A computer linear regression model to determine ventilatory anaerobic threshold

1982 ◽  
Vol 52 (5) ◽  
pp. 1349-1352 ◽  
Author(s):  
G. W. Orr ◽  
H. J. Green ◽  
R. L. Hughson ◽  
G. W. Bennett
Keyword(s):  
Gas Exchange ◽  
Linear Regression ◽  
Regression Model ◽  
Anaerobic Threshold ◽  
Break Point ◽  
Mean Value ◽  
Ventilatory Anaerobic Threshold ◽  
Response To Exercise ◽  
Exchange Data

The anaerobic threshold has generally been determined by simple visual inspection of ventilation or other gas-exchange data obtained during incremental exercise. To establish objective criteria for the determination of anaerobic threshold, a computer algorithm has been developed that models the ventilatory response to exercise using multisegment linear regression. The best-fit regression model is chosen by minimizing the pooled residual sum of squares . The anaerobic threshold is reported as the first break point in that model. The computer-determined anaerobic threshold values for 37 subjects were compared with subjectively determined values as chosen by four independent observers. The observers' estimates, when pooled to yield a single a single value for each subject, gave a mean value for the gas-exchange anaerobic threshold of 2.26 +/- 0.69 l/min. The estimates by the computer method averaged 2.21 +/- 0.65 l/min. The correlation coefficient for these two methods was 0.94.

scholarly journals Validation of Velocity Measuring Devices in Velocity Based Strength Training

2021 ◽  
Vol 20 (1) ◽  
pp. 106-118
Author(s):  
Thorben Menrad ◽  
Jürgen Edelmann-Nusser
Keyword(s):  
Linear Regression ◽  
Strength Training ◽  
Mean Value ◽  
Coefficient Of Determination ◽  
Regression Analyses ◽  
Measuring Devices ◽  
Strength Exercises ◽  
Measurement Units ◽  
Velocity Measuring

Abstract To control and monitor strength training with a barbell various systems are on the consumer market. They provide the user with information regarding velocity, acceleration and trajectory of the barbell. Some systems additionally calculate the 1-repetition-maximum (1RM) of exercises and use it to suggest individual intensities for future training. Three systems were tested: GymAware, PUSH Band 2.0 and Vmaxpro. The GymAware system bases on linear position transducers, PUSH Band 2.0 and Vmaxpro base on inertial measurement units. The aim of this paper was to determine the accuracy of the three systems with regard to the determination of the average velocity of each repetition of three barbell strength exercises (squat, barbell rowing, deadlift). The velocity data of the three systems were compared to a Vicon system using linear regression analyses and Bland-Altman-diagrams. In the linear regression analyses the smallest coefficient of determination (R2.) in each exercise can be observed for PUSH Band 2.0. In the Bland-Altman diagrams the mean value of the differences in the average velocities is near zero for all systems and all exercises. PUSH Band 2.0 has the largest differences between the Limits of Agreement. For GymAware and Vmaxpro these differences are comparable.

On one twoepoch linear model with the nuisance parameters

2008 ◽  
Vol 58 (1) ◽  
Author(s):  
Karel Hron
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Model ◽  
Linear Regression Model ◽  
Mean Value ◽  
Nuisance Parameters ◽  
Variable Parameters ◽  
Linear Estimators ◽  
Theoretical Results

AbstractThe optimum linear estimators of the useful mean value parameters within a linear regression model with the stable and variable parameters and with the nuisance parameters are derived including their characteristics of accuracy. Some verification of theoretical results is presented.

Non-invasive automatic determination of anaerobic threshold using double gas exchange parameters

1990 ◽  
Vol 31 (2) ◽  
pp. 73-79
Author(s):  
Yoshiyuki Fukuba ◽  
Sachio Usui ◽  
Koichi Iwanaga ◽  
Takashige Koba ◽  
Masaki Munaka
Keyword(s):  
Gas Exchange ◽  
Anaerobic Threshold ◽  
Automatic Determination ◽  
Gas Exchange Parameters ◽  
Non Invasive ◽  
Exchange Parameters

scholarly journals A Methodology for Buildings Access to Solar Radiation in Sustainable Cities

2019 ◽  
Vol 11 (23) ◽  
pp. 6596 ◽  
Author(s):  
L. M. Fernández-Ahumada ◽  
J. Ramírez-Faz ◽  
R. López-Luque ◽  
A. Márquez-García ◽  
M. Varo-Martínez
Keyword(s):  
Solar Radiation ◽  
Regression Model ◽  
Mean Value ◽  
Urban Morphology ◽  
Sustainable Cities ◽  
Energy Sustainability ◽  
Solar Radiation Incident ◽  
Solar Access ◽  
The One

