scholarly journals NONPARAMETRIC SEQUENTIAL ESTIMATION OF A MULTIPLE REGRESSION FUNCTION

10.5109/13104 ◽  
1976 ◽  
Vol 17 (1/2) ◽  
pp. 63-75 ◽  
Author(s):  
Ibrahim A. Ahmad ◽  
Pi-Erh Lin
Keyword(s):  
Multiple Regression ◽  
Sequential Estimation ◽  
Regression Function

Mathematical model for determining the expenditure of cooling and lubricating fluid reaching directly the grinding zone

2018 ◽  
Vol 1 (94) ◽  
pp. 27-34
Author(s):  
W. Stachurski ◽  
J. Sawicki ◽  
K. Krupanek ◽  
S. Midera
Keyword(s):  
Mathematical Model ◽  
Multiple Regression ◽  
Regression Function ◽  
Multiple Regression Equation ◽  
Grinding Wheel ◽  
Grinding Process ◽  
Rake Face ◽  
Rejection Method ◽  
Ansys Cfx ◽  
The Mathematical Model

Purpose: The purpose of this article is to discuss the method of determining the mathematical model used for calculating the amount of emulsion reaching directly the grinding zone during the hob sharpening process. Design/methodology/approach: The mathematical model, in the form of a multiple regression function, was determined based on the acceptance and rejection method. The data for the calculations was obtained by conducting numerical simulations of fluid flow in the Ansys CFX software. Findings: A mathematical model enables calculating the amount of efficient expenditure of emulsion reaching directly the zone of contact between the grinding wheel and workpiece (hob cutter rake face) at various nozzle angle settings and different nominal expenditures of emulsion. The verification of the mathematical relationship confirmed its accuracy. Research limitations/implications: Further research should focus on the other types of grinding process and other types of cooling and lubricating fluids. Practical implications: The mathematical model enables a selection and application in the workshop and industrial practice of various variants of emulsion supply during the grinding of hob cutter rake face. Analysis of the multiple regression equation created on the basis of the acceptance and rejection method also allows predicting changes in the analyzed numerical model. Originality/value: The literature review has shown that no research of this type has been conducted with regard to analyses and optimisation of the grinding process during hob cutter sharpening. The results of this research are a novelty on a worldwide scale.

Fitting a multiple regression function

1984 ◽  
Vol 9 (2) ◽  
pp. 163-176 ◽  
Author(s):  
Ibrahim A. Ahmad ◽  
Pi-Erh Lin
Keyword(s):  
Multiple Regression ◽  
Regression Function

Birthweight of Children With Phenylketonuria

PEDIATRICS ◽  
1976 ◽  
Vol 58 (6) ◽  
pp. 842-844
Author(s):  
Kenneth J. Rothman ◽  
Siegfried N. Pueschel
Keyword(s):  
Multiple Regression ◽  
Weighted Average ◽  
Regression Function ◽  
Physical Growth ◽  
The Difference ◽  
Unaffected Child

The assumption that children with phenylketonuria (PKU) develop normally until birth was brought into question by the recent report that PKU children weigh several hundred grams less at birth than their unaffected siblings. We have examined intrafamily differences in birthweight in 40 sibships with at least one affected and one unaffected child. The difference in mean birthweights computed by taking a weighted average of the intrafamily differences was –69 gm, and the adjusted estimate of the birthweight difference between children with PKU and their siblings obtained from a fitted multiple regression function, is –51 gm. The findings are not consistent with the large difference in birthweight reported previously and are compatible with the assumption that the intrauterine physical growth of children with classical PKU is not adversely affected.

scholarly journals PEMODELAN REGRESI SPLINE (Studi Kasus: Herpindo Jaya Cabang Ngaliyan)

2015 ◽  
Vol 4 (3) ◽  
pp. 110
Author(s):  
I MADE BUDIANTARA PUTRA ◽  
I GUSTI AYU MADE SRINADI ◽  
I WAYAN SUMARJAYA
Keyword(s):  
Multiple Regression ◽  
Regression Function ◽  
Predictor Variables ◽  
Coefficient Of Determination ◽  
Purchasing Decisions ◽  
Product Price ◽  
Nonparametric Approach ◽  
The Relationship ◽  
Response Variables

