Improved Stem Volume Estimation using P-Value Approach In Polynomial Regression Models

Abdullah, Noraini; Jubok, Zainodin Hj.; Ahmed, Amran

doi:10.3923/rjf.2011.50.65

Cited by 12 publications

(5 citation statements)

References 14 publications

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“…Phase 2 of model-building is then carried out on the possible models of the transformed variables. The multicollinearity test is carried out on all the models as illustrated by [7], and followed by the coefficient test as shown by [9]. This has resulted in 34 selected models being obtained for Model I.…”

Section: Resultsmentioning

confidence: 99%

Palm Oil to Biodiesel: Determination Factors on Production and Export Volume

Abdullah¹,

Zainodin²,

Amir³

2015

JOCET

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Abstract-In this study, determination factors that affect the production volume of oil palm fruits and the export volume of biodiesel are identified using the multiple regression technique, and results using model transformation method are compared. For this purpose, two models are developed, namely, Model I is for the production volume of oil palm fruits, while Model II is for the export volume of biodiesel. Data variables are transformed using the ladder of power transformation method. There are five independent variables in Model I and four independent variables in Model II. The four phases in multiple regression model-building are carried out on both Model I and II respectively. Model I will use the model transformation method to change the data from non-normality to normality. The best model obtained for Model I by model transformation is M72.2.5. The main factor is the total workers employed during last pay period, and the interaction factors up to order two are: harvested area interact with yield per hectare, harvested area interact with local delivery average price, harvested area interact with total workers employed during last pay period, yield per hectare interact with local delivery average price, harvested area interact with local delivery average price interact with total workers employed during last pay period and yield per hectare interact with local delivery average price interact with total workers employed during last pay period. The MAPE value for best model on model transformation is 2.62%. Therefore, the best model using the model transformation method is said to be acceptable to forecast for the production volume. The best model M72.2.5 is then used to obtain Model II which is given by M2.0.0. The main factor is the export volume of biodiesel.

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Section: Resultsmentioning

confidence: 99%

Palm Oil to Biodiesel: Determination Factors on Production and Export Volume

Abdullah¹,

Zainodin²,

Amir³

2015

JOCET

Self Cite

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“…Multicollinearity occur if Correlation Coefficient is greater than 0.95. Zainodin-Noraini multicollinearity alterative strategies has been implement in this study [1] [2].…”

Section: Phase 21 Multicollinearity Testmentioning

confidence: 99%

Single Order of Multiple Regression Model of Water Quality Index (WQI) in Manjung River and its Tributaries

Yahaya¹,

Salih²,

Zarina³

et al. 2019

IJITEE

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This research highlights a multi-variety technique to examine the relationship between dependent and independent variable in predicting the water quality index in Manjung Rivers and its affluents. The model building process been used to analyse and generate the data. There are 63 possible models for single order multiple regressions. The number of possible model started to reduce as we started to eliminate insignificant variable. This model then needs to run under eight selection criteria to identify the best model. The best model will be certified by using Mean absolute percentage error (MAPE) in order to measure the validity of the model.

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“…To assess models, we need a model selection. Model selection was also made by different researchers, Abdullah et al [33] used eight selection criteria (8SC) to obtain the best model among all possible models. Similarly, Javaid et al used in model selection problem [15,[34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Accurate and Hybrid Regularization - Robust Regression Model in Handling Multicollinearity and Outlier Using 8SC for Big Data

Mukhtar,

Ali,

Javaid

et al. 2021

MMEP

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Regressions have been continuously received great attention. However, there are still open issues in regression, and two of the issues is regression with multicollinearity and outlier. Regularization (Ridge, Lasso, and Elastic Net) techniques implement a means to control regression coefficients. The methods can decrease the variance and reduce our sample error for tackle multicollinearity. In robust regression, it is a form of regression method designed to overcome outliers. Robust regression is an important method for analyzing data that are infected with outliers. The data have been interacted on the second order interaction. The data contained 435 different independent interaction variables. The primary focus of this paper is to analyze and compare the impact of three different variable selection techniques regularization regression algorithms for the data seaweed drying. After that, it will be analyzed through robust regression (Tukey Bi-Square, Hampel, and Huber). As the result, the Lasso-Hampel was better than others with the MAE (4.09641), RMSE (5.275992), MAPE (7.9962), SSE (182491.2), R-square (0.6514791), and R-square Adjusted (0.649279). The method of Lasso-Hampel is able to be relied on investigation of the accuracy in big data obtained from regularization and robust regression.

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Improved Stem Volume Estimation using P-Value Approach In Polynomial Regression Models

Cited by 12 publications

References 14 publications

Palm Oil to Biodiesel: Determination Factors on Production and Export Volume

Palm Oil to Biodiesel: Determination Factors on Production and Export Volume

Single Order of Multiple Regression Model of Water Quality Index (WQI) in Manjung River and its Tributaries

Accurate and Hybrid Regularization - Robust Regression Model in Handling Multicollinearity and Outlier Using 8SC for Big Data

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