Proper determination of the δ18O–δD relationship for ice and water by least-squares cubic regression

1993 ◽  
Vol 30 (1) ◽  
pp. 109-112 ◽  
Author(s):  
C. R. Burn ◽  
M. G. Maxwell
Keyword(s):  
Mass Spectrometry ◽  
Functional Analysis ◽  
Linear Regression ◽  
Least Squares ◽  
Experimental Error ◽  
Regression Line ◽  
The Other ◽  
Analytical Error ◽  
Isotope Concentration

The δ18O–δD relationship for ice and water is frequently summarized with a line fitted by least-squares linear regression. This technique assumes that one variable is known exactly and all error can be ascribed to the other. Unfortunately, determinations by mass spectrometry of both δ18O and δD are subject to experimental error. Often a blanket laboratory precision is provided for δ18O and δD, in which case functional analysis, accounting for the relative error in the variables, is appropriate. Properly, however, each sample has an individual analytical error in both variables, defined by the variance in estimates of isotope concentration provided by the mass spectrometer. Where individual errors are known, the least-squares cubic method, which assigns a weight to each sample and generates the summary line by an iterative method, may be used. An algorithm sufficient to determine both the functional fit and the least-squares cubic regression line is presented. Illustrations are provided, one of which demonstrates that if the plot of δ18O versus δD is scattered (r2 < 0.9), both the functional fit and the least-squares cubic regression line may be significantly different from the least-squares linear regression lines.

scholarly journals Foot Length as a Predictor of Weight in Children up to Five Years

2019 ◽  
Vol 31 (2) ◽  
pp. 39-44
Author(s):  
Md Shameem ◽  
Nazneen Akhter Banu ◽  
ANM Nurul Haque Bhuiyan ◽  
Ariful Islam
Keyword(s):  
Body Weight ◽  
Linear Regression ◽  
Regression Line ◽  
The Body ◽  
Critically Ill Children ◽  
Linear Regression Line ◽  
Foot Length ◽  
Measured Weight ◽  
Variable Weight

Weight measurement is essential for the management of pediatric patients to calculate the dose of the drugs. But it is not possible to move the child to a weighing scale for determination of body weight when the child is in a critical condition. The purpose of this study was to check if foot length correlates with child’s body weight in our situation and to devise a formula for prediction of weight based on foot– length observed. This Cross-sectional study was carried out in the Department of Pediatrics, Sir Salimullah Medical College, Mitford hospital, Dhaka over a period of 12 months between January 2008 and December 2008. A total of 300 children, between 0 day to five years, meeting the predefined eligibility criteria were included in the study. Using the available data, simple linear regression analysis was performed between the dependent variable weight and independent variable foot length. The estimated linear regression line was: Predicted weight (kg) = a+ [b× foot length]. Data were analyzed using correlation coefficient (r) between foot length and children’s weight. In this study correlation between foot length and weight (r) was 0.92(P<0.001) indicating a perfect linear relationship between them. In the present study determination of correlation (r2) was 0.85 meaning that 85% of the variability in weight might be explained by variation in foot length. The estimated linear regression line was: Predicted weight (kg) = - 4.64 + [1.12 X foot length], where- 4.64 was the intercept and 1.12 was the slope of the regression line. Comparison between measured weight and predicted weight revealed that94% of variation between measured weight and predicted weight was within ±2kg. More than half of the cases (58.3%) the above-mentioned variations were within ±1kg.  This study concluded, there was a strong correlation between foot length and weight in children up to five years. The body weight in children from 0 days up to the age of 5 years can be predicted from foot length. Prediction of weight simply by foot-length measurement could be a great help to the health care provider including doctors and health workers for drug dose calculation in critically ill children. TAJ 2018; 31(2): 39-44

scholarly journals Application of artificial neural network to predict properties of diesel-biodiesel blends

1970 ◽  
Vol 6 (2) ◽  
pp. 98-103 ◽  
Author(s):  
Jatinder Kumar ◽  
Ajay Bansal
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Linear Regression ◽  
Least Squares ◽  
Statistical Technique ◽  
Training Algorithms ◽  
Biodiesel Blends ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The experimental determination of various properties of diesel-biodiesel mixtures is very time consuming as well as tedious process. Any tool helpful in estimation of these properties without experimentation can be of immense utility. In present work, other tools of determination of properties of diesel-biodiesel blends were tried. A traditional statistical technique of linear regression (principle of least squares) was used to estimate the flash point, fire point, density and viscosity of diesel and biodiesel mixtures. A set of seven neural network architectures, three training algorithms along with ten different sets of weight and biases were examined to choose best Artificial Neural Network (ANN) to predict the above-mentioned properties of dieselbiodiesel mixtures. The performance of both of the traditional linear regression and ANN techniques were then compared to check their validity to predict the properties of various mixtures of diesel and biodiesel. Key words: Biodiesel; Artificial Neural Network; Principle of least squares; Diesel; Linear Regression. DOI: 10.3126/kuset.v6i2.4017Kathmandu University Journal of Science, Engineering and Technology Vol.6. No II, November, 2010, pp.98-103

