scholarly journals Linear calibrations in chromatography: The incorrect use of ordinary least squares for determinations at low levels, and the need to redefine the limit of quantification with this regression model

2020 ◽  
Vol 43 (13) ◽  
pp. 2708-2717
Author(s):  
Juan M. Sanchez
Keyword(s):  
Regression Model ◽  
Least Squares ◽  
Ordinary Least Squares ◽  
Limit Of Quantification ◽  
Low Levels

scholarly journals A Monograph on Nonlinear Regression Models

2018 ◽  
Vol 7 (4.10) ◽  
pp. 543
Author(s):  
B. Mahaboob ◽  
B. Venkateswarlu ◽  
C. Narayana ◽  
J. Ravi sankar ◽  
P. Balasiddamuni
Keyword(s):  
Regression Model ◽  
Least Squares ◽  
Nonlinear Regression ◽  
Nonlinear Models ◽  
Nonlinear Least Squares ◽  
Error Variance ◽  
Ordinary Least Squares ◽  
Parameter Estimates ◽  
Nonlinear Regression Model ◽  
Nonlinear Regression Models

This research article uses Matrix Calculus techniques to study least squares application of nonlinear regression model, sampling distributions of nonlinear least squares estimators of regression parametric vector and error variance and testing of general nonlinear hypothesis on parameters of nonlinear regression model. Arthipova Irina et.al [1], in this paper, discussed some examples of different nonlinear models and the application of OLS (Ordinary Least Squares). MA Tabati et.al (2), proposed a robust alternative technique to OLS nonlinear regression method which provide accurate parameter estimates when outliers and/or influential observations are present. Xu Zheng et.al [3] presented new parametric tests for heteroscedasticity in nonlinear and nonparametric models.  

scholarly journals Population and Age-Based Cardiorespiratory Fitness Level Investigation and Automatic Prediction

2022 ◽  
Vol 8 ◽  
Author(s):  
Liangliang Xiang ◽  
Kaili Deng ◽  
Qichang Mei ◽  
Zixiang Gao ◽  
Tao Yang ◽  
...  
Keyword(s):  
Physical Activity ◽  
Regression Model ◽  
Least Squares ◽  
Cardiorespiratory Fitness ◽  
Aerobic Capacity ◽  
Ordinary Least Squares ◽  
Population Based ◽  
Activity Level ◽  
Least Squares Regression ◽  
Ordinary Least Squares Regression

Maximal oxygen consumption (VO2max) reflects aerobic capacity and is crucial for assessing cardiorespiratory fitness and physical activity level. The purpose of this study was to classify and predict the population-based cardiorespiratory fitness based on anthropometric parameters, workload, and steady-state heart rate (HR) of the submaximal exercise test. Five hundred and seventeen participants were recruited into this study. This study initially classified aerobic capacity followed by VO2max predicted using an ordinary least squares regression model with measured VO2max from a submaximal cycle test as ground truth. Furthermore, we predicted VO2max in the age ranges 21–40 and above 40. For the support vector classification model, the test accuracy was 75%. The ordinary least squares regression model showed the coefficient of determination (R2) between measured and predicted VO2max was 0.83, mean absolute error (MAE) and root mean square error (RMSE) were 3.12 and 4.24 ml/kg/min, respectively. R2 in the age 21–40 and above 40 groups were 0.85 and 0.75, respectively. In conclusion, this study provides a practical protocol for estimating cardiorespiratory fitness of an individual in large populations. An applicable submaximal test for population-based cohorts could evaluate physical activity levels and provide exercise recommendations.

scholarly journals PROVIDING THE FIRE RISK MAP IN FOREST AREA USING A GEOGRAPHICALLY WEIGHTED REGRESSION MODEL WITH GAUSSIN KERNEL AND MODIS IMAGES, A CASE STUDY: GOLESTAN PROVINCE

