Regression models based on experimental data

2014 ◽  
Vol 34 (9) ◽  
pp. 570-572
Author(s):  
Yu. N. Polyanchikov ◽  
E. M. Frolov ◽  
D. S. Klyuikov
Keyword(s):  
Experimental Data ◽  
Regression Models

Regression models to predict herbage production and digestibility in a non-regular sequence of cuts

1979 ◽  
Vol 92 (3) ◽  
pp. 575-585 ◽  
Author(s):  
P. R. Edelsten ◽  
A. J. Corrall
Keyword(s):  
Experimental Data ◽  
Regression Models ◽  
Regular Sequence ◽  
Model Parameters ◽  
Yield Model ◽  
Cutting Interval ◽  
Time Of Year ◽  
Cutting Experiments ◽  
Cutting Strategy ◽  
Good Agreement

SUMMARYRegression models were constructed to predict the yields and digestibilities of herbage cut in different sequences of harvests.The yield model used a seasonal production curve modified by the effects of defoliation. Values for the model parameters were obtained by fitting the model to experimental data using a non-linear regression procedure. When these parameters were used, to predict treatment effects in another series of experimental data, good agreement was obtained. The digestibility model incorporated the effect on digestibility time of year, regrowth time and yield.Using the models to interpolate between the results of cutting experiments, annual yields were shown to increase with the date of first cut and also with the interval between subsequent cuts, whereas the average digestibility of the harvested material ecreased with the date of first cut and with the subsequent cutting interval. Finally, a procedure was devised for combining the two models in order to find an optimum cutting strategy for a hypothetical animal production system.

scholarly journals Controlling changes in the combustion surface of solid fuel charges through the use of heat-conducting elements

2021 ◽  
pp. 67-75
Author(s):  
S. K. Saveliev ◽  
D. K. Shcheglov
Keyword(s):  
Experimental Data ◽  
Thermal Diffusivity ◽  
Melting Point ◽  
Combustion Rate ◽  
Regression Models ◽  
Combustion Surface ◽  
Heat Conducting ◽  
Variation Range ◽  
Crystalline Forms ◽  
Condensed Systems

On the basis of experimental data on the local values of the combustion rate of condensed systems along the heat-conducting filaments placed therein, regression models were constructed to relate the value of the local combustion rate with such characteristics of heat-conducting filaments as the thermal diffusivity and melting point. The obtained regression model was used to assess a possible expansion of the variation range of the local combustion rate when using various crystalline forms of CVD diamonds as heat-conducting filaments. It was shown that a local increase in the combustion rate could exceed the baseline value by 200 times. The possibility of controlling the transformation of the combustion surface by using heat-conducting filaments with variable characteristics was confirmed.

Increased accuracy of prediction of fragmentation duration of urinary stones based on multifactorial regression models

2020 ◽  
pp. 34-42
Author(s):  
V. S. Chernega ◽  
◽  
A. N. Eremenko ◽  
S. N. Eremenko ◽  
◽  
...  
Keyword(s):  
Experimental Data ◽  
Regression Models ◽  
Laser Lithotripsy ◽  
Urinary Stones ◽  
Anatomical Characteristics ◽  
Expected Time ◽  
Model Based ◽  
Additional Costs ◽  
Accuracy Of Prediction

The regression models for prediction of contact holmium lithotripsy duration are given. Models are obtained on the basis of calculated and experimental data on duration of different stages of laser lithotripsy. They allow, based on the volume and radiological density of urinary stones and taking into account the anatomical characteristics of the patient, to calculate the expected time of complete fragmentation of the stones with a higher accuracy than on the factor of additional costs the known model based.

Calibration of stormwater quality regression models: a random process?

2010 ◽  
Vol 62 (4) ◽  
pp. 875-882 ◽  
Author(s):  
A. Dembélé ◽  
J.-L. Bertrand-Krajewski ◽  
B. Barillon
Keyword(s):  
Experimental Data ◽  
Least Squares ◽  
Regression Models ◽  
Linear Models ◽  
Least Squares Method ◽  
Weighted Least Squares ◽  
Ordinary Least Squares ◽  
Data Sets ◽  
Data Set ◽  
Urban Catchments

