The objectives of this research are to propose a proper model for defective products in an autoparts manufacturing industry to apply the proposed model to the defective products data to investigate the factors related to the machines producing defective products and to compare the proposed model with the generalized estimating equations model (GEE). The proposed model is a generalized linear mixed model (GLMM) in which the dependent variables have a Poisson distribution. The parameter are estimated using a Bayesian method. The data were collected from 12 machines 15 days for each machine from an autoparts manufacturing factory. The data collection dates vary depending on the working steps and the product types. The results show that the proposed model fits the data. The factors related to the machines producing defective products are working steps workers and product types. According to the estimates of the factors effects the working step producing the largest number of defective products is the working step 2 followed by the working steps 3 1 and 4 respectively. The worker producing the largest of defective products is the worker 4 followed by the workers 10 5 8 6 2 3 1 7 and 9 respectively. The product type 3 has the largest number of defective products followed by the product types 2 and 1 respectively. If the population-averaged effects are focused the GEE is applied. However the subject-specific is focused the GLMM is applied.

The objective of this research is to use a stochastic process for building a model using Bayesian parameter estimation. The model is applied to the of crash prices and future prices of rice in Thailand for 120 moths including monthly closing average price of white rice 5% non-glutinous paddy 15% and Hom Mali non-glutinous paddy 105. The data are time series data containing autocorrelation trend seasonality and outliers. The algorithms are turned into codes in OpenBugs and the performance of the model is evaluated using simulation in R. The model with a Weibull cumulative distribution function trend the model with an exponential cumulative distribution function trend and the exponential smoothing model are compared. The root mean squared error (RMSE) mean absolute percentage error (MAPE) and mean absolute error (MAE) are used as the criteria. The results show that the model with the model with an exponential cumulative distribution function trend gives smallest RMSE MAPE and MAE for the crash prices of non-glutinous paddy 15% and Hom Mali non-glutinous paddy 105 for both model fitting part and model validation part. The model with a Weibull cumulative distribution function trend gives the smallest RMSE MAPE and MAE for the closing average price of white rice 5% for both model fitting part and model validation part.

The objectives of this research are to propose a Bayesian model for spatial time series analysis to apply the proposed model to forecast a monthly rubber yield in Southern provinces of Thailand and to compare the performance of the proposed model with the classical Holt-Winters Additive Exponential smoothing. The proposed model is a linear mixed model (LMM) with spatial effects which follow a conditional autoregressive model (CAR). Dummy variables are used for seasonal effects. A Bayesian method is used for parameter estimation. The estimated monthly yields are used for the monthly rubber yield forecasting. The dependent variables are the rubber yield in each month of each province. The data are secondary data at a provincial level. The factors considered are spatial effects heterogeneity effects and seasonal effects. The results show that the factors influencing on the amount of rubber yields are spatial heterogeneity and seasonal effects. The proposed model is proper and forecast accurately. Using the mean absolute error (MAE) the proposed model has a better performance compared to the classical Holt-Winters Additive Exponential smoothing in both model fitting and model validating. The proposed model should be the first consideration for spatial time series forecasting.

A distributed generation is a small-scale Installed in the customer density sources. The benefits of distributed energy resources is one example. To improve the reliability of power distribution system for the better. The increased reliability of power distribution system that is connected to the source of power distribution efficiency depends on the size of the supply and installation of power distribution are considered appropriate. To Evaluation the reliability in distribution system with the main purpose is to the minimization for customer interruption cost. The technique employed to minimization problem for customer interruption cost in solved by the developed tabu search algorithm together with reliability worth analysis that provide an indirect measure for cost implication associated with power failure. The developed methodology is tested with a distribution system of Roy Billinton Test System (RBTS) bus 2. Numerical results from the tests demonstrate that distributed generation can be used to promote economy and reliability of the distribution systems.

This study was aimed to investigate income and expenses which led to indebtedness of Royal Aide De Camp Department noncommissioned officers. The questionnaire was administered as the instrument for collecting data and the investigation was performed among the entire population using Survey Analytic Studies. The samples were 40 Royal Aide-De-Camp noncommissioned officers. The results of the study revealed that most officers had 100001 – 500000 in estimate and took on more public loan than pawnbroker’s loan. The public loans were taken from Cooperative saving banks while the pawnbroker’s loans were from relatives or friends. Their consumption behavior which caused indebtedness was expenses on buying household equipment such as washing machines fridges televisions etc. From primary analysis noncommissioned officers who were most in debt were Sergeant Major 3rd class(SM.3) Sergeant Major 2nd class (SM.2) and Sergeant Major 1st class (SM.1) who had worked for over ten years. They were married and had children but their spouses did not work with the total income of 5000 Baht.