scholarly journals Background levels of some trace elements in weathered soils from the Brazilian Northern region

2010 ◽  
Vol 67 (1) ◽  
pp. 53-59 ◽  
Author(s):  
Francisco Souza Fadigas ◽  
Nelson Moura Brasil do Amaral Sobrinho ◽  
Lucia Helena Cunha dos Anjos ◽  
Nelson Mazur
Keyword(s):  
Heavy Metals ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Linear Regression Analysis ◽  
Clay Mineralogy ◽  
Inverse Correlation ◽  
Northern Region ◽  
Soil Parameters ◽  
General Terms ◽  
Metal Levels

Soils formed from the Barreiras Group sediments, located mainly along the coast of Brazil Northern and Northeastern regions, generally present low concentrations of iron oxides and total organic carbon, high quantities of quartz in the sand fraction, and kaolinitic clay mineralogy. The objective of the present study was to quantify the pseudo total concentrations of Cd, Co, Cu, Cr, Mn, Ni, Zn and Fe in Xhantic Udox and Xhantic Udult soils derived from these sediments. The reference sites were covered by native vegetation and located in the States of Pará and Amapá, Brazil. Multiple linear regression analysis was applied to determine correlations between soil parameters and the levels of these metals. The best correlation was obtained between Fe, Mn, clay, and silt contents, and Cd, Co, Cu, Zn, Cr, Ni. A correlation between pH and these metal levels was not found. Clay and sand contents showed a negative inverse correlation with the metal levels,of same magnitude but with a different sign; this was the reason for excluding one of the parameters in the regression model. In general, the contents of the elements were lower than those found in soils formed from other parent materials. The Mn content was included in the model of multiple linear regression for Cd and Co, due to its association with these last metals. Silt level showed to have a significant influence in the equations for Cr and Co, which is attributed to the presence of clay minerals and Fe and Mn oxides in ferruginous and clay aggregates of silt size. The equations obtained in this paper, are useful to predict, in general terms, the amounts of those heavy metals in an unknown soil sample, if the soil material were not contaminated or affected by land usage. Thus, they may be applied to evaluate soil contamination by the studied heavy metals.

PENGARUH FAKTOR-FAKTOR INTERNAL PERBANKAN TERHADAP NON PERFORMING FINANCING PADA BANK UMUM SYARIAH

2019 ◽  
Vol 6 (2) ◽  
pp. 115-130
Author(s):  
Eka Ambara Harci Putranta ◽  
Lilik Ambarwati
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Capital Adequacy ◽  
Annual Reports ◽  
Regression Methods ◽  
Capital Adequacy Ratio ◽  
Negative Effect ◽  
Independent Variable

The study aims to analyze the influence of internal banking factors in the form of: Capital Adequency Ratio (CAR), Financing to Deposit Ratio (FDR) and Total Assets (TA) to Non Performing Financing at Sharia Banks. This research method used multiple linear regression analysis with the help of SPSS 16.00 software which is used to see the influence between the independent variables in the form of Capital Adequacy Ratio (CAR), Financing to Deposit Ratio (FDR) and Total Assets (TA) to Non Performing Financing. The sample of this study was 3 Islamic Commercial Banks, so there were 36 annual reports obtained through purposive sampling, then analyzed using multiple linear regression methods. The results showed that based on the F Test, the independent variable had an effect on the NPF, indicated by the F value of 17,016 and significance of 0,000, overall the independent variable was able to explain the effect of 69.60%. While based on the partial t test, showed that CAR has a significant negative effect, Total assets have a significant positive effect with a significance value below 0.05 (5%). Meanwhile FDR does not affect NPF.

ANALISIS PENGARUH INFLASI, KURS DAN IHSG TERHADAP RETURN REKSA DANA SAHAM PT NIKKO SECURITIES INDONESIA PERIODE 2014-2017

2019 ◽  
Vol 2 (2) ◽  
pp. 103
Author(s):  
Muhammad Rois Rois ◽  
Manarotul Fatati Fatati ◽  
Winda Ihda Magfiroh
Keyword(s):  
Regression Analysis ◽  
Exchange Rate ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Stock Price ◽  
Price Index ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Equity Fund ◽  
Stock Price Index

This study aims to determine the effect of Inflation, Exchange Rate and Composite Stock Price Index (IHSG) to Return of PT Nikko Securities Indonesia Stock Fund period 2014-2017. The study used secondary data obtained through documentation in the form of PT Nikko Securities Indonesia Monthly Net Asset (NAB) report. Data analysis is used with quantitative analysis, multiple linear regression analysis using eviews 9. Population and sample in this research are PT Nikko Securities Indonesia. The result of multiple linear regression analysis was the coefficient of determination (R2) showed the result of 0.123819 or 12%. This means that the Inflation, Exchange Rate and Composite Stock Price Index (IHSG) variables can influence the return of PT Nikko Securities Indonesia's equity fund of 12% and 88% is influenced by other variables. Based on the result of the research, the variables of inflation and exchange rate have a negative and significant effect toward the return of PT Nikko Securities Indonesia's equity fund. While the variable of Composite Stock Price Index (IHSG) has a negative but not significant effect toward Return of Equity Fund of PT Nikko Securities Indonesia

