scholarly journals Controlling the outcome of SN2 reactions in ionic liquids: from rational data set design to predictive linear regression models

2020 ◽  
Vol 22 (40) ◽  
pp. 23009-23018
Author(s):  
Alexandra Schindl ◽  
Rebecca R. Hawker ◽  
Karin S. Schaffarczyk McHale ◽  
Kenny T.-C. Liu ◽  
Daniel C. Morris ◽  
...  
Keyword(s):  
Ionic Liquids ◽  
Linear Regression ◽  
Regression Models ◽  
Reaction Rate ◽  
Computational Approach ◽  
Set Design ◽  
Linear Regression Models ◽  
Data Set ◽  
Reaction Rate Constants ◽  
Sn2 Reactions

An iterative, combined experimental and computational approach towards predicting reaction rate constants in ionic liquids is presented.

scholarly journals On Comparing and Classifying Several Independent Linear and Non-Linear Regression Models with Symmetric Errors

Symmetry ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 820 ◽  
Author(s):  
Ji-jun ◽  
Mahmoudi ◽  
Baleanu ◽  
Maleki
Keyword(s):  
Data Mining ◽  
Linear Regression ◽  
Real World ◽  
Regression Models ◽  
Computational Approach ◽  
Parametric Methods ◽  
Linear Regression Models ◽  
Science Data ◽  
Non Linear ◽  
Real World Problems

In many real world problems, science fields such as biology, computer science, data mining, electrical and mechanical engineering, and signal processing, researchers aim to compare and classify several regression models. In this paper, a computational approach, based on the non-parametric methods, is used to investigate the similarities, and to classify several linear and non-linear regression models with symmetric errors. The ability of each given approach is then evaluated using simulated and real world practical datasets.

scholarly journals Breeding progress of disease resistance and impact of disease severity under natural infections in winter wheat variety trials

2021 ◽  
Vol 134 (5) ◽  
pp. 1281-1302 ◽  
Author(s):  
F. Laidig ◽  
T. Feike ◽  
S. Hadasch ◽  
D. Rentel ◽  
B. Klocke ◽  
...  
Keyword(s):  
Linear Regression ◽  
Winter Wheat ◽  
Disease Severity ◽  
Regression Models ◽  
Resistance Breeding ◽  
Linear Regression Models ◽  
Data Set ◽  
Variety Trials ◽  
Ageing Effects ◽  
Severity Scores

Abstract Key message Breeding progress of resistance to fungal wheat diseases and impact of disease severity on yield reduction in long-term variety trials under natural infection were estimated by mixed linear regression models. Abstract This study aimed at quantifying breeding progress achieved in resistance breeding towards varieties with higher yield and lower susceptibility for 6 major diseases, as well as estimating decreasing yields and increasing disease susceptibility of varieties due to ageing effects during the period 1983–2019. A further aim was the prediction of disease-related yield reductions during 2005–2019 by mixed linear regression models using disease severity scores as covariates. For yield and all diseases, overall progress of the fully treated intensity (I2) was considerably higher than for the intensity without fungicides and growth regulators (I1). The disease severity level was considerably reduced during the study period for mildew (MLD), tan spot (DTR) and Septoria nodorum blotch (ear) (SNB) and to a lesser extent for brown (leaf) rust (BNR) and Septoria tritici blotch (STB), however, not for yellow/stripe rust (YLR). Ageing effects increased susceptibility of varieties strongly for BNR and MLD, but were comparatively weak for SNB and DTR. Considerable yield reductions under high disease severity were predicted for STB (−6.6%), BNR (−6.5%) and yellow rust (YLR, −5.8%), but lower reductions for the other diseases. The reduction for resistant vs. highly susceptible varieties under high severity conditions was about halved for BNR and YLR, providing evidence of resistance breeding progress. The empirical evidence on the functional relations between disease severity, variety susceptibility and yield reductions based on a large-scale multiple-disease field trial data set in German winter wheat is an important contribution to the ongoing discussion on fungicide use and its environmental impact.

Estimation of magnitudes in central and eastern North America using intensity and felt area

1987 ◽  
Vol 77 (5) ◽  
pp. 1635-1654
Author(s):  
M. S. Sibol ◽  
G. A. Bollinger ◽  
J. B. Birch
Keyword(s):  
North America ◽  
Linear Regression ◽  
Regression Models ◽  
Seismic Waves ◽  
Estimation Methods ◽  
Eastern North America ◽  
Linear Regression Models ◽  
Data Set ◽  
Underlying Distribution ◽  
Felt Area

Abstract Over 480 earthquakes from central and eastern North America having instrumental mb magnitudes and maximum Modified Mercalli intensities (I0) were examined using exploratory data analysis techniques and modeled with robust estimation methods (91 of those earthquakes also have felt area data). A previously undocumented distinct offset of magnitudes is observed between Modified Mercalli intensities VI and VII in both the central and eastern North America data set and a separate western North American catalog of earthquakes. The offset is most probably a characteristic of the Modified Mercalli scale and brings into question the often assumed linear relationship between magnitude and intensity. (In particular, linear regression models could not accurately estimate magnitudes for larger, I0 = VII or VIII, events.) Instead, robust estimates of center and spread for individual intensity interval distributions are recommended. In studies where conversion of intensity to magnitude for groups of earthquakes is required (e.g., studies involving the Gutenberg-Richter recurrence relation), the underlying distribution of magnitudes (or its approximation) for each intensity should be used. Linear regression models using I0 and/or felt area (and several transformations were tested. The robust linear regression models m b = 2.48 + 0.0769 log 2 ( F A ) and m b = 2.16 + 0.0219 l 0 2 + 0.0596 log 2 ( F A ) (where FA is felt area in square kilometers) proved to be the most accurate magnitude estimation models for central and eastern North America earthquakes. A comparison of regional felt area models indicates that regional differences in attenuation of seismic waves may exist between central and eastern North America that becomes apparent only for events of sufficient magnitude. Those differences appear as larger felt areas for earthquakes of central North America as compared to earthquakes of eastern North America.