The growing need to improve the environmental and energy sustainability of buildings involves the use of solar radiation incident on their surfaces. However, in cities, this task is complicated due to the constructive geometry that leads to shading between buildings. In this context, this work presents a study of solar access to the façades of buildings in cities. The methodology is based on the determination of the incident annual solar radiation in 121 significant points of each façade considering the twelve representative days of the year. To characterize the influence of the different city typologies on solar access, the urban solar coefficient is proposed. A study of two neighborhoods in Cordoba (Spain) with different urban settings have been analyzed. Specifically, two typologies of neighborhoods have been compared: one with “L-shaped” and “U-shaped blocks” and another with “Grouped blocks”. For both of them, the Urban Solar Coefficient has been calculated, obtaining a higher mean value for the neighborhood with “L-shaped” and “U-shaped blocks” (0.317) than for the one with “Grouped blocks” (0.260). Accordingly, the results show that urban morphology can influence the Urban Solar Coefficient and solar access. Finally, a regression model for each neighborhood has been obtained in order to determine the dependence of the Urban Solar Coefficient on neighborhood geometry factors.

scholarly journals PENGEMBANGAN MODEL REGRESI POLINOMIAL BERGANDA PADA KASUS DATA PEMASARAN

2013 ◽  
Vol 12 (2) ◽  
pp. 149 ◽  
Author(s):  
Julyanti S Malensang ◽  
Hanny Komalig ◽  
Djoni Hatidja
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Polynomial Regression ◽  
Predictor Variable ◽  
Coefficient Of Determination ◽  
Linear Regression Models ◽  
A Value ◽  
Marketing Data ◽  
Set Up

PENGEMBANGAN MODEL REGRESI POLINOMIAL BERGANDA PADA KASUS DATA PEMASARANABSTRAK Regresi polinomial merupakan regresi linier berganda yang dibentuk dengan menjumlahkan pengaruh variabel prediktor (X) yang dipangkatkan secara meningkat sampai orde ke-k. Model regresi polinomial, struktur analisisnya sama dengan model regresi linier berganda. Artinya, setiap pangkat atau orde variabel prediktor (X) pada model polinomial, merupakan transformasi variabel awal dan dipandang sebagai sebuah variabel prediktor (X) baru dalam linier berganda. Model terbaik dari kelima model yang telah diuji adalah persamaan regresi model ke-5. Hal ini dapat dilihat dari nilai koefisien determinasi sebesar 99,1% dan nilai R-Sq(adj) = 98,8%, karena nilai R2 mendekati nilai yang telah diatur dan berdasarkan pengujian yang dilakukan ternyata seluruh koefisien-koefisien dari setiap variabel bebas signifikan serta ada kelengkungan yang bersifat kubik (pangkat 3) terhadap data X3 terhadap Y. Kata kunci: Pemasaran, Regresi polynomial. DEVELOPMENT OF MULTIPOLYNOMIAL REGRESSION MODEL ON MARKETING DATA CASE ABSTRACT Polynomial regression is linear regression multiple were created by summing the effect of each predictor variable (X) is raised to increase to the order of the k.  Polynomial regression model, has the same structure with linear regression models. That is, any rank or order predictor variable (X) in polynomial models, an initial variable transformation and is seen as a predictor variable (X) has the linear regression. The best model of the six models tested were equation regression model to-5.  It can be seen from the value of the coefficient of determination of 99.1% and a value of R-Sq (adj) = 98.8%, due to the value of R2 close to the value that has been set up and based on tests performed turns all the coefficients of each independent variable significantly and there are cubic curvature (rank 3) to the data X3 to Y. Keywords : Marketing, Polynomial regression.

Noninvasive Determination of Anaerobic Threshold by Monitoring the %SpO2 Changes and Respiratory Gas Exchange

2009 ◽  
Vol 23 (7) ◽  
pp. 2107-2113 ◽  
Author(s):  
Roohollah Nikooie ◽  
Reza Gharakhanlo ◽  
Hamid Rajabi ◽  
Morteza Bahraminegad ◽  
Ali Ghafari
Keyword(s):  
Gas Exchange ◽  
Anaerobic Threshold ◽  
Respiratory Gas Exchange
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