Iron Filings as PRB Media Regression Analysis of Remediation of Chromium-Contaminated Groundwater

2013 ◽  
Vol 864-867 ◽  
pp. 1178-1182
Author(s):  
Zhi Yong Han ◽  
Xiao Bin Wei ◽  
Wang Bing Du
Keyword(s):  
Regression Analysis ◽  
Regression Model ◽  
Flow Velocity ◽  
Removal Rate ◽  
Contaminated Groundwater ◽  
Analysis Software ◽  
Initial Concentration ◽  
Data Regression ◽  
Cubic Polynomial ◽  
Iron Filing

Regarding the iron filing as PRB padding media, this experiment researched the repair effect of iron filing to the groundwater polluted by Cr on condition that different initial concentration of Cr6+ and different flow velocity, and then with the help of the data regression analysis software SPSS、1stOpt、Excel etc, it studied the regression analysis of Cr6+ removal rate with the change of initial concentration and the flow velocity. The results indicated that: under the certain flow velocity, cubic polynomial is the best regression model of Cr6+ removal rate with the change of initial concentration;under the certain initial Cr6+ concentration, the change of Cr6+ removal rate with flow velocity can be fitted in the regression model of unitary linear well, and the decision coefficient R2of it can be up to above 0.9.

Iron Filings as PRB Media Binary Regression Analysis of Remediation of Chromium-Contaminated Groundwater

2013 ◽  
Vol 838-841 ◽  
pp. 2673-2676
Author(s):  
Zhi Yong Han ◽  
Wang Bing Du ◽  
Xiao Bin Wei ◽  
Li Li Ma ◽  
Bo Qu ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Regression Model ◽  
Flow Velocity ◽  
Removal Rate ◽  
Initial Concentration ◽  
Data Regression ◽  
Binary Regression Analysis ◽  
Iron Filing ◽  
Change Trend ◽  
The One

Regarding the iron filing as PRB padding media, this experiment researched the repair effect of iron filing to the groundwater polluted by Cr+6on condition that different initial concentration of Cr6+and different flow velocity, and then with the help of the data regression analysis software SPSS1stOptExcel etc, it studied the regression analysis of Cr6+removal rate with binary linear and binary nonlinear of the initial concentration and the flow velocity. The results indicated that: the change process of Cr6+removal rate with initial concentration and flow velocity can adopt regression model analysis of binary linear and binary nonlinear, and in this test, the decision coefficient of the regression model of binary linear (0.825) was smaller than the decision coefficient R2of binary nonlinear (0.955), which illustrated, comparing with the regression model of binary linear, the one of binary nonlinear z=(p1+p3*x+p5*Ln (y)+p7*x^2+p9*(Ln (y))^2+p11*x *Ln (y))/(1+p2*x+p4*Ln (y)+p6*x^2+p8*(Ln (y))^2+p10*x*Ln (y)) can simulate the change trend of Cr6+removal rate with initial concentration and flow velocity better.

A Review of Regression Models in Machine Learning

2021 ◽  
Vol 2 (2) ◽  
pp. 40-47
Author(s):  
Sunil Kumar ◽  
Vaibhav Bhatnagar
Keyword(s):  
Machine Learning ◽  
Regression Analysis ◽  
Regression Model ◽  
Regression Models ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Analysis Model ◽  
Data Set ◽  
Data Regression ◽  
Different Types

Machine learning is one of the active fields and technologies to realize artificial intelligence (AI). The complexity of machine learning algorithms creates problems to predict the best algorithm. There are many complex algorithms in machine learning (ML) to determine the appropriate method for finding regression trends, thereby establishing the correlation association in the middle of variables is very difficult, we are going to review different types of regressions used in Machine Learning. There are mainly six types of regression model Linear, Logistic, Polynomial, Ridge, Bayesian Linear and Lasso. This paper overview the above-mentioned regression model and will try to find the comparison and suitability for Machine Learning. A data analysis prerequisite to launch an association amongst the innumerable considerations in a data set, association is essential for forecast and exploration of data. Regression Analysis is such a procedure to establish association among the datasets. The effort on this paper predominantly emphases on the diverse regression analysis model, how they binning to custom in context of different data sets in machine learning. Selection the accurate model for exploration is the most challenging assignment and hence, these models considered thoroughly in this study. In machine learning by these models in the perfect way and thru accurate data set, data exploration and forecast can provide the maximum exact outcomes.

