scholarly journals Regression models for estimating chick hatchling weight from some egg geometry traits

2018 ◽  
Vol 34 (3) ◽  
pp. 323-334
Author(s):  
Nadya Mincheva ◽  
Mitko Lalev ◽  
Magdalena Oblakova ◽  
Pavlina Hristakieva
Keyword(s):  
Linear Regression ◽  
Surface Area ◽  
Regression Models ◽  
Linear Models ◽  
Geometric Mean ◽  
Coefficient Of Determination ◽  
Regression Equations ◽  
Linear Regression Models ◽  
Egg Weight ◽  
Mean Diameter

The prediction of chicks? weight before hatching is an important element of selection, aimed at improving the uniformity rate and productivity of birds. With this regards, our goal was to develop and evaluate optimum models for similar prediction in two White Plymouth Rock chickens lines - line L and line K on the basis of the incubation egg weight and egg geometry characteristics - egg maximum breadth (B), egg length (L), geometric mean diameter (Dg), egg volume (V), egg surface area (S). A total of 280 eggs (140 from each line) laid by 40-weekold hens were randomly selected. Mean arithmetic values, standard deviations and coefficients of variation of studied parameters were determined for each line. Correlation coefficients between the weight of hatchlings and predictors were the highest for egg weight, geometric mean diameter, volume and surface area of eggs (r=0.731-0.779 for line L; r=0.802-0.819 for line ?). Nine linear regression models were developed and their accuracy evaluated. The regression equations of hatchlings? weight vs egg length had the lowest coefficient of determination (0.175 for line K and 0.291 for line L), but when egg length and breadth entered the model together, its value increased significantly up to 0.541 and 0.665 for lines L and K, respectively. The weight of day-old chicks from line L could be predicted with higher accuracy with a model involving egg surface area apart egg weight (ChW=0.513EW+0.282S - 10.345; R2=0.620). In line ? a more accurate prognosis was attained by adding egg breadth as an additional predictor to the weight in the model (ChW=0.587EW+0.566? - 19.853; R2=0.692). The study demonstrated that multiple linear regression models were more precise that single linear models.

scholarly journals Non-Linear Regression Models with Vibration Amplitude Optimization Algorithms in a Microturbine

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 130
Author(s):  
Omar Rodríguez-Abreo ◽  
Juvenal Rodríguez-Reséndiz ◽  
L. A. Montoya-Santiyanes ◽  
José Manuel Álvarez-Alvarado
Keyword(s):  
Linear Regression ◽  
Vibration Amplitude ◽  
Regression Models ◽  
Engineering Problem ◽  
Coefficient Of Determination ◽  
Model Parameters ◽  
Gray Wolf ◽  
Linear Regression Models ◽  
Machinery Condition Monitoring ◽  
Non Linear

Machinery condition monitoring and failure analysis is an engineering problem to pay attention to among all those being studied. Excessive vibration in a rotating system can damage the system and cannot be ignored. One option to prevent vibrations in a system is through preparation for them with a model. The accuracy of the model depends mainly on the type of model and the fitting that is attained. The non-linear model parameters can be complex to fit. Therefore, artificial intelligence is an option for performing this tuning. Within evolutionary computation, there are many optimization and tuning algorithms, the best known being genetic algorithms, but they contain many specific parameters. That is why algorithms such as the gray wolf optimizer (GWO) are alternatives for this tuning. There is a small number of mechanical applications in which the GWO algorithm has been implemented. Therefore, the GWO algorithm was used to fit non-linear regression models for vibration amplitude measurements in the radial direction in relation to the rotational frequency in a gas microturbine without considering temperature effects. RMSE and R2 were used as evaluation criteria. The results showed good agreement concerning the statistical analysis. The 2nd and 4th-order models, and the Gaussian and sinusoidal models, improved the fit. All models evaluated predicted the data with a high coefficient of determination (85–93%); the RMSE was between 0.19 and 0.22 for the worst proposed model. The proposed methodology can be used to optimize the estimated models with statistical tools.

scholarly journals Valuation of Large Variable Annuity Portfolios Using Linear Models with Interactions

2018 ◽  
Author(s):  
Guojun Gan
Keyword(s):  
Linear Regression ◽  
Life Insurance ◽  
Predictive Models ◽  
Regression Models ◽  
Linear Models ◽  
Insurance Companies ◽  
Linear Regression Models ◽  
Insurance Product ◽  
Valuation Process ◽  
Speed Up

