Satu kaedah alternatif bagi menyelesaikan masalah transformasi dan aplikasinya dalam bidang biostatistik [QC1].

Penyelidikan yang dijalankan ini bertujuan untuk memberi pendedahan mengenai kewujudan masalah-masalah data daripada pelbagai rekabentuk ujikaji yang tidak memenuhi andaian-andaian analisis varians (ANOVA), analisis regresi dan prosedur yang seumpamanya secara khusus serta langkah-langkah mengatasinya dengan membuat penambahbaikan terhadap rumus asal transformasi Box-Cox sehinggalah ianya menjelajahi ke semua nombor-nombor nyata. The aim of this research is to expose the issue of the data problems from various studies which do not fulfill the assumptions for analysis of variance (ANOVA), regression analysis, and other such procedures in detail together with the steps to overcome this problem with modifying the original of Box-Cox formula until it can be used for all the real numbers

Some Practical Guidelines for Effective Sample-Size Determination in Observational Studies

Abstract- The purpose of this present study was to determine the right sample size in observational study which is focusing on medical or health sciences field. Sample size calculation is actually depends on the type of how the study is designed. For example different formulas are used to calculate the sample size in different type of the study. In this article, the formula of single proportion and two proportions is discussed and an example from medical research, which may contribute to the understanding of this problem, is presented

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

Factors Influencing Oral Health-Related Quality of Life Among Preschool Children in District of Kota Bharu, Malaysia: A Cross-Sectional Study

Objective: To assess caries experience and its association with oral health-related quality of life (OHRQoL) of preschool children in Kota Bharu, Kelantan, Malaysia. Material and Methods: A sample of 169 preschool children of 5-6-year-old at a private preschool in the district of Kota Bharu, Kelantan was participated and were subjected to an oral examination to determine their caries experience by a single calibrated dentist. The parents were responding to self-administered Malay-ECOHIS and their socio- demographic background. Descriptive, Chi-square test and Spearman correlation were done to analyse the data required in this study. Results: Subjects comprised of girls (55.6%) with most parents (39%) had a secondary level of education, and 47.9% of them had a monthly income of RM1000-RM2999. Caries prevalence was 74.6% with a mean (SD) dmft was 5.27(5.22). The impacts on OHRQoL were more prevalent in the family section of Malay-ECOHIS (12.5%; 95%CI: 7.5%-17.5%) than the child section (4.2%; 95%CI: 1.16%-7.24%). Items related to ‘felt guilty’ (22.5%; 95%CI: 16%-29%) and  ‘been  upset’ (20.2%; 95%CI: 14.1%-26.3%) were the frequently reported on the family impact section. In child impact section, the item related to ‘pain’ (36.7%; 95%CI: 30%-43%) and ‘difficulty eating’ (20.8%; 95%CI: 14.7%-26.9%) were reported more frequently. Children with caries experience were significantly associated with the impact on family OHRQoL (p<0.05). Conclusion: The presence of dental caries was a significant predictor of poor OHRQoL

Sample size calculations made easy using G*Power

G*power is a free software that helps researchers to calculate the sample size needed when conducting a research. The importance of sample size calculation is imperative for the knowledge of researchers. Sample size is very important in designing and planning a successful research as it involves time and financial planning. What is best about the G*power is that researchers can plan the real sample size according to their study design, such as minimum sample size for regression analysis with three predictor variables. This technique is commonly used when researchers need a quick decision on: “What is the sample size needed for an analysis?” This brief book illustrates how sample size is calculated based on specific statistical test. The step-by-step and simple discussions through simple presentation and easy-to-understand language used in this book help researchers to understand better about the sample size calculations. http://www.penerbit.usm.my/index.php/buku/261-sample-size-calculations-made-easy-using-g-powe

A Way of Selecting the Right Statistical Model for Handling Overdispersion and Excess Zeroes

Abstract: This study aims to find the risk factors of Diabetes Mellitus (DM) and to find the best model among Poisson, Negative Binomial (NB) and Zero-Inflated Negative Binomial (ZINB

Modeling and Handling Overdispersion Health Science Data with Zero-Inflated Poisson Model

Health sciences research often involves analyses of repeated measurement or longitudinal count data analyses that exhibit excess zeros. Overdispersion occurs when count data measurements have greater variability than allowed. This phenomenon can be carried over to zero-inflated count data modeling. Referred to as zero-inflation, the Zero-Inflated Poisson (ZIP) model can be used to model such data. The Zero-Inflated Negative Binomial (ZINB) model is used to account for overdispersion detected in count data. The ZINB model is considered as an alternative for the Zero-Inflated Generalized Poisson (ZIGP) model for zero-inflated overdispersed count data. Consequently, zero-inflated models have been proposed for the situations where the data generating process results are overdispersed. This study considers modeling and handling overdispersion data among children with Thalassemia disease using the ZIP, ZINB and ZIGP models

Categorical data analysis using SPSS for periodontology

Statistics is a very powerful tool for data measurement. It can give meaning to meaningless numbers. On the basis, data can be classified as categorical or continuous. This book focuses on categorical data analysis. Categorical data classifies an observation to one or more categories. To get a clear understanding on how to analyze categorical data, this book emphasizes on categorical data analysis, especially for all beginners among dental students, dental researchers and dental lecturers. It is a compulsory method of analysis that researchers should learn, as it provides core skills that will be very useful for conducting categorical data analysis. Besides that, this book gives the researchers an idea on how to design a study that will optimize the output of the research study

A New Strategy of Handling General Insurance Modelling Using Applied Linear Method

This paper proposes the use of bootstrap, robust and fuzzy multiple linear regressions method in handling general insurance in order to get improved results. The main objective of bootstrapping is to estimate the distribution of an estimator or test statistic by resampling one's data or a model estimated from the data under conditions that hold in a wide variety of econometric applications. In addition, bootstrap also provides approximations to distributions of statistics, coverage probabilities of confidence intervals, and rejection probabilities of hypothesis tests that produce accurate results. In this paper, we emphasize the combining and modelling using bootstrapping, robust and fuzzy regression methodology. The results show that alternative methods produce better results than multiple linear regressions (MLR) model. Keywords: Multiple linear regression; MM estimation; robust regression; bootstrap method; fuzzy regressio

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