JMASM41: An Alternative Method for Multiple Linear Model Regression Modeling, a Technical Combining of Robust, Bootstrap and Fuzzy Approach (SAS)

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. An algorithm for combining method is given by SAS language. We also provided some technical example of application of method discussed by using SAS computer software. The visualizing output of the analysis is discussed in detail

Statistical analysis using SPSS Version 24

This book provides the best solution for students and researchers in understanding the basic concept and the right procedure for the data analysis. The main objective of this book is to guide and help students or researchers who are using Statistical Package for the Social Sciences (SPSS) software in performing the statistical methods in their applied research. This book is very easy to follow for beginners in SPSS by following simple step-by-step instructions. It is arranged in a way that it is user friendly, and is written in a simple language that can easily be understood by the users. This book will give a straightforward solution to students and can also be used as a guideline in performing the statistical test using statistical analysis tools. Hopefully this book will help students in making good presentations and conclusions based on the results obtained and provides valuable information on statistical methods in applied research

Analisis kecekapan relatif bagi industri saham amanah menggunakan pendekatan ekonometrik

Purpose – This paper measures the relative efficiency of unit trust in Malaysia for the year 2003 and 2004, consisting of 65 funds from 16 unit trust management company which are categorised into three types; growth fund, income fund, and Islamic fund. The importance of this study can help the investor/trustee to choose the most efficient fund. Design/Methodology/Approach – The study employed the production model by Battese and Coelli (1992). Frontier software Version 4.1 was used to analyse the efficiency score of unit trust funds and to estimate the parameters of stochastic production using maximum likelihood method.Findings – Score efficiency analysis is important to measure the level of technical efficiency in the unit trust industry and other industries. The growth fund showed increasing efficiency score when tested funds were categorised depending on the type or the investment objectives. The mean of efficiency score for the growth fund in 2003 is 95% and 99% in 2004. Entirely, the income fund in 2003 was more efficient than 2004 with 100% mean efficiency in 2003 and 93% in 2004. However, both funds were still considered as excellent and efficient.Meanwhile, the Islamic fund had low efficiency scores with 73% in 2003 and 84% in 2004. Originality/Value – The paper investigated extensively the relative efficiency and highlights these to investors, policy makers of unit trust, the unit trust industry and other industries

JMASM37: Simple Response Surface Methodology Using RSREG (SAS)

Response surface methodology (RSM) can be used when the response variable, y, is influenced by several variables, x’s. When treatments take the form of quantitative values, then the true relationship between response variables and independent variables might be known. Examples are given in SAS

JMASM39: Algorithm for Combining Robust and Bootstrap In Multiple Linear Model Regression (SAS)

The aim of bootstrapping is to approximate the sampling distribution of some estimator. An algorithm for combining method is given in SAS, along with applications and visualizations

The Influence of Passion towards Critical Thinking Disposition among Athletes in University

Passion is a real strength enabling individuals go to action and achieve great things. Many glories of discoveries and achievements were only reached due to enthusiasm and the perseverance that characterize passionate behavior. Critical thinking disposition is essential affective component of critical thinking skills that really beneficial in growth of cognitive knowledge. The purpose of this study was to investigate the influence of passion towards critical thinking disposition among athletes in higher education. The study employs a correlational research design by using survey procedures. A questionnaire composes passion items and critical thinking disposition items were used to collect the data. T- Test was employed to compute the mean in order to identify the significant differences on passion and critical thinking disposition construct between technical and social science fields. Results showed that there was only significant difference on passion in sport but not in critical thinking disposition construct. The multiple regression analysis indicated that harmonious passion weakly influenced critical thinking disposition (Adj. R Square= .052) and most of its subscales. Based on the findings, researcher concludes that harmonious passion towards sport has an impact on critical thinking disposition. More research is needed to concern on critical thinking development underlying harmonious passion among national athletes. DOI: 10.5901/mjss.2015.v6n2p56

Modeling for exponential growth and decay methodology in biometry using SAS syntax

This paper provided an alternative method for exponential growth modeling as a regression analysis technique through the SAS algorithm. This alternative method is a combination technique (using nonlinear model bootstrap and fuzzy regression) for the small data set and gives the researcher an option to start the analysis, even if there is not enough data set. This method enhances the previous methodology with embedded bootstrapping and fuzzy technique to a nonlinear regression model. This principle aims to propose an alternative method of analysis with better results. In our case, we applied this principle to farm data and compared the results obtained by looking at the average width of the predicted interval

Malaysian and Italian trend line for Covid-19: A study on trend analysis

The first objective of this study was to evaluate trend line pattern, obtain the appropriate statistical equation model, and predict individual numbers infected by Covid-19. The second objective is to obtain a predictive equation model and forecast death rate for Malaysia and Italy. Malaysia's first positive case Covid-19 recorded January 24, 2020, consisting of three cases. Collected from January 24 to March 29, 2020. Sixty-six day-observations, based on their trend line pattern, earned special attention. Although the first positive case was identified on January 31, 2020, involving two patients. From January 31 to March 29, 2020, approximately 59 observations were collected from Italy. On 18 March 2020, the pattern will contrast with the Malaysian Movement Control Order (MCO). Malaysia and Italy collect death figures. A similar methodology will be applied to find the best-fitted model that fits both countries' death-number scenario. In Italy, the number of Covid-19-infected patients rises and meets quadratic trend line patterns. This induces extreme public distress and diversion. The quadratic trend line series analysed individual Covid-19-infected results. After March 18, 2020, it will continue to use a linear pattern. However, trend deaths also follow quadratic trend line pattern. Trend-line quadratic matched Italy's results. The quadratic line-of-trend model projection demonstrated dominance in estimating infected Covid-19. The quadratic death line from daily death collection data also showed superiority in estimating death number. The fitted quadratic model is better fitted in the Malaysian case, but the pattern shifts to linear trend line after MCO is implemented

Fuzzy regression model with Bayesian approach and its application to public health data

The application of the Bayesian Linear Regression (BLR) and Fuzzy Bayesian Linear Regression method through the SAS algorithm is the focus of this paper. As an alternative method of data analysis in biostatistics, this modified method can be used. This modified method includes a bootstrapping technique, residual normality checking and some Bayesian Linear Regression Modeling (BLR) enhancement through Fuzzy Bayesian Linear Regression. We illustrated the application of the algorithm for Bayesian Linear Regression (BLR) and Fuzzy Bayesian Linear Regression in this paper

Analisis Kecekapan Relatif bagi Industri Saham Amanah Menggunakan Pendekatan Ekonometrik

Makalah ini bertujuan untuk mengukur kecekapan relatif industri saham amanah di Malaysia menggunakan kaedah Analisis Sempadan Stokastik (ASS) bagi tahun 2003 dan 2004 yang terdiri daripada 65 buah dana daripada 16 buah syarikat pengurusan dana saham amanah di Malaysia. Dana yang dikaji dibahagikan kepada tiga jenis, iaitu pertumbuhan, pendapatan dan Islam/Syariah menggunakan spesifi kasi Batt ese dan Coelli (1992). Analisis data dan keputusan kajian menggunakan program Frontier Versi 4.1 (Coelli, 1996). Kecekapan merupakan pengukuran penting yang boleh digunakan untuk menilai perkembangan dan pertumbuhan ekonomi sesebuah negara.