Perbandingan Metode Kuadrat Terkecil Dan Metode Bayes Pada Model Regresi Linier Berganda Yang Mengandung Multikolinieiritas

Auqino, Siska; Maiyastri, Maiyastri; Diana, Rita

doi:10.25077/jmu.8.1.307-312.2019

Cited by 2 publications

(4 citation statements)

References 2 publications

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“…One of the methods for estimating multiple linear regression parameters is the Least Squares Method (Auqino et al, 2019). In general, the multiple linear regression model is:…”

Section: Least Squares Methodsmentioning

confidence: 99%

“…Multicollinearity was first introduced by Ragnar Frisch in 1934. It is the presence of a definite or exact linear relationship between some or all of the predictor variables in a multiple linear regression model (Auqino et al, 2019).…”

Section: Multicollinearitymentioning

confidence: 99%

“…This can make it difficult to interpret the predicted regression coefficients effectively when employing the least squares method since they become unstable (Lavery et al, 2019). The presence of multicollinearity cases causes the standard error (SE) of the regression parameters to increase, so an alternative method is needed for estimating multiple linear regression parameters (Auqino et al, 2019). According to (Adeboye et al, 2014) multicollinearity affects the standard error of the regression coefficient estimate, so the estimation results may not be accurate.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The Application of the Least Squares Method to Multicollinear Data

Harefa¹,

Zega²,

Mendrofa³

2023

IJMSS

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Regression analysis is an analysis that aims to determine whether there is a statistically dependent relationship between two variables, namely the predictor variable and the response variable. One of the methods for estimating multiple linear regression parameters is the Least Squares Method. Therefore, careful and meticulous analysis and selection of appropriate techniques are required to overcome the multicollinearity problem and ensure accurate and meaningful regression analysis results. Descriptive statistical table of response variables and predictor variables, where the average results are rounded. The regression equation using the OLS method is as follows: Y ̂=2,037+0.302X_1+0.206X_2+0.172X_3+0.342X_4. Therefore, it is important to use special techniques such as regularization or PCA to overcome the multicollinearity problem in the data before applying the least squares method. Thus, we can obtain more stable and accurate regression coefficient estimates and a more reliable linear regression model.

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“…One of the methods for estimating multiple linear regression parameters is the Least Squares Method (Auqino et al, 2019). In general, the multiple linear regression model is:…”

Section: Least Squares Methodsmentioning

confidence: 99%

Section: Multicollinearitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Application of the Least Squares Method to Multicollinear Data

Harefa¹,

Zega²,

Mendrofa³

2023

IJMSS

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“…Teorima bayes adalah metode yang baik di dalam mesin pembelajaran (Bangun, 2019;Tarigan et al, 2022) berdasarkan data training, dengan menggunakan probabilitas bersyarat sebagai dasarnya (Setiawan et al, 2020;Sitepu, 2019). Metode Bayes juga merupakan suatu metode untuk menghasilkan estimasi parameter (Auqino et al, 2019;Rahmawati & Budiarti, 2021) dengan menggabungkan informasi dari sampel dan informasi lain yang telah tersedia sebelumnya, dan salah satu cara untuk mengatasi ketidakpastian (Arif et al, 2021;Chandra et al, 2020;Sudiarto, 2018;Wulandari et al, 2019).…”

Section: Pendahuluanunclassified

Penerapan Teorema Bayes pada Sistem Pakar Pendeteksi Penyakit Domba

Andika¹,

Maharani²,

Mardalius³

2022

Edumatic

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Sheep are one of the livestock that can provide benefits such as meat, milk, and skin which are used as a source of income for the community, especially in the Asahan regency area. However, still, the farmer's knowledge in terms of care and knowledge of sheep diseases can result in the sheep being able to die. The purpose of this study was to create an expert system to diagnose sheep disease using the theory of Bayes. The waterfall is used as a model in this study with stages, analysis, design, implementation, and testing. analysis was carried out to find the required data using observations and interviews. The design of this system consists of Unified Modeling Language (use case ), diagram classes, flowcharts, and interfaces. Testing the system uses a black box which aims to find out the functionality of the system that has been implemented, and data analysis using the theory of Bayes. Our findings resulted in bayes having an expert system for the diagnosis of diseases in sheep. The results of Bayes analysis with the theory of Bayes showed that the disease experienced in sheep is deworming with a probability level of 60.71%. The results of the black box test show that the functionality of this system is already running well

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Perbandingan Metode Kuadrat Terkecil Dan Metode Bayes Pada Model Regresi Linier Berganda Yang Mengandung Multikolinieiritas

Cited by 2 publications

References 2 publications

The Application of the Least Squares Method to Multicollinear Data

The Application of the Least Squares Method to Multicollinear Data

Penerapan Teorema Bayes pada Sistem Pakar Pendeteksi Penyakit Domba

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