Independent Representations of Limb Axis Length and Orientation in Spinocerebellar Response Components

2002 ◽  
Vol 87 (1) ◽  
pp. 409-422 ◽  
Author(s):  
R. E. Poppele ◽  
G. Bosco ◽  
A. M. Rankin
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Joint Angle ◽  
Population Analysis ◽  
Principal Component ◽  
Linear Regression Models ◽  
Data Set ◽  
Axis Orientation ◽  
Parasagittal Plane ◽  
Axis Length

Dorsal spinocerebellar tract (DSCT) neurons transmit sensory signals to the cerebellum that encode global hindlimb parameters, such as the hindlimb end-point position and its direction of movement. Here we use a population analysis approach to examine further the characteristics of DSCT neuronal responses during continuous movements of the hind foot. We used a robot to move the hind paw of anesthetized cats through the trajectories of a step or a figure-8 footpath in a parasagittal plane. Extracellular recordings from 82 cells converted to cycle histograms provided the basis for a principal-component analysis to determine the common features of the DSCT movement responses. Five principal components (PCs) accounted for about 80% of the total variance in the waveforms across units. The first two PCs accounted for about 60% of the variance and they were highly robust across samples. We examined the relationship between the responses and limb kinematic parameters by correlating the PC waveforms with waveforms of the joint angle and limb axis trajectories using multivariate linear regression models. Each PC waveform could be at least partly explained by a linear relationship to joint-angle trajectories, but except for the first PC, they required multiple angles. However, the limb axis parameters more closely related to both the first and second PC waveforms. In fact, linear regression models with limb axis length and orientation trajectories as predictors explained 94% of the variance in both PCs, and each was related to a particular linear combination of position and velocity. The first PC correlated with the limb axis orientation and orientation velocity trajectories, whereas second PC with the length and length velocity trajectories. These combinations were found to correspond to the dynamics of muscle spindle responses. The first two PCs were also most representative of the data set since about half the DSCT responses could be at least 85% accounted for by weighted linear combinations of these two PCs. Higher-order PCs were unrelated to limb axis trajectories and accounted instead for different dynamic components of the responses. The findings imply that an explicit and independent representation of the limb axis length and orientation may be present at the lowest levels of sensory processing in the spinal cord.

FAKTOR DETERMINAN PENYALURAN KREDIT BANK PERSERO

2018 ◽  
Vol 23 (1) ◽  
pp. 60-71
Author(s):  
Wigiyanti Masodah
Keyword(s):  
Interest Rate ◽  
Linear Regression ◽  
Interest Rates ◽  
Regression Models ◽  
Linear Regression Models ◽  
Negative Impacts ◽  
Main Activity ◽  
The Impact ◽  
The Given ◽  
Multiple Linear Regression Models

Offering credit is the main activity of a Bank. There are some considerations when a bank offers credit, that includes Interest Rates, Inflation, and NPL. This study aims to find out the impact of Variable Interest Rates, Inflation variables and NPL variables on credit disbursed. The object in this study is state-owned banks. The method of analysis in this study uses multiple linear regression models. The results of the study have shown that Interest Rates and NPL gave some negative impacts on the given credit. Meanwhile, Inflation variable does not have a significant effect on credit given. Keywords: Interest Rate, Inflation, NPL, offered Credit.

Linear Regression Models for Interval-Valued Data using Log-transformation

2020 ◽  
Author(s):  
Nykolas Mayko Maia Barbosa ◽  
João Paulo Pordeus Gomes ◽  
César Lincoln Cavalcante Mattos ◽  
Diêgo Farias Oliveira
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Linear Regression Models ◽  
Log Transformation ◽  
Interval Valued

THE PREDICTIVE CONTENT OF DISAGGREGATED NORMAL INCOME: An Empirical Study in the JSX

2003 ◽  
Vol 5 (3) ◽  
pp. 363 ◽  
Author(s):  
Slamet Sugiri
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Stock Exchange ◽  
Manufacturing Firms ◽  
Single Step ◽  
Model Parameters ◽  
Linear Regression Models ◽  
Operating Income ◽  
Predictive Content ◽  
Multiple Step

The main objective of this study is to examine a hypothesis that the predictive content of normal income disaggregated into operating income and nonoperating income outperforms that of aggregated normal income in predicting future cash flow. To test the hypothesis, linear regression models are developed. The model parameters are estimated based on fifty-five manufacturing firms listed in the Jakarta Stock Exchange (JSX) up to the end of 1997.This study finds that empirical evidence supports the hypothesis. This evidence supports arguments that, in reporting income from continuing operations, multiple-step approach is preferred to single-step one.

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