Alternative Method for the Determination of a Full S-N Fatigue Profile

2016 ◽  
Vol 250 ◽  
pp. 209-216 ◽  
Author(s):  
Przemysław Strzelecki ◽  
Janusz Sempruch ◽  
Tomasz Tomaszewski
Keyword(s):  
Fatigue Life ◽  
Linear Regression ◽  
Fatigue Limit ◽  
Fatigue Curve ◽  
The Other ◽  
Staircase Method ◽  
Alternative Method ◽  
Random Fatigue

This paper presents two methods for estimating the S-N fatigue curve. The first is the traditional linear regression and staircase method. The other, alternative, method is based on random fatigue life, fatigue limit and probability. The both methods provide similar results but the latter one requires fewer test samples

Reduction of a two‐gravimeter base network by linear regression

Geophysics ◽  
1985 ◽  
Vol 50 (5) ◽  
pp. 867-869
Author(s):  
C. Patrick Ervin
Keyword(s):  
Linear Regression ◽  
Regression Line ◽  
Field Work ◽  
Base Station ◽  
The Other ◽  
Base Stations ◽  
Simultaneous Operation ◽  
Station Network ◽  
Exploration Environment ◽  
Base Network

In the exploration environment, a primary application of gravity surveying is regional reconnaissance. The first step in such a survey is to establish a base‐station network. Since an error in the network will propagate to many stations in the subsequent survey, careful field work and accurate reduction of these data are particularly critical. Optimally, successive base stations are tied by minimum‐time loops using at least two meters read simultaneously. Using two meters has the obvious advantage of doubling the number of ties with minimal increase in time and cost. Erroneous readings are also much easier to detect and correct with two meters. Furthermore, the simultaneous operation of the meters allows calibrations of the two to be compared by computing a linear regression of the readings of one meter against the corresponding readings of the other. If the meter calibrations are identical, the regression line should have a slope of 1. A significant deviation from 1 indicates a systematic variation in calibration.

scholarly journals A theoretical approach for discriminating accurately intrinsic pattern of biological systems and recognizing three kind soybean proteomes

2018 ◽  
Author(s):  
Huabin Zou
Keyword(s):  
Mass Spectrometry ◽  
High Performance ◽  
Theoretical Method ◽  
Biological Species ◽  
The Other ◽  
Liquid Chromatography Mass Spectrometry ◽  
Data Sequence ◽  
Biological Similarity ◽  
Proteomic Research

Abstractproteomics is able to reveal plentiful information related to different physiological and pathological states of biology. Further, the determination of accurately proteomic pattern is the essential platform for deeply proteomic research. While this has been somewhat ignored so far. In this article the quantitative standard Pg=61%, a biological similarity constant for discriminating accurately intrinsic proteomic patterns was established depending on biological common heredity and variation information equation in symmetric variation state. On the other hand, a novel theoretical method was proposed for linearly dividing nonlinear data sequence into linear segments. The proteomes of three kind soybeans were precisely distinguished from one another by analyzing their infrared fingerprint spectra relying on this theoretically systemic approach. Additionally, methods employed in this paper enable us to quickly, accurately and quantitatively determine the proteomic patterns without using any prior knowledge and learning samples, and without using electrophoresis, high performance liquid chromatography-mass spectrometry techniques, which are high cost, time-consuming. This approach provide us with an excellent one for quickly accurate determining biological species, physiological states and diagnosing pathological states based on proteomes.

scholarly journals Can climate help fighting COVID-19 trauma? A case study of Maricopa County, Arizona, USA

2021 ◽  
pp. 105-116
Keyword(s):  
Linear Regression ◽  
Solar Radiation ◽  
The Other ◽  
Climatic Parameters ◽  
Factors Affecting ◽  
Regression Test ◽  
Spearman Correlation Test ◽  
Maximum Minimum

