scholarly journals Modelling Wages in Croatia Using a Second Order Polynomial Regression Model

2015 ◽  
Vol 6 (6) ◽  
Author(s):  
Tea Baldigara ◽  
Kristina Duvnjak
Keyword(s):  
Regression Model ◽  
Polynomial Regression ◽  
Second Order ◽  
Order Polynomial ◽  
Polynomial Regression Model

scholarly journals An empirical study of Taiwan’s food security index

2009 ◽  
Vol 13 (7) ◽  
pp. 1056-1063 ◽  
Author(s):  
Chih-Yang Yeh ◽  
Pei-San Liao ◽  
Chieh-Yu Liu ◽  
Jeng-Fu Liu ◽  
Hsing-Yi Chang
Keyword(s):  
Regression Model ◽  
Polynomial Regression ◽  
Energy Requirement ◽  
Second Order ◽  
Activity Level ◽  
Dietary Energy ◽  
Order Polynomial ◽  
The Status ◽  
Level Of Economic Development ◽  
Polynomial Regression Model

AbstractObjectiveThe FAO has developed an approach for estimating the prevalence of undernourishment. Based on the FAO method Taiwan has a prevalence of undernourishment of 3·98 %, which is higher than that of some developing countries. As this is not a true reflection of the status of undernourishment in our nation, the purpose of the present study was to modify the FAO methodology for Taiwan.DesignTwo factors were considered in the modified version. As the minimum dietary energy requirement was the main factor contributing to the inflated prevalence in Taiwan, we adjusted for a lighter physical activity level, based on the average BMI of the Taiwanese population, and calculated a new minimum dietary energy requirement. We then fitted a second-order polynomial regression model for prediction of per capita dietary energy supply.ResultsThe adjusted minimum dietary energy requirement was reduced to 7648 kJ/d or 7765 kJ/d compared with the original value of 8054 kJ/d. This resulted in a decrease of the prevalence of undernourishment in Taiwan to 2·5 % or 3·0 %, which is much closer to that of other countries with the same level of economic development. The second-order polynomial regression model efficiently reduced the variation in dietary energy consumption and resulted in an undernourishment prevalence of less than 2·5 %.ConclusionsThis new adapted method is more appropriate for Taiwan. It is recommended that each country evaluates the appropriateness of the FAO approach for its population.

The influence of the regression model and final speed criteria on the reliability of lactate threshold determined by the Dmax method in endurance-trained runners

2016 ◽  
Vol 41 (10) ◽  
pp. 1039-1044 ◽  
Author(s):  
Júlio César Camargo Alves ◽  
Cecília Segabinazi Peserico ◽  
Geraldo Angelo Nogueira ◽  
Fabiana Andrade Machado
Keyword(s):  
Regression Model ◽  
Regression Models ◽  
Lactate Threshold ◽  
Polynomial Regression ◽  
Third Order ◽  
The Third ◽  
Constant Regression ◽  
Order Polynomial ◽  
Final Speed ◽  
Polynomial Regression Model

Few studies verified the reliability of the lactate threshold determined by Dmax method (LTDmax) in runners and it remains unclear the effect of the regression model and the final speed on the reliability of LTDmax. This study aimed to examine the test–retest reliability of the speed at LTDmax in runners, considering the effects of the regression models (exponential-plus-constant vs third-order polynomial) and final speed criteria (complete vs proportional). Seventeen male, recreational runners performed 2 identical incremental exercise tests, with increments of 1 km·h–1 each for 3 min on treadmill to determine peak treadmill speed (Vpeak) and lactate threshold. Earlobe capillary blood samples were collected during rest between the stages. The Vpeak was defined as the speed of the last complete stage (complete final speed criterion) and as the speed of the last complete stage added to the fraction of the incomplete stage (proportional final speed criterion). Lactate threshold was determined from exponential-plus-constant and from third-order polynomial regression models with both complete and proportional final speed criteria and from fixed blood lactate level of 3.5 mmol·L−1 (LT3.5mM). The LTDmax obtained from the exponential-plus-constant regression model presented higher reliability (coefficient of variation (CV) ≤ 3.7%) than the LTDmax calculated from the third-order polynomial regression model (CV ≤ 5.8%) and LT3.5mM (CV = 5.4%). The proportional final speed criterion is more appropriate when using the exponential-plus-constant regression model, but less appropriate when using the third-order polynomial regression model. In conclusion, exponential-plus-constant using the proportional final speed criterion is preferred over LT3.5mM and over third-order polynomial regression model to determine a reliable LTDmax.

