Analytic Solution of Inverse Regression Equation for Evaluating Concrete Strength of Building Materials: Experimental and Application

2013 ◽  
Vol 357-360 ◽  
pp. 757-760
Author(s):  
Jian Wei Chen ◽  
You Po Su ◽  
Hai Bin Chen
Keyword(s):  
Regression Model ◽  
Regression Models ◽  
Building Materials ◽  
Analytic Solution ◽  
Concrete Strength ◽  
Regression Equation ◽  
Basic Theory ◽  
Inverse Regression ◽  
Analysis Method

The basic theory of the rebound method is introduced, and analytic solution of inverse regression equation for evaluating concrete strength of building materials is presented herein based on the inverse regression model. Furthermore, two engineering examples are given, satisfactory results are obtained using the analysis method. The analysis method can be also used to gain the presumption of strength for others building materials and solutions of other inverse regression models.

Analytic Solution of Inverse Regression Equation for Evaluating Concrete Strength of Building Materials: Theory and Method

2013 ◽  
Vol 357-360 ◽  
pp. 743-746
Author(s):  
Jian Wei Chen ◽  
You Po Su ◽  
Hai Bin Chen
Keyword(s):  
Building Materials ◽  
Analytic Solution ◽  
Concrete Strength ◽  
Estimation Method ◽  
Interval Estimation ◽  
Accurate Solution ◽  
Point Estimation ◽  
Regression Equation ◽  
Inverse Regression ◽  
Point Estimation Method

The interval estimation for the presumption of structure concrete strength should be gained because of strong randomness of test data. For further exaltation of accuracy and credibility test concrete strength, analytic solution of inverse regression equation for evaluating concrete strength of building materials is presented herein based on the inverse regression model. The accurate solution for the presumption of concrete strength can be obtained through the analytic solution, detection precision and reliability for the presumption of structure concrete strength can be improved with respect to the traditional point estimation method.

scholarly journals Türkiye’de Organik Bal Üretiminin Yıllara Göre Değişiminin Regresyon Analizi ile İncelenmesi Üzerine Bir Çalışma

2018 ◽  
Vol 6 (11) ◽  
pp. 1507 ◽  
Author(s):  
Şenol Çelik ◽  
Turgay Şengül ◽  
Bünyamin Söğüt ◽  
A. Yusuf Şengül
Keyword(s):  
Regression Analysis ◽  
Regression Model ◽  
Regression Models ◽  
Mean Squared Error ◽  
Parameter Estimates ◽  
Suitable Model ◽  
Inverse Regression ◽  
Linear Quadratic ◽  
Honey Production ◽  
Organic Honey

In this study, changes in organic honey production in Turkey between 2004 and 2016 were examined by regression analysis. In regression analysis, linear, quadratic, cubic, logarithmic and inverse regression models have been studied comparatively. The R2 values obtained with these models are; 0.155, 0.616, 0.699, 0.366, 0.522, R ̅^2 values were found as 0.079, 0.539, 0.599, 0.308, 0.479 and MSE (Mean Squared Error) values were 48743.013, 24376.605, 21228.605, 36580.476, 27563.473, respectively. The quadratic regression model, in which the parameter estimates are significant, R ̅^2 is the highest and MSE is the lowest, is the most appropriate model. According to this regression model, estimated organic honey production yields in 2017 and 2018 are going to be 693 and 891 tons, respectively. In addition, regression analysis of non-organic honey production was done in the same period and linear regression model was determined as the most suitable model. For this model, R2= 0.772 and R ̅^2 = 0.750 were calculated. As a result, it has been concluded that organic and non-organic honey production yields can be estimated with different regression models.

scholarly journals Use of a Big Data Analysis in Regression of Solar Power Generation on Meteorological Variables for a Korean Solar Power Plant

2021 ◽  
Vol 11 (4) ◽  
pp. 1776
Author(s):  
Young Seo Kim ◽  
Han Young Joo ◽  
Jae Wook Kim ◽  
So Yun Jeong ◽  
Joo Hyun Moon
Keyword(s):  
Power Generation ◽  
Data Analysis ◽  
Power Plant ◽  
Relative Humidity ◽  
Regression Model ◽  
Regression Models ◽  
Solar Power ◽  
Meteorological Variables ◽  
Solar Power Plant ◽  
Solar Power Generation

This study identified the meteorological variables that significantly impact the power generation of a solar power plant in Samcheonpo, Korea. To this end, multiple regression models were developed to estimate the power generation of the solar power plant with changing weather conditions. The meteorological data for the regression models were the daily data from January 2011 to December 2019. The dependent variable was the daily power generation of the solar power plant in kWh, and the independent variables were the insolation intensity during daylight hours (MJ/m2), daylight time (h), average relative humidity (%), minimum relative humidity (%), and quantity of evaporation (mm). A regression model for the entire data and 12 monthly regression models for the monthly data were constructed using R, a large data analysis software. The 12 monthly regression models estimated the solar power generation better than the entire regression model. The variables with the highest influence on solar power generation were the insolation intensity variables during daylight hours and daylight time.

