The objectives of this research are to propose a model for estimating and detecting rice yields in Thailand to investigate the factors related to the rice yields to find the trend and to construct the maps of rice yields in Thailand. A linear mixed model (LMM) including the spatial effects which follow the conditional autoregressive model (CAR) and the time effects which follow the first-order linear autoregressive (AR(1)) model is proposed. The estimated rice yields are used to construct the rice yield maps in Thailand. The dependent variables are the rice yield in each month of each province. The factors considered are rainfall average temperatures and regions. The results indicate that the factors enfluencing the rice yields are rainfall average temperatures and regions (North Northeast South East West Central region is a refrence region). The trend also effects the rice yields. The rice yeilds maps are easy for readers to identify which areas have high or low yields and easy to compare the yields among areas by looking at their different colors.

The objectives of this research are to propose a linear mixed model including spatial effects to present the distribution of air pollution of each gas being Sulphur dioxide (SO2) Nitrogen dioxide (NO2) Carbon monoxide (CO) and Ozone (O3) and particulate matter (PM10) and to investigate the factors related to those air pollution in each area of Bangkok Metropolitan Region. The spatial effects are assumed to be a conditional autoregressive (CAR) model. The dependent variables are the amount of SO2 NO2 CO O3 and PM10 in each area and the independents considered are Industrial area business area and suburb area. The results indicate that the factors influencing the amount of air pollution ranking from most to least are industrial area suburb area and business area respectively.

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.