A variable annuity is a popular life insurance product that comes with financial guarantees. Using Monte Carlo simulation to value a large variable annuity portfolio is extremely time-consuming. Metamodeling approaches have been proposed in the literature to speed up the valuation process. In metamodeling, a metamodel is first fitted to a small number of variable annuity contracts and then used to predict the values of all other contracts. However, metamodels that have been investigated in the literature are sophisticated predictive models. In this paper, we investigate the use of linear regression models with interaction effects for the valuation of large variable annuity portfolios. Our numerical results show that linear regression models with interactions are able to produce accurate predictions and can be useful additions to the toolbox of metamodels that insurance companies can use to speed up the valuation of large VA portfolios.

scholarly journals ANALISIS PENGARUH FUNGSI INTERMEDIASI PERBANKAN TERHADAP PERTUMBUHAN EKONOMI DI INDONESIA TAHUN 2008 - 2011

2013 ◽  
Vol 11 (2) ◽  
pp. 147
Author(s):  
. Anjarwati
Keyword(s):  
Economic Growth ◽  
Interest Rate ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Regression Models ◽  
Coefficient Of Determination ◽  
Test Results ◽  
Linear Regression Models ◽  
Independent Variables ◽  
Multiple Linear Regression Models

This study aims to analyze the development of intermediation to economic growth in Indonesia and a significant test whether or not the effect of intermediation on economic growth. From the test results obtained by the coefficient of determination (R2) for multiple linear regression models for 0.916. It means that the independent variables can explain the variation in the dependent variable 91.6% together, then variable t can be seen that variable Interest Rate Loans and lending have a significant effect on economic growth, It is proved that t-count > t-table. Lending to the variable (X1) 7,944 t > t table 2.026, and for variable Interest Rate Loans (X2) 4.521 t-count > t table 2.026. From the analysis has been conducted simultaneously indicates that those independent variables have a significant effect on economic growth, with simultaneous F test results are calculated F value > F 204.012> 3.25. it means that Ho is rejected.

scholarly journals Valuation of Large Variable Annuity Portfolios Using Linear Models with Interactions

Risks ◽  
2018 ◽  
Vol 6 (3) ◽  
pp. 71 ◽  
Author(s):  
Guojun Gan
Keyword(s):  
Linear Regression ◽  
Life Insurance ◽  
Predictive Models ◽  
Regression Models ◽  
Linear Models ◽  
Insurance Companies ◽  
Linear Regression Models ◽  
Insurance Product ◽  
Valuation Process ◽  
Speed Up

A variable annuity is a popular life insurance product that comes with financial guarantees. Using Monte Carlo simulation to value a large variable annuity portfolio is extremely time-consuming. Metamodeling approaches have been proposed in the literature to speed up the valuation process. In metamodeling, a metamodel is first fitted to a small number of variable annuity contracts and then used to predict the values of all other contracts. However, metamodels that have been investigated in the literature are sophisticated predictive models. In this paper, we investigate the use of linear regression models with interaction effects for the valuation of large variable annuity portfolios. Our numerical results show that linear regression models with interactions are able to produce accurate predictions and can be useful additions to the toolbox of metamodels that insurance companies can use to speed up the valuation of large VA portfolios.

scholarly journals Estimation of Causal Functional Linear Regression Models

2017 ◽  
Vol 9 (6) ◽  
pp. 106
Author(s):  
J.C.S. De Miranda
Keyword(s):  
Linear Regression ◽  
Tensor Product ◽  
Linear Regression Model ◽  
Regression Models ◽  
Linear Models ◽  
Linear Regression Models ◽  
Functional Responses ◽  
Functional Linear Models ◽  
Triangular Region ◽  
Functional Linear Regression

We present a methodology for estimating causal functional linear models using orthonormal tensor product expansions. More precisely, we estimate the functional parameters $\alpha$ and $\beta$ that appear in the causal functional linear regression model:$$\mathcal{Y}(s)=\alpha(s)+\int_a^b\beta(s,t)\mathcal{X}(t)\mathrm{d}t+\mathcal{E}(s),$$ where  $\mbox{supp } \beta \subset \mathfrak{T},$ and $\mathfrak{T}$ is the closed triangular region whose vertexes are $(a,a) , (b,a)$ and $(b,b).$ We assume we have an independent sample $\{ (\mathcal{Y}_k,\mathcal{X}_k) : 1\le k \le N, k\in \mathbb{N}\}$ of observations where the $\mathcal{X}_k $'s are functional covariates, the $\mathcal{Y}_k$'s are time order preserving functional responses and $\mathcal{E}_k,$ $1\le k \le N,$ is i.i.d. zero mean functional noise.