An LIBS quantitative analysis method for alloy steel at high temperature based on transfer learning

2018 ◽  
Vol 33 (7) ◽  
pp. 1184-1195 ◽  
Author(s):  
Jianhong Yang ◽  
Xiaomeng Li ◽  
Huili Lu ◽  
Jinwu Xu ◽  
Haixia Li
Keyword(s):  
High Temperature ◽  
Quantitative Analysis ◽  
Regression Model ◽  
Transfer Learning ◽  
Alloy Steel ◽  
Room Temperature ◽  
Analysis Method ◽  
Quantitative Analysis Method

Information learnt from spectra at room temperature is transferred to assist in building a better regression model at high temperature.

scholarly journals Analysis models for variables associated with breastfeeding duration

2013 ◽  
Vol 31 (3) ◽  
pp. 306-314 ◽  
Author(s):  
Edson Theodoro dos S. Neto ◽  
Eliana Zandonade ◽  
Adauto Oliveira Emmerich
Keyword(s):  
Logistic Regression ◽  
Protective Factors ◽  
Regression Model ◽  
Odds Ratio ◽  
Regression Models ◽  
Adjusted Odds Ratio ◽  
Cox Regression ◽  
Bottle Feeding ◽  
Breastfeeding Duration ◽  
Factors Associated

OBJECTIVE To analyze the factors associated with breastfeeding duration by two statistical models. METHODS A population-based cohort study was conducted with 86 mothers and newborns from two areas primary covered by the National Health System, with high rates of infant mortality in Vitória, Espírito Santo, Brazil. During 30 months, 67 (78%) children and mothers were visited seven times at home by trained interviewers, who filled out survey forms. Data on food and sucking habits, socioeconomic and maternal characteristics were collected. Variables were analyzed by Cox regression models, considering duration of breastfeeding as the dependent variable, and logistic regression (dependent variables, was the presence of a breastfeeding child in different post-natal ages). RESULTS In the logistic regression model, the pacifier sucking (adjusted Odds Ratio: 3.4; 95%CI 1.2-9.55) and bottle feeding (adjusted Odds Ratio: 4.4; 95%CI 1.6-12.1) increased the chance of weaning a child before one year of age. Variables associated to breastfeeding duration in the Cox regression model were: pacifier sucking (adjusted Hazard Ratio 2.0; 95%CI 1.2-3.3) and bottle feeding (adjusted Hazard Ratio 2.0; 95%CI 1.2-3.5). However, protective factors (maternal age and family income) differed between both models. CONCLUSIONS Risk and protective factors associated with cessation of breastfeeding may be analyzed by different models of statistical regression. Cox Regression Models are adequate to analyze such factors in longitudinal studies.

A Unifying Classification for Functional Regression Modeling

2018 ◽  
Author(s):  
Frédéric Ferraty ◽  
Philippe Vieu
Keyword(s):  
Regression Model ◽  
Regression Models ◽  
Functional Model ◽  
Additive Functional ◽  
Regression Modeling ◽  
Functional Regression ◽  
Regression Problem ◽  
Nonparametric Models ◽  
Index Model ◽  
Functional Linear Regression

This article presents a unifying classification for functional regression modeling, and more specifically for modeling the link between two variables X and Y, when the explanatory variable (X) is of a functional nature. It first provides a background on the proposed classification of regression models, focusing on the regression problem and defining parametric, semiparametric, and nonparametric models, and explains how semiparametric modeling can be interpreted in terms of dimension reduction. It then gives four examples of functional regression models, namely: functional linear regression model, additive functional regression model, smooth nonparametric functional model, and single functional index model. It also considers a number of new models, directly adapted to functional variables from the existing standard multivariate literature.

scholarly journals Genotypic parameters for egg production in pure breed hens by using random regression model

2017 ◽  
Vol 47 (5) ◽  
Author(s):  
Priscila Becker Ferreira ◽  
Paulo Roberto Nogara Rorato ◽  
Fernanda Cristina Breda ◽  
Vanessa Tomazetti Michelotti ◽  
Alexandre Pires Rosa ◽  
...  
Keyword(s):  
Regression Model ◽  
Regression Models ◽  
Egg Production ◽  
Random Regression ◽  
Broad Sense Heritability ◽  
Residual Matrix ◽  
Random Regression Model ◽  
Residual Covariance ◽  
Genotypic Correlations ◽  
Selection Of

ABSTRACT: This study aimed to test different genotypic and residual covariance matrix structures in random regression models to model the egg production of Barred Plymouth Rock and White Plymouth Rock hens aged between 5 and 12 months. In addition, we estimated broad-sense heritability, and environmental and genotypic correlations. Six random regression models were evaluated, and for each model, 12 genotypic and residual matrix structures were tested. The random regression model with linear intercept and unstructured covariance (UN) for a matrix of random effects and unstructured correlation (UNR) for residual matrix adequately model the egg production curve of hens of the two study breeds. Genotypic correlations ranged from 0.15 (between age of 5 and 12 months) to 0.99 (between age of 10 and 11 months) and increased based on the time elapsed. Egg production heritability between 5- and 12-month-old hens increased with age, varying from 0.15 to 0.51. From the age of 9 months onward, heritability was moderate with estimates of genotypic correlations higher than 90% at the age of 10, 11, and 12 months. Results suggested that selection of hens to improve egg production should commence at the ninth month of age.

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