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 estimate the morbidity rates of Leptospirosis in each month of all provinces in Thailand to determine the trend of leptospirosis over time to investigate factors influencing on the morbidity rates and to construct the disease maps of the leptospirosis. The generalized linear mixed model (GLMM) in which the spatial effects follow the conditional autoregressive model (CAR) and the temporal effects follow a linear trend. The estimated morbidity rates are used to construct the disease maps. The dependent variables are the numbers of leptospirosis patients in each month of each province and are assumed to have a Poisson distribution. The data are secondary data at a provincial level. The factors considered are rainfall averaged temperatures and regions. The results show that the factors influencing on the morbidity rates are rainfall (Relative Risk (RR) = 1.0013) averaged temperature (RR = 0.9922) north region (RR = 10.6440) northeast region (RR = 14.3249) southern region (RR = 15.5491) western region (RR = 3.6219) and eastern region (RR = 1.0103) where the central region is a reference region and a linear trend (RR = 1.0183). The top ten province and months with high morbidity rates (per 1000000 population) ranking from largest to smallest values are Phangnga in September (59.00) Phangnga in August (33.49) Sisaket in October (23.14) Sisaket in August ศรีสะเกษ (22.60) Sisaket in September (22.57) Phngnga in October (22.55) Loei in September (21.11) Phangnga in July (20.81) Phangnga in May (20.72) and Phangnga in June (20.47) respectively. The leptospirosis maps are easy for readers to identify which areas are at high risk. They are a useful tool for planning and controlling the leptospirosis.

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 objectives of this research are to estimate the morbidity rates of Hypertension and Ischemic heart disease in each province of Thailand to investigate factors influencing on the morbidity rates and to construct the disease maps of those two diseases. The generalized linear mixed model (GLMM) in which the responses have a Poisson distribution and the spatial effects are a multivariate conditional autoregressive model (MCAR) are adopted. The provincial secondary data sets consisting of a number of Hypertension and Ischemic heart disease patients an average income per head and a student proportion were collected. The results show the average income per head (Relative Risk (RR) = 1.0002) and student proportion (RR = 1.0186) influence the morbidity rates of Hypertension and Ischemic heart disease. Hypertension and Ischemic maps are easy for readers to see which areas are at high risk. They are a useful tool for planning and controlling the Hypertension and Ischemic heart disease.