The objective of this study was to construct the black pepper prices forecasting model by 4 statistical methods: Box-Jenkins method Holt’s exponential smoothing method Brown’s exponential smoothing method and damped trend exponential smoothing method. Time series of monthly black pepper prices which were gathered from the website of the Office of Agricultural Economics during January 2005 to December 2018 of 168 observations were divided into 2 datasets. The first dataset had 156 observations from January 2005 to December 2017 for constructing the forecasting models. The second dataset had 12 observations from January to December 2018 for comparing the accuracy of the forecasts via the criteria of the lowest mean absolute percentage error (MAPE) and root mean squared error (RMSE). Research findings indicated that for all forecasting methods that had been studied the most accurate method was damped trend exponential smoothing method (MAPE = 6.5773 RMSE = 10.3775)

The purpose of this research was to construct the appropriate forecasting model for the prices of field corn in Thailand using four time series analysis methods including Box-Jenkins method Holt’s exponential smoothing method damped trend exponential smoothing method and combined forecasting method. Time series data from the website of the Office of Agricultural Economics with total 227 months during January 1997 to November 2015 are used and divided into two series. The first 216 months from January 1997 until December 2014 used to build the forecasting models and the last 11 months from January until November 2015 used to compare the forecasting methods accuracies via the criteria of the lowest mean absolute percentage error and root mean squared error. Research finding indicated that combined forecasting model was the most accurate.

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.