scholarly journals Physical Properties, Pasting Characteristics and Rheological Behaviour of Paddy Varieties Suitable for Flaking

2020 ◽  
Author(s):  
Dhritiman Saha ◽  
Arun Kumar T V ◽  
Swati Sethi ◽  
Indore Navnath
Keyword(s):  
Moisture Content ◽  
Physical Properties ◽  
Amylose Content ◽  
Geometric Mean ◽  
Brown Rice ◽  
Rheological Behaviour ◽  
Angle Of Repose ◽  
Coefficient Of Determination ◽  
Regression Equations ◽  
Mean Diameter

A study was conducted to find the effect of moisture content on various physical properties of paddy varieties (MTU-1010 and BB-11) suitable for flaking. The range of moisture studied was 11.11% to 28.2% (d.b.) for both the paddy varieties. The bulk density of the paddy increased from 514.76 kg/m3 to 563.62 kg/m3 for MTU-1010 variety and 605.28 kg/m3 to 632.62 kg/m3 for BB-11 variety respectively when the moisture content was increased in the experimental range. The sphericity increased from 0.386 to 0.399 for MTU-1010 variety and 0.448 to 0.458 for BB-11 variety with increase in moisture content. The true density and porosity decreased with the increase in moisture content of paddy. The other physical properties such as test weight, surface area, arithmetic mean diameter, geometric mean diameter and angle of repose increased with the increase in moisture content of paddy. The static coefficient of friction of paddy increased with the increase in moisture content on different surfaces e.g. wood, mild steel, and galvanized iron. The regression equations for all the response variables were significant at P < 0.05 with coefficient of determination, R2 (> 0.90). The amylose content in MTU-1010 and BB-11 variety was found to be 30.23% and 26.32% respectively indicating that high amylose containing paddy varieties are generally used for flaking paddy. Further, the pasting properties of both the paddy varieties revealed higher pasting temperature in paddy variety MTU-1010 than BB-11. The rheological studies highlighted higher storage modulus of brown rice than the polished rice for both the paddy varieties.

scholarly journals METHODS OF THE ESTIMATED TWO-PARAMETER LINEAR REGRESSION MODELSFOR DIAGNOSIS OF BRONCHIAL ASTHMA SEVERITY IN CHILDREN

2021 ◽  
pp. 23-30
Author(s):  
PIHNASTYI OLEH MYKHAILOVYCH ◽  
KOZHYNA OLGA SERGEYEVNA
Keyword(s):  
Linear Regression ◽  
Bronchial Asthma ◽  
Regression Models ◽  
Linear Models ◽  
Asthma Severity ◽  
Linear Regression Models ◽  
Experimental Data Processing ◽  
Preliminary Experimental Data ◽  
Factor Regression ◽  
Two Parameter

Objectives: Prognostication of bronchial asthma severity in children by means of two-parameter regression models building. Methods: A clinical study of 70 children with bronchial asthma diagnosis of 6 to 18 years old was done.142 factors were analyzed and a degree of relationship among them was revealed. Single-factor regression models were used during preliminary experimental data processing. Results: The correlation connection between the value observed and the factors under research was revealed. The method of two-parameter linear models with a fair accuracy was developed. Conclusion: The suggested method of approximate two-parameter linear regression models can be used for preliminary analysis of medical research data where the value observed depends on a big number of loosely connected factors.

scholarly journals The Distribution of the Coefficient of determination in Linear Regression Model: A Review

2021 ◽  
Vol 23 (09) ◽  
pp. 126-127
Author(s):  
El Houssainy A. Rady ◽  
◽  
Ahmed Amin El-Sheikh ◽  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Regression Models ◽  
Error Term ◽  
Coefficient Of Determination ◽  
Linear Regression Models ◽  
Common Assumption ◽  
Normal Distributions ◽  
Error Terms

In this article, we review the different studies about the coefficient of determination in linear regression models and make a highlight about the inferences and the density function of the coefficient of determination which presented under the most common assumption when the error terms obey the normal distributions, and also analyzed the certain effects of departures from normality of the error term

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