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

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

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

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

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

Patient Satisfaction Towards Dentist-Patient Interaction Among Patients Attending Outpatient Dental Clinic Hospital Universiti Sains Malaysia

Objective: To determine the satisfaction with the dentist-patient interaction and factors associated with patient satisfaction among patients attending the outpatient dental clinic Hospital Universiti Sains Malaysia (HUSM). Material and Methods: This cross-sectional study involved 229 patients who attended outpatient dental clinic Hospital USM that located in the East Coast region of Malaysia. A self-administered Skala Kepuasan Interaksi Perubatan – 11 (SKIP-11) questionnaire was used to assess the satisfaction towards dentist-patient interaction. Systematic random sampling was applied in this study. The data were analyzed using simple logistic regression analysis to determine the factors associated with patient satisfaction with dentist-patient interaction. Results: The mean age of patients was 32.6 ± 13.9 years, 71.6% of them study up to tertiary level, 31.5% came to for dental check up and 23.6% of them had tooth decay. More than half (64.6%) of the patients were satisfactory with dentist-patient interaction. The satisfaction percentage in the distress relief domain was 60.7%, 56.8% in the rapport domain, and 53.7% in the interaction outcome domain. Satisfaction with dentist-patient interaction was significantly associated with the dentists’ characteristics such as age (OR = 0.583, 95%CI 0.44-0.76, p=0.001), gender (OR = 0.386, 95% CI 0.22-0.69, p=0.001) and years of service (OR = 0.294, 95% CI 0.15-0.57, p=0.001). Conclusion: The result showed that slightly more than half of the patients who attended the outpatient dental clinic HUSM were satisfied with the dentist-patient interaction, which was found to be influenced by the characteristics of the dentists. Efforts to improve patient-dentist interaction are recommended to ensure delivery of good quality oral health care

Patient Satisfaction Towards Dentist-Patient Interaction Among Patients Attending Outpatient Dental Clinic Hospital Universiti Sains Malaysia

Objective: To determine the satisfaction with the dentist-patient interaction and factors associated with patient satisfaction among patients attending the outpatient dental clinic Hospital Universiti Sains Malaysia (HUSM). Material and Methods: This cross-sectional study involved 229 patients who attended outpatient dental clinic Hospital USM that located in the East Coast region of Malaysia. A self-administered Skala Kepuasan Interaksi Perubatan – 11 (SKIP-11) questionnaire was used to assess the satisfaction towards dentist-patient interaction. Systematic random sampling was applied in this study. The data were analyzed using simple logistic regression analysis to determine the factors associated with patient satisfaction with dentist-patient interaction. Results: The mean age of patients was 32.6 ± 13.9 years, 71.6% of them study up to tertiary level, 31.5% came to for dental check up and 23.6% of them had tooth decay. More than half (64.6%) of the patients were satisfactory with dentist-patient interaction. The satisfaction percentage in the distress relief domain was 60.7%, 56.8% in the rapport domain, and 53.7% in the interaction outcome domain. Satisfaction with dentist-patient interaction was significantly associated with the dentists’ characteristics such as age (OR = 0.583, 95%CI 0.44-0.76, p=0.001), gender (OR = 0.386, 95% CI 0.22-0.69, p=0.001) and years of service (OR = 0.294, 95% CI 0.15-0.57, p=0.001). Conclusion: The result showed that slightly more than half of the patients who attended the outpatient dental clinic HUSM were satisfied with the dentist-patient interaction, which was found to be influenced by the characteristics of the dentists. Efforts to improve patient-dentist interaction are recommended to ensure delivery of good quality oral health care

MODIFIED NONLINEAR MODEL FOR EXPONENTIAL GROWTH METHOD AND ITS APPLICATION IN BIOSTATISTICSS USING SAS

This paper supplied an alternative method for exponential growth modeling as a technique for regression analysis through SAS algorithm. This alternative method is a combination technique (using bootstrap and fuzzy regression for nonlinear model) for the small data set and gives the researcher an option to launch the analysis even there is not enough data set. This method current method improves the previous methodology with embedded bootstrapping and fuzzy technique to the step of nonlinear regression model. The aim of this principle is to propose an alternative method of doing analysis with better improved results. In our case, we applied this principle to the agriculture data and the gained results were compared by looking at the average width of predicted interval

Carta Kawalan XmR Dan Median : Satu Penyelesaian Untuk Data Pencong

Carta kawalan banyak digunakan dalam kawalan mutu berstatistik. Tujuan menggunakan carta kawalan ini adalah untuk memastikan sesuatu proses berada dalam keadaan terkawal dan stabil sepanjang masa secara grafik. Carta kawalan ini terdiri daripada had-had kawalan iaitu, had kawalan atas, had kawalan tengah dan had kawalan bawah. Untuk mengetahui sesuatu proses tersebut adalah terkawal, kesemua titik yang mewakili variasi dalam sesuatu proses berada di dalam lingkungan had atas dan had bawah. Jika terdapat titik yang terkeluar daripada had kawalan atas mahupun had kawalan bawah, maka proses tersebut dikatakan tidak terkawal dan tidak stabil. Salah satu penggunaan carta kawalan yang meluas ialah carta kawalan XmR iaitu X mewakili nilai individu manakala mR mewakili peralihan julat. Untuk memastikan sesuatu proses tersebut berada dalam keadaan terkawal dan stabil, anggapan kenormalan mestilah dipenuhi. Namun demikian, kebanyakan proses pengeluaran menghasilkan data yang pencong dan ini menyebabkan proses tersebut tidak terkawal dan tidak stabil. Oleh itu, satu pendekatan berdasarkan kuasa penjelmaan akan turut dibincangkan dalam kajian ini

Developing New Method in Measuring City Economic Resilience by Imposing Disturbances Factors and Unwanted Condition

Recent research uses an index to measure economic resilience, but the index is inadequate because it is impossible to determine which disturbance factors have the greatest impact on the economic resilience of cities. This study aims to develop a new methodology to measure the economic resilience of a city by simultaneously examining unwanted conditions and disturbance factors. The ratio of regional original income to the number of poor people is known as Z and is identified as a measure of economic resilience in Indonesia. Resilience is measured by Z’s position in relation to the unwanted area following a specific level of disturbance. If Z is in the unwanted condition, the city’s per capita income will decrease, and the city will be considered economically not resilient. The results of the analysis show that six levels of economic resilience have been successfully distinguished based on research on 514 cities in Indonesia involving nine indicators of disturbance and one variable of economic resilience during the five-year observation period, 2015–2019. Only 3.11 percent of cities have economic resilience level 1, while 69.18 percent have level 0. Economically resilient cities consist of 4.24 percent of cities at level 2, as much as 3.39 percent at level 3, as much as 3.39 percent at level 4, and as much as 16.69 percent at level 5. The novelty of this research is to provide a new methodology for measuring the economic resilience of cities by integrating unwanted conditions as necessary conditions and disturbance factors as sufficient conditions. The measurement of a city’s economic resilience is critical to help the city government assess the security of the city so the government can take preventive actions to avoid the cities falling into unwanted conditions