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Full-Text Articles in Physical Sciences and Mathematics
Jmasm41: An Alternative Method For Multiple Linear Model Regression Modeling, A Technical Combining Of Robust, Bootstrap And Fuzzy Approach (Sas), Wan Muhamad Amir W Ahmad, Mohamad Arif Awang Nawi, Nor Azlida Aleng, Mohamad Shafiq
Jmasm41: An Alternative Method For Multiple Linear Model Regression Modeling, A Technical Combining Of Robust, Bootstrap And Fuzzy Approach (Sas), Wan Muhamad Amir W Ahmad, Mohamad Arif Awang Nawi, Nor Azlida Aleng, Mohamad Shafiq
Journal of Modern Applied Statistical Methods
Research on modeling is becoming popular nowadays, there are several of analyses used in research for modeling and one of them is known as applied multiple linear regressions (MLR). To obtain a bootstrap, robust and fuzzy multiple linear regressions, an experienced researchers should be aware the correct method of statistical analysis in order to get a better improved result. The main idea of bootstrapping is to approximate the entire sampling distribution of some estimator. To achieve this is by resampling from our original sample. In this paper, we emphasized on combining and modeling using bootstrapping, robust and fuzzy regression methodology. …
The Goldilocks Dilemma: Impacts Of Multicollinearity -- A Comparison Of Simple Linear Regression, Multiple Regression, And Ordered Variable Regression Models, Grayson L. Baird, Stephen L. Bieber
The Goldilocks Dilemma: Impacts Of Multicollinearity -- A Comparison Of Simple Linear Regression, Multiple Regression, And Ordered Variable Regression Models, Grayson L. Baird, Stephen L. Bieber
Journal of Modern Applied Statistical Methods
A common consideration concerning the application of multiple linear regression is the lack of independence among predictors (multicollinearity). The main purpose of this article is to introduce an alternative method of regression originally outlined by Woolf (1951), which completely eliminates the relatedness between the predictors in a multiple predictor setting.
Building Designers' Perception And The Effect On Sustainability In Malawi, Lloyd Ndau
Building Designers' Perception And The Effect On Sustainability In Malawi, Lloyd Ndau
Walden Dissertations and Doctoral Studies
Environmental sustainability in buildings is an important part of preserving the environment and reducing climate change. The increasing amount of physical infrastructure systems in Malawi has not been accompanied by policy-makers clearly understanding perceptions and attitudinal behaviors of building designers to promote environmental sustainability. Some building designers in Malawi might not be practicing sustainability innovations adequately, requiring more research to understand their perceptions and behaviors. The purpose of this mixed methods sequential and explanatory study was to explore how building designers' behaviors relate to the implementation of sustainability innovations in Malawi. Ajzen's theory of planned behavior explaining how attitudinal behaviors …
Tourism Demand Modelling And Forecasting Using Data Mining Techniques In Multivariate Time Series: A Case Study In Turkey, Selçuk Cankurt, Abdülhami̇t Subaşi
Tourism Demand Modelling And Forecasting Using Data Mining Techniques In Multivariate Time Series: A Case Study In Turkey, Selçuk Cankurt, Abdülhami̇t Subaşi
Turkish Journal of Electrical Engineering and Computer Sciences
In this study multiple linear regression, multilayer perceptron (MLP) regression, and support vector regression (SVR) are used to make multivariate tourism forecasting for Turkey. This paper is a comparative study of data mining techniques based on multivariate regression modelling with monthly data points to forecast tourism demand; it focuses on Turkey. Both MLP and SVR methods are widely employed in the variety forecasting problems. Most of the previous research on tourism forecasting used univariate time series or a limited number of variables with mostly yearly or quarterly, and rarely monthly frequencies. However, the application of data mining techniques for multivariate …
Comparative Performance Evaluation Of Blast Furnace Flame Temperature Prediction Using Artificial Intelligence And Statistical Methods, Yasi̇n Tunçkaya, Etem Köklükaya
Comparative Performance Evaluation Of Blast Furnace Flame Temperature Prediction Using Artificial Intelligence And Statistical Methods, Yasi̇n Tunçkaya, Etem Köklükaya
Turkish Journal of Electrical Engineering and Computer Sciences
The blast furnace (BF) is the heart of the integrated iron and steel industry and used to produce melted iron as raw material for steel. The BF has very complicated process to be modeled as it depends on multivariable process inputs and disturbances. It is very important to minimize operational costs and reduce material and fuel consumption in order to optimize overall furnace efficiency and stability, and also to improve the lifetime of the furnace within this task. Therefore, if the actual flame temperature value is predicted and controlled properly, then the operators can maintain fuel distribution such as oxygen …