Regression models are among the most frequently used models to estimate pollutants event mean concentrations (EMC) in wet weather discharges in urban catchments. Two main questions dealing with the calibration of EMC regression models are investigated: i) the sensitivity of models to the size and the content of data sets used for their calibration, ii) the change of modelling results when models are re-calibrated when data sets grow and change with time when new experimental data are collected. Based on an experimental data set of 64 rain events monitored in a densely urbanised catchment, four TSS EMC regression models (two log-linear and two linear models) with two or three explanatory variables have been derived and analysed. Model calibration with the iterative re-weighted least squares method is less sensitive and leads to more robust results than the ordinary least squares method. Three calibration options have been investigated: two options accounting for the chronological order of the observations, one option using random samples of events from the whole available data set. Results obtained with the best performing non linear model clearly indicate that the model is highly sensitive to the size and the content of the data set used for its calibration.

Limitations of Statistical Design of Experiments Approaches in Engineering Testing

2000 ◽  
Vol 122 (2) ◽  
pp. 254-259 ◽  
Author(s):  
Stelu Deaconu ◽  
Hugh W. Coleman
Keyword(s):  
Experimental Data ◽  
Design Of Experiments ◽  
Regression Models ◽  
Statistical Design ◽  
Statistical Design Of Experiments ◽  
Experimental Conditions ◽  
True Value ◽  
Correct Model ◽  
Degree Of Confidence ◽  
Significant Change

A hypothetical experiment and Monte Carlo simulations were used to examine the effectiveness of statistical design of experiments methods in identifying from the experimental data the correct terms in postulated regression models for a variety of experimental conditions. Two analysis of variance techniques (components of variance and pooled mean square error) combined with F-test statistics were investigated with first-order and second-order regression models. It was concluded that there are experimental conditions for which one or the other of the procedures results in model identification with high confidence, but there are also other conditions in which neither procedure is successful. The ability of the statistical approaches to identify the correct models varies so drastically, depending on experimental conditions, that it seems unlikely that arbitrarily choosing a method and applying it will lead to identification of the effects that are significant with a reasonable degree of confidence. It is concluded that before designing and conducting an experiment, one should use simulations of the proposed experiment with postulated truths in order to determine which statistical design of experiments approach, if any, will identify the correct model from the experimental data with an acceptable degree of confidence. In addition, no significant change in the effectiveness of the methods in identifying the correct model was observed when systematic uncertainties of up to 10 percent in the independent variables and in the response were introduced into the simulations. An explanation is that the systematic errors in the simulation data caused a shift of the whole response surface up or down from the true value, without a significant change in shape. [S0098-2202(00)03102-3]

Supporting the Empiric Research of Diesel Engines with Multi-Equation Models

2013 ◽  
Vol 196 ◽  
pp. 74-81 ◽  
Author(s):  
Ryszard Zadrąg
Keyword(s):  
Experimental Data ◽  
Regression Models ◽  
Combustion Engine ◽  
Engine Test ◽  
Output Variable ◽  
Software Applications ◽  
Single Output ◽  
Measurement Results ◽  
Multiple Regression Models ◽  
Paper Author

Contemporary empirical researches on the object, which is combustion engine, are processed basing on the theory of experiment. Available software applications to analyze the experimental data commonly use the multiple regression models, which enables studying effects and interactions between input values of the model and single output variable. Using multi-equation models gives free hand at analyzing measurement results because it enables analysis of effects and interaction of many output variables. It also allows analysis of the measurement results during dynamic process. In this paper author presents advantages of using the multidimensional regression model on example of researches conducted on engine test stand.

Framework of decision in data modeling for quality improvement

2015 ◽  
Vol 27 (1) ◽  
pp. 135-149 ◽  
Author(s):  
Ângelo Márcio Oliveira Sant'Anna
Keyword(s):  
Experimental Data ◽  
Regression Model ◽  
Regression Models ◽  
Formal Analysis ◽  
Data Modeling ◽  
Industrial Processes ◽  
Statistical Regression ◽  
Mining Company ◽  
Content Type