Regression analysis is a method of data analysis to describe the relationship between response variables and predictor variables. There are two approaches to estimating the regression function. They are parametric and nonparametric approaches. The parametric approach is used when the relationship between the predictor variables and the response variables are known or the shape of the regression curve is known. Meanwhile, the nonparametric approach is used when the form of the relationship between the response and predictor variables is unknown or no information about the form of the regression function. The aim of this study are to determine the best spline nonparametric regression model on data of quality of the product, price, and advertising on purchasing decisions of Yamaha motorcycle with optimal knots point and to compare it with the multiple regression linear based on the coefficient of determination (R2) and mean square error (MSE). Optimal knot points are defined by two point knots. The result of this analysis is that for this data multiple regression linear is better than the spline regression one.

Surface roughness of the Ti-6Al-4V ELI titanium alloy after the turning process

2019 ◽  
Vol 2 (98) ◽  
pp. 74-80
Author(s):  
R. Rosik ◽  
N. Kępczak ◽  
M. Sikora ◽  
B. Witkowski ◽  
R. Wójcik ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Surface Roughness ◽  
Titanium Alloy ◽  
Multiple Regression ◽  
Roughness Parameter ◽  
Regression Function ◽  
Turning Process ◽  
Longitudinal Turning ◽  
Machining Parameter ◽  
The Mathematical Model

Purpose: The purpose of this article is discussing the methods of determining the surface roughness of the Ti-6Al-4V ELI titanium alloy obtained after longitudinal turning. The method of determining the mathematical model used for determining the Rz roughness parameter and then the results obtained were compared with values measured and calculated on the basis of equations available in the literature. Design/methodology/approach: The mathematical model in the form of multiple regression function of exponential polynomial was determined using the algorithm of the acceptance and rejection method. The data for calculations was obtained by measuring the surface roughness after turning with different machining parameter values. Findings: A mathematical model was elaborated in the form of a multiple regression function, enabling calculation of the Rz parameter describing the Ti-6Al-4V ELI titanium alloy surface roughness after longitudinal turning. The verification of the dependence obtained confirmed its accuracy. Research limitations/implications: Further research should encompass other values of machining plate geometry, as well as other types of cooling and lubricating fluids and method of applying them. Practical implications: The mathematical model can be helpful when choosing the conditions in which the turning process will be carried out. It also constitutes a basis for further optimisation of that process. Originality/value: The results of this research are a novelty on a worldwide scale. No research of this type has been conducted with regard to analyses and optimisation of longitudinal turning of the Ti-6Al-4V ELI titanium alloy.

scholarly journals Passenger level of service estimation model for queuing systems at the airport

2018 ◽  
Vol 47 (3) ◽  
pp. 29-38 ◽  
Author(s):  
Artur Kierzkowski ◽  
Tomasz Kisiel ◽  
Maria Pawlak
Keyword(s):  
Mathematical Model ◽  
Multiple Regression ◽  
Process Management ◽  
Regression Function ◽  
Service Operations ◽  
Level Of Service ◽  
Estimation Model ◽  
Scientific Publications ◽  
Time Space ◽  
The Mathematical Model

This paper presents a model for the management of passenger service operations at airports by the estimation of a global index of the level of service. This paper presents a new approach to the scheduling of resources required to perform passenger service operations at airports. The approach takes into account the index of level of service as a quantitative indicator that can be associated with airport revenues. Taking this index into account makes it possible to create an operating schedule of desks, adapted to the intensity of checking-in passengers, and, as such, to apply dynamic process management. This offers positive aspects, particularly the possibility of improvement of service quality that directly translates into profits generated by the non-aeronautical activity of airports. When talking about level of service, there can be distinguish other important indicators that are considered very often (eg maximum queuing time, space in square meters). In this model, however, they are considered as secondary. Of course, space in square meters is important when designing a system. Here this system is already built and functioning. The concept of the model is the use of a hybrid method: computer simulation (Monte Carlo simulation) with multiple regression. This paper focuses on the presentation of a mathematical model used to determine the level of service index that provides new functionality in the current simulation model, as presented in the authors’ previous scientific publications. The mathematical model is based on a multiple regression function, taking into account the significance of individual elementary operations of passenger service at an air terminal.

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