An Alternative Multicollinearity Approach in Solving Multiple Regression Problem

Zainodin, H.J.; Noraini, Alifah; Yap, S.J.

doi:10.3923/tasr.2011.1241.1255

Cited by 29 publications

(28 citation statements)

References 8 publications

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“…The proposed equation also helps to save tremendous 3,4 helps to countercheck the number of parameters left in amount of time, where there are analysis involving the selected model which is free from multicollinearity complex iterations or repeated tasks, especially i n and from insignificant variable. It presented a equation to software development.…”

Section: ---------------------------------------------------------------mentioning

confidence: 99%

Number of Parameters Counting in a Hierarchically Multiple Regression Model

Zainodin¹,

Abdullah²,

Yap³

2014

Science International

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Background: Manually, when a dependent variable is affected by a large number of independent variables, the number of parameters helps researchers determine the number of independent variables to be considered in an analysis. However, when there are many parameters to be estimated in a model, the manual counting is tedious and time consuming. Thus, this study derives a method to determine the number of parameters systematically in a model. Methods: The model building procedure in this study involves removing variables due to multicollinearity and insignificant variables. Eventually, a selected model is obtained with significant variables. Results: The findings of this study would enable researchers to count the number of parameters in a resulting model (selected model) with ease and speed. On top of that, models which fulfill the assumption are considered in the statistical analysis. In addition, human errors caused by manual counting can also be minimised and avoided by implementing the proposed procedure. Conclusion: These findings will also undoubtedly help many researchers save time when their analyses involve complex iterations.

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Section: ---------------------------------------------------------------mentioning

confidence: 99%

Number of Parameters Counting in a Hierarchically Multiple Regression Model

Zainodin¹,

Abdullah²,

Yap³

2014

Science International

Self Cite

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“…Since high correlation coefficients of absolute values in the range of |r| = 0.75 are considered to exhibit multicollinearity effects, these multicollinearity source variables have to be removed as illustrated by the case types of the Zainodin-Noraini remedial techniques in . 13 In this study, multicollinearity source variables with high correlation coefficient of absolute values greater than 0.95 (i.e. |r| = 0.95) are to be removed.…”

Section: Data Preparation and Modelling Proceduresmentioning

confidence: 99%

“…The eliminations of insignificant 13 variables from the models are then perfomed using the backward elimination method as illustrated by .…”

Section: Multicollinearity Remedials and Insignificant Variables Remomentioning

confidence: 99%

A Polynomial Stem Volume Estimation on Cinnamon Wood For Cinnamomum Extracts of Aldehyde Content

Abdullah¹,

Zainodin²

2014

Science International

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Background:The potentials of cinnamon wood, extracts and oils in a variety of industries and their applications are undeniable. As a natural and safe alternative, the need of an estimation tool is vital. Hence, this study proffers to outline the estimation processes involving the parameters of the stem volume of cinnamon wood with the tree mensuration variables using the polynomial regression technique. Methods: Polynomial models with the auxiliary variables are considered up to the first order of interactions and polynomial characterization of the quadratic terms. Nonlinearity of data variables exhibit the need of data conversion into generated variables. Preliminary, data normality using the maximum p-value approach is executed. Results: Modelling procedures are performed on all polynomial models and comparisons are made based on the eight selection criteria (8SC). The best polynomial model is identified when it satisfies all of the minimum values of the eight selection criteria. Conclusion: The best polynomial model is found to have a quadratic term with direct and positive contributions from the diameters at the base, middle and tree height and inversely from the diameter at the top. Interaction factors are contributed by the diameters at the top, breast height and the generated variable. The estimation efficiency of 6.442% has signified good predictive capability towards stem volume estimation.

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“…The interactions of the single independent variables (X 1 X 2 -first order; X 1 X 2 X 3 -second order; X 1 X 2 X 3 X 4 -third order) denote the product of these variables, and can be rewritten as X 12 , X 123 and X 1234 respectively [5]. All the possible models would undergo through the Global test as shown by [11].…”

Section: Study Areamentioning

confidence: 99%

“…The selected models of Phase 2 will then undergo two tests, namely, the correlation based multicollinearity test and the coefficient test at  = 5% significant level. Each variable removal due to multicollinearity, and elimination of insignificant variables from the model using the coefficient test would be denoted in the model labelling [12] as shown in Fig. 2.…”

Section: Study Areamentioning

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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An Alternative Multicollinearity Approach in Solving Multiple Regression Problem

Cited by 29 publications

References 8 publications

Number of Parameters Counting in a Hierarchically Multiple Regression Model

Number of Parameters Counting in a Hierarchically Multiple Regression Model

A Polynomial Stem Volume Estimation on Cinnamon Wood For Cinnamomum Extracts of Aldehyde Content

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

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