Purpose – The purpose of this paper is to propose a framework of decision making to aid practitioners in modeling and optimization experimental data for improvement quality of industrial processes, reinforcing idea that planning and conducting data modeling are as important as formal analysis. Design/methodology/approach – The paper presents an application was carried out about the modeling of experimental data at mining company, with support at Catholic University from partnership projects. The literature seems to be more focussed on the data analysis than on providing a sequence of operational steps or decision support which would lead to the best regression model given for the problem that researcher is confronted with. The authors use the concept of statistical regression technique called generalized linear models. Findings – The authors analyze the relevant case study in mining company, based on best statistical regression models. Starting from this analysis, the results of the industrial case study illustrates the strong relationship of the improvement process with the presented framework approach into practice. Moreover, the case study consolidating a fundamental advantage of regression models: modeling guided provides more knowledge about products, processes and technologies, even in unsuccessful case studies. Research limitations/implications – The study advances in regression model for data modeling are applicable in several types of industrial processes and phenomena random. It is possible to find unsuccessful data modeling due to lack of knowledge of statistical technique. Originality/value – An essential point is that the study is based on the feedback from practitioners and industrial managers, which makes the analyses and conclusions from practical points of view, without relevant theoretical knowledge of relationship among the process variables. Regression model has its own characteristics related to response variable and factors, and misspecification of the regression model or their components can yield inappropriate inferences and erroneous experimental results.

scholarly journals Desorption isotherms and isosteric heat of araticum (Annona crassiflora Mart.) seeds

2020 ◽  
Vol 24 (9) ◽  
pp. 630-636
Author(s):  
Weder N. Ferreira Junior ◽  
Osvaldo Resende ◽  
Kelly A. de Sousa ◽  
Melícia I. A. Gavazza ◽  
Juliana de F. Sales ◽  
...  
Keyword(s):  
Experimental Data ◽  
Moisture Content ◽  
Nonlinear Regression ◽  
Regression Models ◽  
Information Criterion ◽  
Isosteric Heat ◽  
Nonlinear Regression Models ◽  
Desorption Isotherms ◽  
Annona Crassiflora ◽  
Content Range

ABSTRACT The objective of this study was to determine the desorption isotherms and isosteric heat of Annona crassiflora Mart. seeds, using Akaike information criterion (AIC) and Schwarz’s Bayesian information criterion (BIC) to assist in the choice of the nonlinear regression model. The desorption isotherms were determined by indirect static method and water activity was obtained using the instrument HygroPalm; the product was put in the device in B.O.D. chamber set at 10, 20, 30 and 40 °C. Several nonlinear regression models were fitted to the experimental data by the Gauss-Newton method. The desorption isotherms of Annona crassiflora Mart. seeds can be represented by the models of Chung-Pfost, Copace, Modified GAB, Modified Henderson, Modified Oswin, Sabbah and Sigma Copace. However, the Sigma Copace model showed better fit to the experimental data, with lower AIC and BIC values, being chosen to represent the desorption isotherms of Annona crassiflora Mart seeds. Isosteric heat increased with decreasing moisture content, requiring a greater amount of energy to remove water from seeds, with values ranging from 2541.64 to 2481.56 kJ kg-1, for the moisture content range from 5.69 to 14.93% on a dry basis.

scholarly journals PREPARATION OF WOOD FIBER SEMI-FINISHED PRODUCTS IN KNIFE MACHINES OF VARIOUS MODIFICA-TION

2020 ◽  
pp. 459-466
Author(s):  
Natal'ya Geral'dovna Chistova ◽  
Venera Nurullovna Matygulina ◽  
Yuri Davydovich Alashkevich
Keyword(s):  
Experimental Data ◽  
Regression Models ◽  
Wood Fiber ◽  
Wood Pulp ◽  
Design Parameters ◽  
Grinding Process ◽  
Technological Parameters ◽  
Grinding Machines ◽  
Qualitative Characteristics

In this paper, we consider the results of a study of the influence of the design and technological parameters of grinding machines of various modifications on the quality of wood pulp. According to the results of processing multifactor experiments implemented according to the second-order B-plan, a mathematical description was obtained of the dependence of the degree of grinding of pulp on the gap between the grinding disks, wear of the segments, the rotational speed of the lower screw and the concentration of pulp. The obtained regression models are adequate to the process and can be applied in practice for predicting the qualitative characteristics of wood pulp depending on the parameters of the grinding process. Analyzing the obtained experimental data, it can be noted that such factors as the state of the surfaces of grinding disks, the gap between them and the concentration of wood fiber have the greatest influence on the degree of grinding of the mass. Evaluation of graphical dependencies allows you to determine the extent to which you can vary the operating and design parameters of the grinding process to obtain wood fiber with the required grinding quality.

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