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 purpose of this study is to compare four forecasting methods on SET Index SET 50 Index and SET 100 index. These methods are Simple Exponential Smoothing Holt’s Method Box-Jenkins Method and Regression Analysis. Two sets of Index data were used in this study. The first one used as a training set was collected at the end of each day from January 2 2008 to September 30 2011. The other one used as a testing set was collected at the end of each day from October 3 2011 to October 14 2011. The diagnostic check was used to test each method. The MAE MSE and MAPE from each method shows that Box-Jenkins is more appropriate to the forecasting of SET Index SET 50 Index and SET 100 Index because its MAE MSE and MAPE are fairly low and have no autocorrelations. This leads to the conclusion that the appropriate models for forecasting of SET Index SET 50 Index and SET 100 Index are ARIMA (212) ARIMA (213) and ARIMA (213) respectively. The most stock market indices are correlated to SET Index SET 50 Index and SET 100 Index is Composite Index of Indonesia

The purposes of this study were to develop a model of Business Analysis in Statistics instruction through Content integration to enhance connection skills on Multiple Regression Analysis at undergraduate students of the 1 Semester academics year 2009 Rajamangala University ofTechnology Pha Nakron and to study their Business Analysis in statistics achievement being taught by using those model of Business Analysis in Statistics instruction.

The purpose of this study was to study the attitude towards statistic and the students learning achievement through Statistic I of students through the use of a cooperative learning method with STAD. The samples was students in the 2nd semester of 2005 academic year of Rajamangala University of Technology Phra Nakhon. The students of 1 classroom was selected by using simple random sampling method. The design of this study was one group pretest -posttest design. The data statistically analyzed by t -test for independent samples. The results were as follows: 1.The attitude towards statistic of students who learned form use of a cooperative learning wit~ STAD was significantly higher than the students who without learned form use of a cooperative learning with STAD at the 0.05 level. 2.The statistic I achievement of students who learned form use of a cooperative learning with STAD was significantly higher than the students who without learned form use of a cooperative learning with STAD at the 0.05 level.