Pitman Nearness Comparison of the Traditional Estimator of the Coefficient of Determination and Its Adjusted Version in Linear Regression Models

2005 ◽  
Vol 34 (2) ◽  
pp. 367-374 ◽  
Author(s):  
JEROME P. KEATING ◽  
ROBERT L. MASON
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Coefficient Of Determination ◽  
Linear Regression Models ◽  
Pitman Nearness

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 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 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 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

scholarly journals A Method for Fault Detection and Diagnostics in Ventilation Units Using Virtual Sensors

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3931 ◽  
Author(s):  
Claudio Mattera ◽  
Joseba Quevedo ◽  
Teresa Escobet ◽  
Hamid Shaker ◽  
Muhyiddine Jradi
Keyword(s):  
Energy Consumption ◽  
Linear Regression ◽  
Fault Detection ◽  
Regression Models ◽  
Fault Tolerant ◽  
Coefficient Of Determination ◽  
Linear Regression Models ◽  
Virtual Sensors ◽  
Correct Operation ◽  
Acceptable Range

Buildings represent a significant portion of global energy consumption. Ventilation units are complex components, often customized for the specific building, responsible for a large part of energy consumption. Their faults impact buildings’ energy efficiency and occupancy comfort. In order to ensure their correct operation, proper fault detection and diagnostics methods must be applied. Hardware redundancy, an effective approach to detect faults, leads to increased costs and space requirements. We propose exploiting physical relations inside ventilation units to create virtual sensors from other sensors’ readings, introducing redundancy in the system. We use two different measures to detect when a virtual sensor deviates from the physical one: coefficient of determination for linear models, and acceptable range. We tested our method on a real building at the University of Southern Denmark, developing three virtual sensors: temperature, airflow, and fan speed. We employed linear regression models, statistical models, and non-linear regression models. All models detected an anomalous strong oscillation in the temperature sensors. Readings fell outside the acceptable range and the coefficient of determination dropped. Our method showed promising results by introducing redundancy in the system, which can benefit several applications, such as fault detection and diagnostics and fault-tolerant control. Future work will be necessary to discover thresholds and set up automatic fault detection and diagnostics.

The coefficient of determination and its adjusted version in linear regression models

1995 ◽  
Vol 14 (2) ◽  
pp. 229-240 ◽  
Author(s):  
Anil K. Srivastava ◽  
Virendra K. Srivastava ◽  
Aman Ullah
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Coefficient Of Determination ◽  
Linear Regression Models

scholarly journals Modelling the Impact of Key Pests of Watermelon on its Performance Using Linear Regression Models

2021 ◽  
Vol 18 (7) ◽  
Author(s):  
Emmanuel OKRIKATA ◽  
Emmanuel Oludele OGUNWOLU ◽  
Ngozi Ifeoma ODIAKA
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Field Experiments ◽  
Insect Pests ◽  
Block Design ◽  
Coefficient Of Determination ◽  
Linear Regression Models ◽  
Crop Performance ◽  
Student’S T ◽  
The Impact

Despite the economic, health, and nutritional values of watermelon, insect pests remain a key limitation to its production globally. However, there has, hardly been any research that has statistically modeled the impact of insect pests on its performance. Therefore, this study aims to determine the relationship between the performance of watermelon and the density of its key pests with the aid of correlation and linear regression models, thereby presenting models for forecasting crop performance vis-à-vis pest density for optimum pest management. Data were collected from 40 m2 plots grouped into 4 replicates (10 plots/replicate) in field experiments (arranged in a randomized complete block design) in the early- and late-sown crops of 2016 and 2017 in the Research Farm of Federal University, Wukari, Nigeria. Plant survival rate (%) negatively and significantly (P ≤ 0.05) correlated with each of mean number leaf-feeding beetles (r = −0.80, R2 = 63.5 %, Y = 92.023 – 3.145x; r = −0.79, R2 = 62.1 %, Y = 95.986 – 5.975x), A. gossypii density (r = −0.67, R2 = 44.9 %, Y = 184.048 – 50.444x; r = -0.65, R2 = 42.4 %, Y = 131.852 – 14.618x), and B tabaci density (r = −0.67, R2 = 45.2 %, Y = 188.832 – 11.138x; r = −0.66, R2 = 43.3 %, Y = 178.738 – 3.701x) in both the early- and late-sown crop of 2016, respectively, with a similar trend in those of 2017. All parameters significantly (P ≤ 0.05) fitted the linear regression model. Densities of all major pests consistently correlated negatively and significantly with fruit yield. Student’s t-test detected significant differences between the early- and late-sown crops of both years. We therefore conclude that watermelon experiences multiple pest infestations whose compositions and intensities vary between seasons, and that their influence on agronomic performance, as shown by the coefficient of determination (R2) values (which were indicative of the reliability of the models with respect to the effect of pests on crop performance), were largely close or > 50 %.

FAKTOR DETERMINAN PENYALURAN KREDIT BANK PERSERO

2018 ◽  
Vol 23 (1) ◽  
pp. 60-71
Author(s):  
Wigiyanti Masodah
Keyword(s):  
Interest Rate ◽  
Linear Regression ◽  
Interest Rates ◽  
Regression Models ◽  
Linear Regression Models ◽  
Negative Impacts ◽  
Main Activity ◽  
The Impact ◽  
The Given ◽  
Multiple Linear Regression Models

Offering credit is the main activity of a Bank. There are some considerations when a bank offers credit, that includes Interest Rates, Inflation, and NPL. This study aims to find out the impact of Variable Interest Rates, Inflation variables and NPL variables on credit disbursed. The object in this study is state-owned banks. The method of analysis in this study uses multiple linear regression models. The results of the study have shown that Interest Rates and NPL gave some negative impacts on the given credit. Meanwhile, Inflation variable does not have a significant effect on credit given. Keywords: Interest Rate, Inflation, NPL, offered Credit.

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