2017 ◽  
Vol XLII-4/W4 ◽  
pp. 477-481
Author(s):  
A. Shah-Heydari pour ◽  
P. Pahlavani ◽  
B. Bigdeli
Keyword(s):  
Regression Model ◽  
Least Squares ◽  
Geographically Weighted Regression ◽  
Fire Risk ◽  
Ordinary Least Squares ◽  
Gaussian Kernel ◽  
Weighted Regression ◽  
Slope Aspect ◽  
Risk Map ◽  
Geographically Weighted Regression Model

According to the industrialization of cities and the apparent increase in pollutants and greenhouse gases, the importance of forests as the natural lungs of the earth is felt more than ever to clean these pollutants. Annually, a large part of the forests is destroyed due to the lack of timely action during the fire. Knowledge about areas with a high-risk of fire and equipping these areas by constructing access routes and allocating the fire-fighting equipment can help to eliminate the destruction of the forest. In this research, the fire risk of region was forecasted and the risk map of that was provided using MODIS images by applying geographically weighted regression model with Gaussian kernel and ordinary least squares over the effective parameters in forest fire including distance from residential areas, distance from the river, distance from the road, height, slope, aspect, soil type, land use, average temperature, wind speed, and rainfall. After the evaluation, it was found that the geographically weighted regression model with Gaussian kernel forecasted 93.4% of the all fire points properly, however the ordinary least squares method could forecast properly only 66% of the fire points.

scholarly journals Using Robust Ridge Regression Diagnostic Method to Handle Multicollinearity Caused High Leverage Points

2021 ◽  
Vol 10 (1) ◽  
pp. 326
Author(s):  
Kafi Dano Pati
Keyword(s):  
Regression Model ◽  
Least Squares ◽  
Ridge Regression ◽  
Robust Regression ◽  
Ordinary Least Squares ◽  
Substantial Improvement ◽  
Least Trimmed Squares ◽  
Regression Estimate ◽  
Leverage Points ◽  
Parameter Coefficient

Statistics practitioners have been depending on the ordinary least squares (OLS) method in the linear regression model for generation because of its optimal properties and simplicity of calculation. However, the OLS estimators can be strongly affected by the existence of multicollinearity which is a near linear dependency between two or more independent variables in the regression model. Even though in the presence of multicollinearity the OLS estimate still remained unbiased, they will be inaccurate prediction about the dependent variable with the inflated standard errors of the estimated parameter coefficient of the regression model. It is now evident that the existence of high leverage points which are the outliers in x-direction are the prime factor of collinearity influential observations. In this paper, we proposed some alternative to regression methods for estimating the regression parameter coefficient in the presence of multiple high leverage points which cause the multicollinearity problem. This procedure utilized the ordinary least squares estimates of the parameter as the initial followed by an estimate of the ridge regression. We incorporated the Least Trimmed Squares (LTS) robust regression estimate to down weight the effects of multiple high leverage points which lead to the reduction of the effects of multicollinearity. The result seemed to suggest that the RLTS give a substantial improvement over the Ridge Regression.

scholarly journals Pengaruh Jam Baca Terhadap IPK Mahasiswa FEB UNPAD

2019 ◽  
Vol 13 (2) ◽  
pp. 15
Author(s):  
Siska Musdalifah ◽  
Estro Dariatno Sihaloho
Keyword(s):  
Regression Model ◽  
Least Squares ◽  
Multiple Regression ◽  
Ordinary Least Squares ◽  
Multiple Regression Model

