This research proposes a Bayesian logistic regression model which is applied to the data from autoparts manufacturing machines. Factors related to defective and bad products are investigated. The proposed model is compared with the logistic regression using maximum likelihood method for parameter estimation. The data were collected from 132 machines in an autoparts manufacturing factory. The research found that useful life machine type 6 worker group 3 and 4 working step 1 and 2 influence to the risk of producing defective and bad products. When the useful life is increased by 1 month the risk of producing defective and bad products will be increased by 2.2%. The risk that the machine type 6 will produce defective and bad products is 4.078 times greater than the risk that the machine type will do. The risk that the worker group 3 will produce defective and bad products is 61.7% less than the risk that the worker group 12 will do. The risk that the worker group 4 will produce defective and bad products is 61.5% less than the risk that the worker group 12 will do. The risk that the working step 1 will produce defective and bad products is 2.831 times greater than the risk that the working step 4 will do. The risk that the working step 2 will produce defective and bad products is 13.8 % greater than the risk that the working step 4 will do. The parameter estimates from the Bayesian logistic regression are very close to the ones from the logistic regression using maximum likelihood method for parameter estimation

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.