The objective of this research is to select a proper cumulative function for trend in an analysis of simultaneous multiple time series data with autocorrelation trend and outliers using a Bayesian method. The proposed model is applied to fluctuate stock price data in the stock exchange of Thailand. The data consist of the closing prices of 5 stocks. The algorithms are written in OpenBUGS and the performance of the model is evaluated via a simulation study in R. The cumulative distribution functions considered here are a Weibull cumulative distribution function and an exponential cumulative distribution. They are compared using RMSE MAPE and MAE as criteria. The research found that an exponential cumulative function for trends gives smaller values of the three criteria in both model fitting and model validation for the4 stocks EA NWR and KTB. However a Weibull cumulative distribution function gives smaller values of three criteria for the PTT in both model fitting and model validation.

The objective of this research is to propose a forecasting model for uncertain demand index data of steel. The forecasting model proposed by Yelland (2010) was modified by adjusting the prior distributions of some parameters in the model in order that it is suitable for the demand of steel in Thailand. The algorithms for model fitting were written in OpenBUGS. The proposed model was compared to a classical exponential smoothing model. The research found that the forecast errors from the proposed forecasting model were minimum.

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.

This research proposes a hidden Markov model for detecting machines with multiple fillers producing soft drink for quality control in order to reduce defective products. The proposed model is compared with a classical generalized estimating equations model (GEE). The monthly data the number of defective products the correctness and the time spending in correction were collected from a machine in a sample factory in Pratumthanee province. The two hidden states of the fillers are workable and unworkable. The states has a logistic distribution. The results show that the correctness of the filler error occurrence does not improve the probability of being in a unworkable state neither the period of time spending on correction. The current state seems to be the same as the previous state

The objectives of this research are: to study the characteristics of students in the Faculty of Engineering at Rajamangala University of Technology Pra Nakorn North Bangkok Campus that influence to the risk of withdrawal from Calculus 2 for Engineers; to predict the risk of withdrawal from Calculus 2 for Engineers; and to use the results of the study for planning and solving the problems of withdrawal from Calculus 2 for Engineers. The sample is 226 students in the Faculty of Engineering at Rajamangala University of Technology who enrolled the Calculus 2 for engineers in the second semester of the academic year 2009. The data are from the questionnaire. The factors considered are grad point average the number of enrollments for the Calculus 1 for engineers Calculus 1 for Engineers grade the number of courses that emphasize on calculation that were enrolled in the second semester of the academic year 2009 the sacrifices of students for the Calculus 2 for Engineers course and the need to withdraw from Calculus 2 for Engineers course before other courses. The logistic regression model in which the parameters are estimated by using Bayesian methods is used for analyzing the data. The study shows that the factors that influence to the risk of withdrawal from the Calculus 2 for Engineers are grade point average Calculus 1 for Engineers grade the number of courses that emphasize on calculation that were enrolled in the second semester of the academic year 2009 and the sacrifices of students for the Calculus 2 for Engineers and the need to withdraw from Calculus 2 for Engineers before other courses. The factor that does not influence to the risk of withdrawal from the Calculus 2 for Engineers is the number of enrollments for the Calculus 1 for Engineers.