Untuk mendapatkan jumlah IPK yang memuaskan, mahasiswa perlu melakukan berbagai usaha. Salah satu usaha yang dapat dilakukan ialah belajar, dalam proses belajar tersebut diperlukan minat baca. Penelitian ini bertujuan untuk menjelaskan pengaruh jam baca terhadap besarnya IPK yang diperoleh mahasiswa Fakultas Ekonomi dan Bisnis Universitas Padjajaran. Jam baca dalam penelitian ini merupakan pengasumsian dari seberapa besar minat baca yang dimiliki mahasiswa tersebut. Analisisnya menggunakan lama waktu yang dihabiskan untuk membaca, lama waktu kuliah dan partisipasi dalam organisasi sebagai variabel independen. Sampel penelitian ini adalah mahasiswa mahasiswi aktif di Fakultas Ekonomi dan Bisnis Universitas Padjajaran. Jenis data yang digunakan dalam penelitian ini adalah data kuantitatif dan bersumber dari data primer. Pengumpulan data dilakukan dengan menggunakan kuesioner yang disebarkan langsung ke 111 mahasiswa aktif. Analisis data menggunakan regresi linear berganda (Multiple Regression Model) dengan metode OLS (Ordinary Least Squares) menggunakan bantuan software Stata versi 15 for windows. Hasil pengujian menunjukan bahwa secara parsial, lama waktu yang dihabiskan untuk membaca, lama waktu kuliah dan partisipasi dalam organisasi berpengaruh secara signifikan terhadap IPK yang diperoleh mahasiswa Fakultas Ekonomi dan Bisnis Universitas Padjajaran. Sementara secara simultan, lama waktu yang dihabiskan untuk membaca, lama waktu kuliah dan partisipasi dalam organisasi berpengaruh juga secara signifikan terhadap IPK yang diperoleh mahasiswa Fakultas Ekonomi dan Bisnis Universitas Padjajaran.

Wouldn't It Be Nice …? The Automatic Unbiasedness of OLS (and GLS)

2008 ◽  
Vol 16 (3) ◽  
pp. 345-349 ◽  
Author(s):  
Robert C. Luskin
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Least Squares ◽  
Linear Regression Model ◽  
Generalized Least Squares ◽  
Ordinary Least Squares ◽  
Recent Issue ◽  
The Face ◽  
Best Linear Unbiased

In a recent issue of this journal, Larocca (2005) makes two notable claims about the best linear unbiasedness of ordinary least squares (OLS) estimation of the linear regression model. The first, drawn from McElroy (1967), is that OLS remains best linear unbiased in the face of a particular kind of autocorrelation (constant for all pairs of observations). The second, much larger and more heterodox, is that the disturbance need not be assumed uncorrelated with the regressors for OLS to be best linear unbiased. The assumption is unnecessary, Larocca says, because “orthogonality [of disturbance and regressors] is a property of all OLS estimates” (p. 192). Of course OLS's being best linear unbiased still requires that the disturbance be homoskedastic and (McElroy's loophole aside) nonautocorrelated, but Larocca also adds that the same automatic orthogonality obtains for generalized least squares (GLS), which is also therefore best linear unbiased, when the disturbance is heteroskedastic or autocorrelated.

scholarly journals ANALISIS JUMLAH KENDARAAN BERMOTOR DI DAERAH ISTIMEWA YOGYAKARTA (1990–2012)

KINERJA ◽  
2017 ◽  
Vol 19 (1) ◽  
pp. 68
Author(s):  
I Agus Wantara
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Least Squares ◽  
Linear Regression Model ◽  
Traffic Congestion ◽  
Ordinary Least Squares ◽  
T Test ◽  
Estimation Technique ◽  
The People ◽  
Log Linear

In the last few years, traffic congestions are often occurred in Yogyakarta. This situation is caused by the increasing number of vehicles in Yogyakarta.This study evaluates the effect of the gross regional domesticproduct (PDRB), the people of Daerah Istimewa Yogyakarta (JP), and region income (PD) to the number of vehicles in Daerah Istimewa Yogyakarta (JKB). The model consists of one behavioral equation: the number of vehicles equation. The estimation technique uses Ordinary Least Squares (OLS). MacKinnon, White, and Davidson test (MWD test) is used to choose between the two models: linear regression model or log-linearregression model.The sample covers observations for 23 years (1990 - 2012). The data are obtained from (1) Bank Indonesia (2) Badan Pusat Statistik DIY and various other sources. It is found that individually lnJP andlnPD are statistically significant (positive) except ln PDRB on the basis of (separate) t test. It is also found that on the basis of the F test collectively all the regressors have a significant effect on the regressand lnJKB.Keywords: the number of vehicles, traffic congestion, linear regression model, log-linear regression model.

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