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 propose an efficient and proper model that fits the cassava and rubber yields data. A generalized estimating equation (GEE) and a linear mixed model (LMM) with spatial correlation following the conditional autoregressive model (CAR) were adopted. The dependent variables are the cassava and rubber yields collected each month in every province of Thailand. The factors considered are rainfall averaged temperatures and regions. The results from GEE and LMM show that the factors influencing on the cassava and rubber yields are rainfall averaged temperature and region. Both GEE and LMM fit the correlated data. The GEE is used to explain the influence of factors on the yields in all provinces while the LMM is used to explain the influence of factors on the yields in each province.

The objectives of this research are to propose an approate and efficient model for analyzing rice corn and sugar yields data. A linear mixed model (LMM) with spatial correlation following the conditional autoregressive model (CAR) was adopted. The results show that for rice yields if the amount of rainfall in a harvest season increases 1 mm the yields will decease by 25.74 tons. if the temperature in the harvest season increases 1 Celsius the yields will decease by 212.60 tons and according to the trend the yield will increase by 15.80 tons each year. For sugar cane yields if the amount of rainfall in the harvest season increases 1 mm the yields will decease by 51.35 tons. if the temperature in the harvest season increase 1 Celsius the yields will incease by 121.30 tons and according to the trend the yield will increase by 29.93.80 tons each year. For corn yield if the amount of rainfall in the havest season increases 1 mm the yields will incease by 73.45 tons. if the temperature in he havest season increases 1 celsius the yields will decease by 40.44 tons and according to the trend the yield will increase by 1.74 tons each year.

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.