex. In contrast, increases in alkalinity (>100 mg as CaCO3) and sulfate (>100 mg SO42-/L) reduce V toxicity. How these variables influence V toxicity toOncorhynchus mykiss(rainbow trout) was still unknown. Our results show that increasing pH from 6.2 to 8.9 tended to decrease the 96-h median lethal concentration (LC50) for V toxicity toO. mykissby 9.6 mg V/L. An alkalinity increase from 71 to 330 mg/L as CaCO(3)tended to increase the 96-h LC50 by 3.3 mg V/L, whereas when SO(4)(2-)rose from 150 to 250 mg/L, the LC50 significantly increased by 0.3 mg V/L followed by a significant decrease of 1 mg V/L when SO(4)(2-)was >250 mg/L. Sodium (between 100 and 336 mg/L) showed no effect on V toxicity toO. mykiss. The toxicity patterns forO. mykisswere similar to those observed forD. pulex, except for that of SO42-, potentially indicating different mechanisms of V uptake or regulation in the 2 species. The LC50s and associated water chemistry were combined to develop an MLR model forO. mykissandD. pulex. Alkalinity and pH modified V toxicity to both species, whereas SO(4)(2-)influenced V toxicity toD. pulex. Overall, MLR models should be considered for creating new local benchmarks or water quality guidelines for *** Toxicol Chem2020;00:1-9. (c) 2020 SETAC
Fracture identification and evaluation requires data from various resources, such as image logs, core samples, seismic data, and conventional well logs for a meaningful interpretation. However, several wells have some...
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Fracture identification and evaluation requires data from various resources, such as image logs, core samples, seismic data, and conventional well logs for a meaningful interpretation. However, several wells have some missing data;for instance, expensive cost run for image logs, cost concern for core samples, and occasionally unsuccessful core retrieving process. Thus, a majority of the current research is focused on predicting fracture based on conventional well log data. Interpreting fractures information is very important especially to develop reservoir model and to plan for drilling and field development. This study employed statistical methods such as multiple linear regression (MLR), principal component analysis (PCA), and gene expression programming (GEP) to predict fracture density from conventional well log data. This study explored three wells from a basement metamorphic rock with ten conventional logs of gamma rays, thorium, potassium, uranium, deep resistivity, flushed zone resistivity, bulk density, neutron porosity, sonic porosity, and photoelectric effect. Four different methods were used to predict the fracture density, and the results show that predicting fracture density is possible using MLR, PCA, and GEP. However, GEP predicted the best fracture density with R-2 > 0.86 for all investigated wells, although it had limited use in predicting fracture density. All methods used highlighted that flushed zone resistivity and uranium content are the two most significant well log parameters to predict fracture density. GEP was efficient for use in metamorphic rocks as it works well for conventional well log data as the data is nonlinear, and GEP uses nonlinear algorithms.
We consider multiple linear regression models under nonnormality. We derive modified maximum likelihood estimators (MMLEs) of the parameters and show that they are efficient and robust. We show that the least squares ...
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We consider multiple linear regression models under nonnormality. We derive modified maximum likelihood estimators (MMLEs) of the parameters and show that they are efficient and robust. We show that the least squares esimators are considerably less efficient. We compare the efficiencies of the MMLEs and the M estimators for symmetric distributions and show that, for plausible alternatives to an assumed distribution, the former are more efficient. We provide real-life examples.
Compositional data, containing relative information, occur regularly in many disciplines and practical situations. Multivariate statistics methods including regression analysis have been adopted to model compositional...
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Compositional data, containing relative information, occur regularly in many disciplines and practical situations. Multivariate statistics methods including regression analysis have been adopted to model compositional data, but the existing research is still scattered and fragmented. This paper contributes to modeling the linearregression relationship for compositional data as both dependent and independent variables. First, some operations in Simplex space, such as the perturbation operation, the power transformation, and the inner product, are defined for compositional-data vectors. The regression models are then built by the original compositional data and transformed data, respectively, after the introduction of the Isometric Logratio Transformation (ilr). By theoretical inference, it turns out that the two models are equivalent in essence using the ordinary least squares (OLS) method. Two measures for testing goodness of fit, i.e., the observed squared correlation coefficient R-2 and the cross validated squared correlation coefficient Q(2), are also proposed to evaluate the regression models. Besides, the estimated regression parameters are explained to indicate the notion of relative elasticity. An empirical analysis finally illustrates the usefulness of the multiple linear regression models for compositional-data variables. (C) 2013 Elsevier B.V. All rights reserved.
A multiple linear regression model called MLR-3 is used for predicting the experimental n-octanol/water partition coefficient (log P-N) of 22 N-sulfonamides proposed by the organizers of the SAMPL7 blind challenge. Th...