Determinants Of Capital Structure For Multinational And Domestic Companies In India: Panel Data Regression Model

2019 ◽  
Vol 118 (7) ◽  
pp. 147-154
Author(s):  
K. Maheswari ◽  
Dr. J. Gayathri ◽  
Dr. M. Babu ◽  
Dr.G. Indhumathi
Keyword(s):  
Panel Data ◽  
Capital Structure ◽  
Regression Model ◽  
Business Risk ◽  
Return On Assets ◽  
Panel Data Regression ◽  
Data Regression ◽  
Return On Capital ◽  
Domestic Companies ◽  
The Capital Structure

The capital structure refers to the components of capital needed to establish and expand its business activities. The study was made with an objective to examine the determinants of capital structure of multinational and domestic companies listed in S&P BSE automobile sector. The study concluded that there is significant impact on capital structure determinants such as size, business risk, non debt shield tax, return on assets, tangibility, profit, return on capital employed and liquidity on the capital structure of multinational and domestic companies of Indian Automobile Sector.  

scholarly journals Effect of permanent magnetic field on scale inhibition property of circulating water

2017 ◽  
Vol 76 (8) ◽  
pp. 1981-1991 ◽  
Author(s):  
Lili Jiang ◽  
Xiayan Yao ◽  
Haitao Yu ◽  
Xingang Hou ◽  
Zongshu Zou ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Hydrogen Bonds ◽  
Flow Velocity ◽  
Magnetic Field Intensity ◽  
Water Molecules ◽  
Relative Variation ◽  
Initial Concentration ◽  
Circulating Water ◽  
Scale Inhibition ◽  
Inhibition Property

Effect of a permanent magnet field on the scale inhibition property of circulating water was investigated. Orthogonal experiments of L16(45) were performed and analyzed using the range analysis method. The operating parameters included magnetic field intensity, initial concentration of Ca2+ and Mg2+, magnetic treatment time, temperature, and flow velocity. Scale inhibition rate, hardness, relative variation in the proportion of free water molecules, electrical conductivity, and relative variation of molecular energy were chosen as the objectives. In addition, the morphology and the composition of CaCO3 and MgCO3 scale were studied by X-ray diffraction analysis. The optimal conditions were initial concentration of 900 mg/L, magnetic field intensity of 0.5 T, temperature of 303 K, time of 54 h and flow velocity of 0.17 m/s. The nuclear magnetic resonance results demonstrated that the number of hydrogen bonds increased between water molecules and hydrated ions. The magnetic field can promote the increase in the number of hydrogen bonds, which can inhibit the formation of calcium and magnesium carbonate precipitation. Moreover, the ratio of calcite, aragonite and vaterite will be changed at different magnetic field intensities, and the aragonite ratio will reach the peak at the optimum conditions.

Regression Analysis on Influences of Hem Parameters on Flare Sagging of Semi-Circular Skirt

2011 ◽  
Vol 331 ◽  
pp. 594-598
Author(s):  
Hong Ni
Keyword(s):  
Regression Analysis ◽  
Statistical Analysis ◽  
Measured Data ◽  
Silk Fabric ◽  
Linear Regression Equation ◽  
Analysis Software ◽  
Wool Fabrics ◽  
Object Of Study ◽  
The Relationship ◽  
Statistical Analysis Software

Select 45 pieces of silk, cotton and wool fabrics in total, with semi-circular skirt modeling as object of study, investigate the relationship between flare sagging in straight & inclined grains of skirt pieces and fabric types, analyze the influence of hem parameters on flare sagging of semi-circular skirt and obtain multiple linear regression equation by disposing the measured data with SPSS statistical analysis software. The study result shows that the skirt hem of silk fabric is sagging more significantly than that of cotton and wool fabrics and there is a multi-linear relationship between semi-circular flare sagging and parameters.The research of this subject has both theoretical value and practical value.

scholarly journals The double-edged sword of corporatisation in the hospital sector: evidence from Indonesia

2016 ◽  
Vol 12 (1) ◽  
pp. 61-80 ◽  
Author(s):  
Asri Maharani ◽  
Gindo Tampubolon
Keyword(s):  
Regression Analysis ◽  
Panel Data ◽  
Financial Performance ◽  
System Performance ◽  
Hospital Performance ◽  
Public Hospitals ◽  
Health System Performance ◽  
The Past ◽  
Panel Data Regression ◽  
Data Regression

AbstractHoping to improve their health system performance, many countries have corporatised their hospitals in the past 20 years. What this means for hospital performance remains as yet largely unknown. This study looks into the association of corporatisation and hospital performance in Indonesia. We apply panel data regression analysis to survey data on 54 public hospitals in East Java province. Our analysis suggests that corporatisation is associated with higher hospital income and expenditure, but fails to improve efficiency and equity. These findings suggest that hospital corporatisation policy in Indonesia should increase emphasis on efficiency and equity rather than on financial performance alone.