An Empirical Study of Investor Attitudinal Factors Influencing Herd Behavior: Evidence from Pakistan Stock Exchange

2019 ◽  
Vol 12 (1) ◽  
pp. 1-11
Author(s):  
Faid Gul ◽  
Karamat Khan
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Psychological Factors ◽  
Stock Exchange ◽  
Linear Regression Analysis ◽  
Primary Data ◽  
Herd Behavior ◽  
Individual Investor ◽  
Decision Accuracy ◽  
Attitudinal Factors

Behavioral Finance is an evolving field that studies how psychological factors affect decision making under uncertainty. Herding behavior is one of the psychological factors that instigate investor to mimic the actions of other investors in the market rather than using his personal assessments. This study seeks to find the influence of certain attitudinal factors namely, decision conformity, hasty decision, mood, decision accuracy, and overconfidence, on the individual investor tendency to embrace herd behavior. Primary data for the study are collected using structured questionnaires from a sample of 194 investors who are trading at Islamabad and Lahore branches of Pakistan Stock Exchange. Multiple linear regression analysis is used to test the hypotheses of this study. Findings of this study provide evidence that attitudinal factors have a significant influence on investor’s tendency to take on herd behavior. It is concluded from the results of multiple linear regression that decision conformity, mood, and decision accuracy have a significant impact on individual investor tendency to adopt herd behavior. However, investor hasty decision and overconfidence is insignificant predictors of herd behavior. Keywords: Decision conformity, Hasty decision, Mood, Decision accuracy, Overconfidence, Herd behavior

PENGARUH LOKASI, KUALITAS PRODUK TABUNGAN, KUALITAS PELAYANAN TERHADAP KEPUASAN NASABAH DALAM MENABUNG DI BMT ADZKIYAH KHIDMATUL UMMAH DI KOTA METRO

2019 ◽  
Vol 3 (2) ◽  
pp. 439
Author(s):  
Suwarto Suwarto ◽  
Risa Anggraini
Keyword(s):  
Linear Regression ◽  
Customer Satisfaction ◽  
Service Quality ◽  
Multiple Linear Regression ◽  
Linear Regression Analysis ◽  
Requirements Analysis ◽  
Primary Data ◽  
Homogeneity Test ◽  
Linearity Test

This research is motivated by customer satisfaction which is a customer action to save. The purpose of this study was to determine the ef ect of location, quality of savings products, service quality on customer satisfaction. In this study using primary data collected by explanatory research methods and sample collection techniques in the form of accidental sampling of BMT customers Adzkiyah Khidmatul Ummah using a questionnaire with a likert skla in BMT Adzkiyah Khidmatul Ummah in Metro City. Testing the instrument requirements used include validity, reliability testing. Requirements analysis using normality test, linearity test, homogeneity test. And analysis tools using multiple linear regression with partial test (t test), simultaneous test (f test), coef icient of determination test (R2). As testing requirements analysis and hypothesis testing. Based on the results of research using multiple linear regression analysis obtained location influences customer satisfaction, the quality of savings products does not af ect customer satisfaction, and service quality influences customer satisfaction.

Predictive Factors for Hamstring Autograft Diameter in Anterior Cruciate Ligament Reconstruction

2019 ◽  
Author(s):  
Willem M.P. Heijboer ◽  
Mathijs A.M. Suijkerbuijk ◽  
Belle L. van Meer ◽  
Eric W.P. Bakker ◽  
Duncan E. Meuffels
Keyword(s):  
Anterior Cruciate Ligament ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Acl Reconstruction ◽  
Cruciate Ligament ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Model ◽  
Gracilis Tendon ◽  
Thigh Circumference ◽  
Anterior Cruciate

AbstractMultiple studies found hamstring tendon (HT) autograft diameter to be a risk factor for anterior cruciate ligament (ACL) reconstruction failure. This study aimed to determine which preoperative measurements are associated with HT autograft diameter in ACL reconstruction by directly comparing patient characteristics and cross-sectional area (CSA) measurement of the semitendinosus and gracilis tendon on magnetic resonance imaging (MRI). Fifty-three patients with a primary ACL reconstruction with a four-stranded HT autograft were included in this study. Preoperatively we recorded length, weight, thigh circumference, gender, age, preinjury Tegner activity score, and CSA of the semitendinosus and gracilis tendon on MRI. Total CSA on MRI, weight, height, gender, and thigh circumference were all significantly correlated with HT autograft diameter (p < 0.05). A multiple linear regression model with CSA measurement of the HTs on MRI, weight, and height showed the most explained variance of HT autograft diameter (adjusted R 2 = 44%). A regression equation was derived for an estimation of the expected intraoperative HT autograft diameter: 1.2508 + 0.0400 × total CSA (mm2) + 0.0100 × weight (kg) + 0.0296 × length (cm). The Bland and Altman analysis indicated a 95% limit of agreement of ± 1.14 mm and an error correlation of r = 0.47. Smaller CSA of the semitendinosus and gracilis tendon on MRI, shorter stature, lower weight, smaller thigh circumference, and female gender are associated with a smaller four-stranded HT autograft diameter in ACL reconstruction. Multiple linear regression analysis indicated that the combination of MRI CSA measurement, weight, and height is the strongest predictor.

