Khao’ Niao Dam is a grain which has anthocyanins pigment. Rice flours contained high protein content more than white grain and have higher water absorption. The white rice flour had higher swelling power and lower solubility than 6 rices flours. Amylose which content in the white rice flour is peak viscosity final viscosity setback and pasting temperature than the waxy rice flours. The objectives of this research were to formulate and determine the appropriate condition for sensory test on the rices powdered beverage products to consumers in terms of color flavor sweet taste viscosity and preference overall. Starting from the basic formula 3 formula is a formula that consists of Germinated brown rice drying were 23.33 % millet drying were 3.33% soybean powder were 6.67% powdered milk were 13.33% sugar were 33.33 % and cream powder were 20.00%. Acceptance scores of 7.60 and then to research the use of Germinated brown rice drying a substitute for cream powder in the manufacture of rice powder beverages into 3 levels such as 50 75 and 100% the amount of Germinated brown rice drying a substitute for cream powder 100 % has been the most acceptance. Then the ratio of rice flour 7 the appropriate type of beverage rice powdodor and viscosity of the beverage rice powder for 3 formulas were formulated with Black Glutinous flour were 10.00% the most acceptance. The study of soybean powder substitute millet powder in the manufacture of rice powder beverages to reduce the viscosity into 3 levels: 50 75 and 100%. The result showed that the suitable of soybean powder substitute millet powder were 100% and the most acceptance. This study used amount of soybean powder and milk powder substitutes sugar in the production of powdered rice beverage can be divided into 3 levels in the formula is 5 10 and 15% the result showed that the 10% of the soybean powder and 15% of milk powder substitutes sugar the most acceptance. Then study the temperature of water for rice powder beverages and the ratio of water to the rice powder beverages in the rice powder is 1:5. Nutritional value analysis of rice beverage product showed were 6.46% moisture 11.63% protein 7.34% fat 5.65% dietary fiber 1.47% ash and 67.45% carbohydrate and the shelf-life of rices powdered beverages which pack in aluminum foil bags was preserve products and store at 30 °C more than 6 weeks.

This research aims to apply the principle of stochastic process for modeling. The parameters are estimated using Bayesian methods. The monthly average real price of paddy rice 15% and yield of paddy rice in Thailand were studied. The price and the yield of paddy rice which are time series data consisting of four components autocorrelation an exponential cumulative distribution function for trend outliers and two different types of seasons: the dummy seasons and Fourier function seasons. Writing algorithms programming in OpenBUGS and evaluating the performance of models from simulation programming in R were conducted. After that Bayesian methods with dummy seasons and Fourier function season were compared using RMSE MSE and MAE as the criteria. The results show that the Bayesian model with an exponential cumulative distribution function for trend and dummy seasons give the lowest RMSE MSE and MAE for both model fitting and model validating.

The objective of this research is to use a stochastic process for building a model using Bayesian parameter estimation. The model is applied to the of crash prices and future prices of rice in Thailand for 120 moths including monthly closing average price of white rice 5% non-glutinous paddy 15% and Hom Mali non-glutinous paddy 105. The data are time series data containing autocorrelation trend seasonality and outliers. The algorithms are turned into codes in OpenBugs and the performance of the model is evaluated using simulation in R. The model with a Weibull cumulative distribution function trend the model with an exponential cumulative distribution function trend and the exponential smoothing model are compared. The root mean squared error (RMSE) mean absolute percentage error (MAPE) and mean absolute error (MAE) are used as the criteria. The results show that the model with the model with an exponential cumulative distribution function trend gives smallest RMSE MAPE and MAE for the crash prices of non-glutinous paddy 15% and Hom Mali non-glutinous paddy 105 for both model fitting part and model validation part. The model with a Weibull cumulative distribution function trend gives the smallest RMSE MAPE and MAE for the closing average price of white rice 5% for both model fitting part and model validation part.

The objective of this study is to compare three forecasting methods on three agricultural products consisting of fresh whole chicken (include bone no entrails) red meat pork hip section and Jasmine rice. These methods are Simple Exponential Smoothing method Holt’s method and Winter’s method. Data are colleced for a period of 60 months from January 2007 to December 2011. The most suitable forecasting method are the one that shows no residual autocorrelation and gives the lowest MSE MAPE. The results show that Simple Exponential Smoothing method is the most suitable method on three agricultural products due to this method reveals no residual autocorrelation and displays the lowest MSE MAPE.

The purpose of theis research was to study the expectancy of executives regarding the characteristics of graciuates of Rajamangala institute of TechnologyBangkok commercial Campus in three aspects: AcademicEthics and Professional Characteristics. This included the study of holistic and individual aspects as well as the comparisons of the characteristics of graduates of Rajamangala institute of TechnologyBangkok commercial Campus. The executives were classified by sexagetype of enterprisestype of businesssize of enterprises and major subjects of graduates. The samples in this research were 290 execitoves.The study participates selected by Stratified Fandom sampling. The instrument used was a questionnaire.The statistics used were Percentagemeanstandard deviation t -test One Way ANOVA and LSD.