Introduction: Since the emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, the disease has spread rapidly throughout the world and became a traumatic stressor. Identification of the factors affecting the spread of the disease makes it possible to prevent its further propagation and save more people in similar situations. Environmental and climatic parameters are among the factors affecting the prevalence of diseases. Determination of environmental effects on Coronavirus disease (COVID-19) prevalence can help develop policies to suppress the spread. Methods: This study investigated the effect of climatic parameters on the spread of COVID-19 disease in County Maricopa from March 11, 2020, to November 30, 2020. These parameters include maximum, minimum, and mean daily temperature as well as maximum, minimum, and mean daily humidity, wind speed, solar radiation, and Air Quality Index (AQI) of particulate matter10 (PM10), PM2.5, and O3. A Shapiro-Wilk test was used to evaluate the normality of variables and the Spearman correlation test was used to determine the correlation between parameters and daily COVID-19 cases. A simple linear regression was applied on parameters that had significant Spearman’sranked correlation with the daily COVID-19 cases to determine their contribution to the pandemic. Results: The present study showed that the maximum, minimum, and mean temperature parameters and PM10 and PM2.5 particles had a positive and significant correlation (P<0.01) with the prevalence of COVID-19 disease. The effect of PM10 particles was higher than the other parameters (0.488, P<0.01). The parameters of maximum, minimum, and mean relative humidity along with solar radiation and O3 AQI had a significant and negative correlation with the development of COVID-19 disease (P<0.01). The effect of maximum humidity was higher than that of the other parameters (-0.364, P<0.01). A linear regression test showed that O3 (β=-15.16, P<0.001) and Tmean (β=18.47, P<0.01) significantly predicted daily COVID-19 cases. Conclusion: It can be concluded that climatic parameters can affect the COVID-19 pandemic and should be addressed.

Effect of the Water Spectrum on the Determination of Dissolved Ammonia, Urea, and Chloride Concentrations by Raman Spectroscopy

1998 ◽  
Vol 52 (2) ◽  
pp. 240-249 ◽  
Author(s):  
Hsiaoling Wang ◽  
Chao Wang ◽  
Charles K. Mann ◽  
Thomas J. Vickers
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
The Other ◽  
Ammonium Salts ◽  
Ammonium Ion ◽  
Water Band ◽  
Chloride Concentrations ◽  
Better Than ◽  
Least Squares Approach

The effect of large, changing concentrations of electrolytes on the behavior of the OH stretching band of water have been investigated with the aim of developing methods for compensating for spectral interferences when solute NH bands are made the basis for mixture analyses. With the use of urea and ammonium salts as analytes, it was found that changing electrolyte concentrations affect the shape of the water band but do not appreciably affect the shapes of either the ammonium ion or urea Raman lines. Chlorides, nitrates, and mixtures of these were used as electrolytes. The identity of the anion had a significant effect on the shape of the OH band. Two methods of compensation were used. One involved factor analyzing the spectra of a set of solutions that contained chlorides and nitrates that are Raman inactive in the vicinity of the OH stretching band. The principal abstract factors were used in place of a water reference for a least-squares mixture analysis. The other method was application of partial least-squares. In addition to urea and ammonium ion, the concentration of KCl and the ionic strength of the system can be determined in the partial least-squares approach with limits of detection better than 0.1 M.

The sensitivity of n-alkane analysis to measurement error: implications for use in the study of diet composition

1998 ◽  
Vol 131 (4) ◽  
pp. 465-476 ◽  
Author(s):  
J. A. NEWMAN ◽  
F. CRIBARI-NETO ◽  
M. J. JENSEN
Keyword(s):  
Monte Carlo Simulation ◽  
Measurement Error ◽  
Diet Composition ◽  
Simultaneous Equations ◽  
The Other ◽  
Analytical Error ◽  
The Face ◽  
Ruminant Animals ◽  
Alkane Analysis

It is possible to estimate diet composition from an analysis of n-alkanes in the faeces of ruminant animals. The method requires the estimation of the concentrations of n-alkanes in the plants and faeces and then the solving of a system of simultaneous equations. There are at least three places in which significant measurement error may be introduced. First, there may be error in the determination of the concentrations of the n-alkanes in the herbage. This error may be the result of analytical error in the chemical analysis, or in the gathering of the representative sample of herbage. In either case, error in this estimate may be particularly important, since this estimate is not independently repeated for each animal in the study, but is conducted once and used throughout the study. Error may also be introduced in the estimates of digestibility of the n-alkanes themselves. The n-alkane method might be ideal if in fact the n-alkanes were completely indigestible – they are not and, furthermore, they are differentially digestible. Lastly, there may be measurement error in the estimate of the n-alkane concentrations in the faeces, which utilize the same analytical procedures that are used on the herbage. That is, if measurement error exists in the herbage estimates, it is quite possible that it also exists in the faeces estimates. We address these issues through the use of Monte Carlo simulation to investigate the likely effects of measurement error on diet composition and digestibility estimates obtained using the n-alkane method. Our results suggest the following conclusions: (1) in the face of any sort of measurement error, estimates of digestibility are likely to be unreliable; (2) when measurement error exists, one of the diet components will usually be under-estimated and the other will usually be over-estimated; (3) any sort of progressive bias in the n-alkane recovery estimates will probably have large and very significant effects on the results; and (4) if measurement error in the estimates of the n-alkane concentrations in the herbage and in the faeces are similar in expectation, then their effects tend to cancel each other out.

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