QSAR Docking on Azoles as inhibitors of Notum carboxylesterase

2021 ◽  
Vol 15 ◽  
Author(s):  
Amuthalakshmi Sivaperuman ◽  
Ramalakshmi Natarajan ◽  
Manimegalai P ◽  
Arunkumar Subramani ◽  
Puratchikody Ayarivan
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Wnt Signaling ◽  
Multiple Linear Regression ◽  
Inhibitory Activity ◽  
Linear Regression Analysis ◽  
External Validation ◽  
Negative Regulator ◽  
Qsar Analysis ◽  
Wnt Proteins

Background: Carboxylesterase Notum is a negative regulator of Wnt signaling. Notum carboxylesterase is a carboxylic ester hydrolase enzyme that functions as a negative feedback regulator of Wnt proteins by depalmitoylation reaction. It is of great importance to understand the pathway of Wnt regulation because, conversely, misregulation of Wnt signaling is a telltale sign of cancer and other degenerative diseases. The Wnt inhibition is important in the control of colorectal cancer. Objective: In the present study, we carried out a QSAR analysis of a series of reported compounds with carboxylesterase Notum inhibitory activity using multiple regression analysis. A series of 83 compound datasets of pyrrole derivatives with carboxy Notum inhibitory values were taken from the reported literature. Methods: The study was performed by conducting multiple linear regression analysis followed by validation of the model. The multiple linear regression (MLR) models with the highest coefficients of correlation (R2) and explained variance in leave-one-out (Q2 LOO) prediction and leave-many-out (Q2 LMO) were selected for the whole dataset. The developed models were subjected to internal and external validation. The reliability of the predicted model was checked by plotting the Williams plot. The docking methodology was performed using Autodock 4 for the designed compounds to study the interaction between the ligand and the receptor. Results: The best model generated exhibited an r2 value of 0.7413, Q2LOO =0.6379, Q2LMO =0.6368. Novel compounds of phenyl pyrrolidine were designed based on generated QSAR equations. The carboxylesterase Notum inhibitory activity was predicted using the QSAR equations. The docking studies were carried out for designed compounds using Autodock against Carboxylesterase Notum esterase. Conclusion: From the results, the designed compounds were found to inhibit Notum Carboxylase. Thus, the study led to the development of a novel lead compound for Carboxylesterase Notum.

scholarly journals Can chlorophyll-a in meso-oligotrophic shallow waters be estimated using statistical approaches and empirical models from MODIS imagery?

RBRH ◽  
2018 ◽  
Vol 23 (0) ◽  
Author(s):  
Andres Mauricio Munar ◽  
José Rafael Cavalcanti ◽  
Juan Martin Bravo ◽  
David Manuel Lelinho Da Motta Marques ◽  
Carlos Ruberto Fragoso Júnior
Keyword(s):  
Linear Regression ◽  
Spatial Heterogeneity ◽  
Multiple Linear Regression ◽  
Chlorophyll A ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Accurate Estimation ◽  
Shallow Waters ◽  
Chl A ◽  
Input Variables

ABSTRACT Accurate estimation of chlorophyll-a (Chl-a) concentration in inland waters through remote-sensing techniques is complicated by local differences in the optical properties of water. In this study, we applied multiple linear regression (MLR), artificial neural network (ANN), nonparametric multiplicative regression (NPMR) and four models (Appel, Kahru, FAI and O14a) to estimate the Chl -a concentration from combinations of spectral bands from the MODIS sensor. The MLR, NPMR and ANN models were calibrated and validated using in-situ Chl -a measurements. The results showed that a simple and efficient model, developed and validated through multiple linear regression analysis, offered advantages (i.e., better performance and fewer input variables) in comparison with ANN, NPMR and four models (Appel, Kahru, FAI and O14a). In addition, we observed that in a large shallow subtropical lake, where the wind and hydrodynamics are essential factors in the spatial heterogeneity (Chl-a distribution), the MLR model adjusted using the specific point dataset, performed better than using the total dataset, which suggest that would not be appropriate to generalize a single model to estimate Chl-a in these large shallow lakes from total datasets. Our approach is a useful tool to estimate Chl -a concentration in meso-oligotrophic shallow waters and corroborates the spatial heterogeneity in these ecosystems.

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