The objectives of this study were to calculate the probability of type I error power of a test and to compare the performance of three test statistics: Z Student’s t and Welch’s t. Including the recommending the appropriate test statistic. Classification of the population according to five distribution. The ratios of mean group 1 per group 2 were three levels. The ratios of variance group 1 per group 2 were three levels. The sample sizes of two populations were nine sizes. The significance levels that to study were 0.01 and 0.05. The data were simulated by using SAS programming with 1000 replicates. Results of the research were Z test statistic could control the probability of type I error and had higher power of a test than other test statistics but this test is necessary to know the variances of the two groups. Therefore it was appropriate to apply the other test statistics i.e. Student’s t or Welch’s t. If the variances of the two groups were equal Student’s t was a more appropriate test statistic than Welch’s t. But if the variances of the two groups were not equal Welch’s t was a more appropriate. When both sample sizes were large and the population variances were equal we could use any tests since there is no difference in the performance.

CO2 emission of rail transportation system plays more important role including the expansion of BTS route that consume the energy and emit CO2 gas more and more. The purpose of this research was to find out amount of CO2 emission from travelling by BTS train and determine specific CO2 emission per product in the form of gCO2e/passenger/km then forecasting on CO2 emission from BTS operations. Research process was initiated from collecting monthly data on ridership distance and electricity for 55 months continuously then regression analysis CUSUM and time series analysis were used for prediction and analysis of CO2 emission. The finding indicated that BTS’s specific CO2 emission was 24.7 gCO2e/passenger/km. Forecasting CO2 Emission from BTS result that Winters Exponential Smoothing Method is most accuracy then Double Exponential Smoothing Method Trend Analysis Method and Decomposition Method respectively. The result of these analysis could be used to calculate carbon footprint from staff travelling by BTS train and promote people to use public transport by government as sustainable transportation concept.

This research is the comparative study of three forecasting methods on SET 100 index. These methods are Simple Exponential Smoothing method Holts method and Box-Jenkins method. Two sets of data were used in this study. The first one used as a training set was collected from January 2 2008 to September 30 2011. The other one used as a testing set was collected from October 3 2011 to October 14 2011. We propose that the most appropriate forecasting method is the one which give the lowest MAE MSE MAPE and diagnostic checking. The results show that Holt’s method displays the lowest MAE MSE and MAPE. The next one is Box-Jenkins method while the Simple Exponential Smoothing method exhibits the highest value of all three indicators. However the model from Box-Jenkins method is the only one way that reveals no residual autocorrelation. This leads to the conclusion that the appropriate models for forecasting of SET 100 Index is ARIMA (213).

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.