Systematic Dimensional Calibration Process for 3D Printed Parts in Selective Laser Sintering (SLS)

2015 ◽  
Author(s):  
Sangho Ha ◽  
Hweeyoung Han ◽  
Daeil Kwon ◽  
Namhun Kim ◽  
Hyeonnam Kim ◽  
...  
Keyword(s):  
Regression Model ◽  
Polynomial Regression ◽  
Selective Laser Sintering ◽  
Laser Sintering ◽  
Engineering Properties ◽  
Calibration Process ◽  
Order Polynomial ◽  
3D Printed ◽  
Deformation Patterns ◽  
Polynomial Regression Model

The selective laser sintering (SLS) processes, known for enhancing engineering properties and durability of products, are widely used in auto part development processes. The dimensional displacements of the 3D printed parts, however, hinder utilizing the technology directly to enhance their development process. In general, the SLS process causes curved shapes (convex) due to thermal deformation (thermal expansion and thermal contraction) in the powder sintering and cooling processes, which accompanies multi-phase changes of the raw materials (polymer powders). In this research, we aim to present a systematic dimensional calibration process by investigating and analyzing the dimensional deformation patterns of 3D printed samples in SLS platform (using 3D Systems’ sPro60 SD). Firstly, the test samples with complex features are produced to check the reference dimensional deviation of the SLS process. Secondly, the deformation patterns are measured and analyzed as a form of a 2nd order polynomial regression model in the global Cartesian coordinates of the platform. Lastly, the dimensional calibration methods to minimize the process errors are presented by the pre-processing of the original CAD file (.stl) with inverse transformation of the features using the 2nd order polynomial regression model. At the end of the paper, we will propose an algorithm that predicts the deformation and calibrates point-based 3D CAD STL files of samples in order to mitigate the dimensional deformation, along with test samples for illustrative purposes.

scholarly journals Polynomial Regression Model Analysis of Abdominal Wall Tension and Intra-abdominal Pressure in Critically ill Patients

2021 ◽  
Author(s):  
Hao Tang ◽  
Dongchu Zhao ◽  
Chuan Zhang ◽  
Xiaoying Huang ◽  
Dong Liu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Regression Model ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Polynomial Regression ◽  
Prognostic Indicators ◽  
Wall Tension ◽  
Abdominal Hypertension ◽  
Abdominal Compliance ◽  
Polynomial Regression Model

Abstract BackgroundAbdominal wall tension (AWT) plays an important role in the pathogenesis of abdominal compliance (AC). This study uses a polynomial regression model to analyze the correlation between intra-vesical pressure(IVP) and AWT in critically ill patients and provides new ideas for the diagnosis and treatment of critically ill patients with intra-abdominal hypertension（IAH）.MethodsA retrospective analysis was conducted in critically ill patients who met the inclusion criteria and were admitted to the Department of intensive care unit of Daping Hospital of Army Medical University from March 14, 2019, to May 23, 2020. According to the IVP on the first day of ICU admission and death within 28 days, the patients were divided into the IAH group (IVP ≥12 mmHg), the non-IAH group, the survival group and the nonsurvival group. The demographic and clinical data, prognostic indicators, AWT and IVP on days 1-7 after entering the ICU, IAH risk factors, and 28-day death risk factors were collected.ResultsA total of 100 patients were enrolled, with an average age of 45.59±11.4 years. There were 55 males (55%), 30 patients from departments of internal medicine (30%), 43 patients from surgery departments (43%), and 27 trauma patients (27%). In the IAH group, there were 50 patients (29 males, 58%), with an average age of 45.28±12.27 years; there were 50 patients (26 males, 52%) in the non-IAH group, with an average age of 45.90±10.58 years. The IVP on the 1st day and the average IVP within 7 days of the IAH group was 18.99（17.52，20.77）mmHg and 19.43（16.87，22.25）mmHg, respectively, which was higher than that of the non-IAH group [ 6.14（3.48，8.70）mmHg, 6.66（2.74，9.08）mmHg], p＜0.001. The AWT on the 1st day and the average AWT within 7 days of the IAH group was 2.89±0.32 N/mm and 2.82±0.46 N/mm, respectively, which was higher than that of the non-IAH group [(2.45±0.29)N/mm,(2.43±0.39)N/mm],p＜0.001.The polynomial regression models showed that the average AWT and IVP on the 1st day and within 7 days were AWTday1 = -2.450×10-3IVP2+9.695×10-2 IVP+2.046，r=0.667（p＜0.0001），and AWTmean = -2.293×10-3IVP2+9.273×10-2 IVP+2.081, respectively. The logistic regression analysis showed that AWTday1 of 2.73-2.97 N/mm increased the patient's 28-day mortality risk (OR: 6.834; 95%: 1.105-42.266, p=0.010).ConclusionsThere is a nonlinear correlation between AWT and IVP in critically ill patients, and a high AWT may indicate poor prognosis.

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