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A multiple linear regression model called MLR-3 is used for predicting the experimental n-octanol/water partition coefficient (log P-N) of 22 N-sulfonamides proposed by the organizers of the SAMPL7 blind challenge. The MLR-3 method was trained with 82 molecules including drug-like sulfonamides and small organic molecules, which resembled the main functional groups present in the challenge dataset. Our model, submitted as "TFE-MLR", presented a root-mean-square error of 0.58 and mean absolute error of 0.41 in log P units, accomplishing the highest accuracy, among empirical methods and also in all submissions based on the ranked ones. Overall, the results support the appropriateness of multiple linear regression approach MLR-3 for computing the n-octanol/water partition coefficient in sulfonamide-bearing compounds. In this context, the outstanding performance of empirical methodologies, where 75% of the ranked submissions achieved root-mean-square errors < 1 log P units, support the suitability of these strategies for obtaining accurate and fast predictions of physicochemical properties as partition coefficients of bioorganic compounds.
With the growth and ageing of the stock of existing structures, structural assessment and retrofitting are fast acquiring a significant role in the construction industry. The benefits of upgrading existing reinforced ...
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With the growth and ageing of the stock of existing structures, structural assessment and retrofitting are fast acquiring a significant role in the construction industry. The benefits of upgrading existing reinforced concrete (RC) structures or extending their service life and of ensuring greater durability in designs for de novo construction have led to a need to include deterioration as a factor in structural safety models. Bond between reinforcing steel and concrete is of cardinal importance in this respect. The present paper proposes a unified formulation for assessing bond strength in corroded and non-corroded steel bars, and an associated model to accommodate the effect of transverse pressure where appropriate. The formulation is the result of applying multiple linear regression analysis to a database built from the findings of over 650 bond tests on corroded and non-corroded reinforcing steel reported in the literature. The data collected include a wide range of variables affecting bond strength, such as bar diameter, concrete compressive strength, concrete cover, anchorage length, confinement ratio and cross-sectional loss. A number of statistical criteria are used to compare the proposed formulation to the other bond strength assessment models, including the fib Model Code 2010 proposal for corroded steel bars. Further to the statistical tests conducted, the model proposed can be usefully applied to assess the structural safety of corroded RC members.
Presently population aging issue becomes increasingly serious, old people occupy more and more proportions in Chinese population. To some extent, old people living quality represents a country’s civil living standard...
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This paper develops an analytical framework to explain the lnternet interconnection settlement issues. The paper shows that multiple linear regression can be used in assessing the network value of lnternet Backbone Pr...
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This paper develops an analytical framework to explain the lnternet interconnection settlement issues. The paper shows that multiple linear regression can be used in assessing the network value of lnternet Backbone Providers （ IBPs）. By using the exchange rate of each network, we can define a rate of network value, which reflects the contribution of each network to interconnection and the interconnected network resource usage by each of the network.
Many scholars use the diffusion theory and the Rouse equation to describe the vertical distribution of sediment in the nearshore waters of estuaries. This article analyzes several assumptions for the establishment of ...
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Many scholars use the diffusion theory and the Rouse equation to describe the vertical distribution of sediment in the nearshore waters of estuaries. This article analyzes several assumptions for the establishment of the equation to test how well it can be applied in the marine nearshore zone. Six independent variables were selected based on the traditional Rouse distribution, the linear form of the Rouse equation, and the characteristics of water transported sediment. Then, they were analyzed for their impacts on sediment concentration through multiple linear regression. According to the results, the concentration value at the reference point had the most significant impact on the calculation of the sediment concentration. In comparison, the sediment concentration calculated based on the relative water depth only had a correlation of 0.3 with the measured values. Among the six independent variables, flow velocity's sensitivity (especially that of the variable lnu) was much lower than the sensitivity of the reference point concentration. Thus, flow velocity was decided as a non-sensitive factor. After excluding it from the independent variables, the equation contained five independent variables. Then we used the equation to calculate the sediment concentration at each point. The correlation coefficients between the results and the measured values reached around 0.8. This proves that the method adopted by this paper can reflect the vertical concentration distribution of fine particle sediment in nearshore waters under complex dynamic conditions.
Waste paper is recovered and bleached to produce recycled newsprints and magazines. It is composed of a fibre mixture from different wood pulping processes. Each type of fibre shows a different reactivity towards blea...
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Waste paper is recovered and bleached to produce recycled newsprints and magazines. It is composed of a fibre mixture from different wood pulping processes. Each type of fibre shows a different reactivity towards bleaching. Consequently, if the composition of waste paper changes over time, the actual industrial bleaching process may no longer be suitable to achieve the intended brightness. This study aims to develop a multiple linear regression that correlates brightness and fibre composition to determine in advance whether a waste paper stream can achieve the intended brightness. Several samples of four of the most representative fibre types were bleached under specific laboratory conditions, and the resulting brightness was used to develop the regression. The resulting model is valid and consistent when the amount of bleached fibre chemically pulped type in the mixed fibre stream does not exceed 80%. Waste samples with a known fibre composition were then bleached to verify the model. The measured brightness followed the same trend predicted by the regression but was lower at a constant value. The use of a correction factor allowed for a good fit. The cause of this discrepancy could be the differences between the reference fibre mixtures and waste paper pulp not included in the model (e.g. contaminants or collapsed fibres). This work is a first step to develop a simple statistical tool to estimate the brightness of waste paper pulp, despite some limitations. [GRAPHICS] .
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