Number of Parameters Counting in a Hierarchically Multiple Regression Model

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

doi:10.17311/sciintl.2014.37.43

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…The technique for number of parameters counting can be found in Zainodin et al (2014). Lastly, the model"s goodness of fit was tested using the randomness and normality tests.…”

Section: Resultsmentioning

confidence: 99%

Motivation and Medium of Information Affecting Behavioural Patterns of Film Viewers in Malaysia

Hassan

Abdullah

Zainodin

et al. 2015

Humanities and Social Sciences Letters

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This paper is aimed to identify the key factors on behavioural patterns that affect audiences in making decision to watch films by using the techniques in multiple regressions (MR). The success of a country's film industry is determined by the collaboration between film producers and audiences. Audiences are a major contributor that will determine the success of a film, while producers is the main supplier to provide films to the audiences. Mathematical modelling concept is used in the methodology where two categorical variables, selected after factor analysis, are transformed into dummies. The search for significant factors would involve data cleaning, factor analysis, dummy transformation and four-phase model building. The modelbuilding phases will incorporate the procedures of selecting the best model involving multicollinearity test using the variance-based approach (VIF) below 5. Results revealed that the best model obtained consists of factors on interest, family, friends, the internet and printed medium of posters and brochures which are influential in attracting audiences. This study has therefore extended our knowledge to build bridges between two different fields, that is scientific and non scientific through the concept and methodology of modelling, and consequently, help in advertising and marketing of the film industry by influencing the perceptions of audiences towards cinemas. Contribution/ OriginalityThis study uses new estimation methodology in Social Sciences where significant factors in real-world problems are identified; in this paper, behavioral patterns of film viewers in Malaysia.The mathematical modelling approach with multicollinearity removals and elimination of Humanities and Social Sciences Letters insignificant factors, besides other statistical procedures and tests are incorporated for model"s robustness.

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“…The technique for number of parameters counting can be found in Zainodin et al (2014). Lastly, the model"s goodness of fit was tested using the randomness and normality tests.…”

Section: Resultsmentioning

confidence: 99%

Motivation and Medium of Information Affecting Behavioural Patterns of Film Viewers in Malaysia

Hassan

Abdullah

Zainodin

et al. 2015

Humanities and Social Sciences Letters

Self Cite

View full text Add to dashboard Cite

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“…For illustration, consider a model M12, with Y =β0 +β1X1 +β3X3 +β6X6 +β13X13 +β16X16 +β36X36 +β136X136 +u where u is the random error variable. To complete the discussion, Zainodin et al [10] suggested that there are 8 number of parameters involved in the model (where g =3, h =0 and v =2). With the suggested equation…”

Section: Volume 5 Number 2 April 2015mentioning

confidence: 99%

Selected Model Systematic Sequence via Variance Inflationary Factor

Zainodin¹,

Khuneswari²,

Noraini³

et al. 2015

IJAPM

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Literature reviews revealed that multicollinearity always exists when model a deals with several independent variables. This phenomenon can cause the t statistic and the related probability-value to give a misleading impression of the importance of the independent variables. There are two approaches in tackling this issue. The common approach is correlation-coefficient based and the other is variance-based. Many softwares in the market have highlighted this phenomenon and offer options in minimising the effect. Currently, the variance-based approach is widely available in the software market. This is because it does not depend on the type of dependent variables. This variance-based approach via Variance Inflation Factor (VIF) quantifies the severity of multicollinearity in an ordinary least squares regression analysis. It provides an index that measures how much the variance (the square of the estimate's standard deviation) of an estimated regression coefficient is increased because of collinearity. Thus, here, a novel approach is revealed in detailing the procedures to remove several variables due to multicollinearity effects. Ultimately, the insignificant variables are eliminated. It is found that when a very stringent criterion is set for multicollinearity, the process of elimination of variables becomes smooth and easy besides shortening the number of iteration.

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Number of Parameters Counting in a Hierarchically Multiple Regression Model

Cited by 2 publications

References 12 publications

Motivation and Medium of Information Affecting Behavioural Patterns of Film Viewers in Malaysia

Motivation and Medium of Information Affecting Behavioural Patterns of Film Viewers in Malaysia

Selected Model Systematic Sequence via Variance Inflationary Factor

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