Rainfall data regression model using fuzzy set logic

2016 ◽  
pp. 1060-1064
Author(s):  
Christos Tzimopoulos ◽  
Chris Evangelides ◽  
Basilios Papadopoulos
Keyword(s):  
Regression Model ◽  
Fuzzy Set ◽  
Rainfall Data ◽  
Data Regression

scholarly journals Prognosis of chronic lymphocytic leukemia: a multivariate regression analysis of 325 untreated patients

Blood ◽  
1987 ◽  
Vol 69 (3) ◽  
pp. 929-936 ◽  
Author(s):  
JS Lee ◽  
DO Dixon ◽  
HM Kantarjian ◽  
MJ Keating ◽  
M Talpaz
Keyword(s):  
Alkaline Phosphatase ◽  
Uric Acid ◽  
Regression Analysis ◽  
Lactate Dehydrogenase ◽  
Chronic Lymphocytic Leukemia ◽  
Regression Model ◽  
Multivariate Regression ◽  
Risk Groups ◽  
Multivariate Regression Analysis ◽  
Lymphocytic Leukemia

Three hundred twenty-five previously untreated patients with chronic lymphocytic leukemia were analyzed to identify significant prognostic factors for survival. Univariate analysis identified the following characteristics associated with survival: (1) clinical characteristics: age, race, sex, performance status, lymphadenopathy, and hepatosplenomegaly; (2) hematologic parameters: WBC count, absolute lymphocyte and granulocyte counts, hemoglobin level, and platelet count; and (3) biochemical parameters: serum albumin, calcium, uric acid, lactate dehydrogenase, alkaline phosphatase, BUN, and creatinine. Multivariate regression analysis in a randomly selected training subset of 217 patients demonstrated that the combination of uric acid, alkaline phosphatase, lactate dehydrogenase, external lymphadenopathy, and age had the strongest predictive relation to survival time. The resulting model was validated in the remaining independent subset of 108 patients and led to classification of patients into low, intermediate, and high-risk groups with five-year survival rates of 75%, 59%, and 14%, respectively, and with distinctively different annual mortality rates (P less than .01). Both the regression model and Rai staging were highly effective in identifying risk groups among the entire patient population (P less than 0.001). Overall the regression model was superior to Rai staging in defining prognostic risk groups. In addition, it was able to separate patients into significantly different risk categories within each Rai stage, thus improving on the prognostic prediction of individual patients with chronic lymphocytic leukemia.

scholarly journals Estimation of Regression Parameters from Noise Multiplied Data

2013 ◽  
Vol 4 (2) ◽  
Author(s):  
Yan-Xia Lin ◽  
Phillip Wise
Keyword(s):  
Regression Analysis ◽  
Regression Model ◽  
Real Life ◽  
Simulation Studies ◽  
Independent Variables ◽  
Multiplicative Noises ◽  
Life Data ◽  
Regression Parameters ◽  
Data Application ◽  
Real Life Data

This paper considers the scenario that all data entries in a confidentialised unit record file were masked by multiplicative noises, regardless of whether unit records are sensitive or not and regardless of whether the masked variables are dependent or independent variables in the underlying regression analysis. A technique is introduced in this paper to show how to estimate parameters in a regression model, which is originally fitted by unmasked data, based on masked data. Several simulation studies and a real-life data application are presented.

scholarly journals Effect of Default on Profitability in Kenyan Listed Companies

2020 ◽  
Vol 9 (4) ◽  
pp. 01-10
Author(s):  
James N Ndegwa
Keyword(s):  
Regression Analysis ◽  
Financial Institutions ◽  
Listed Companies ◽  
Short Term ◽  
Listed Firms ◽  
Short Notice ◽  
Panel Data Regression ◽  
Data Regression ◽  
Firm Profitability ◽  
The Relationship

The study investigated the whether the default measures of liquidity and solvency are associated and whether default measures are related to firm profitability. A total of 41 firms were selected to be in the study sample out of 46 non-financial listed firms in the Nairobi Securities Exchange during years 2013 to 2017 and panel data regression analysis was employed. The findings revealed that liquidity and solvency are significantly and negatively associated while the default measures lacked a significant relationship with profitability in Kenyan listed companies. The findings implied that there is no need for firms to focus too much on the relationship between default and profitability including invest heavily in liquidity in order to meet short term obligations as nowadays it is possible for firms to either convert non-cash assets quickly or borrow on short notice from financial institutions in case of an urgent need to meet liquidity shortages. These findings are consistent with the shitability theory.

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