Quantitative research synthesis employed statistical methods to synthesize several research papers which were identical in terms of research problems Meta-Analysis in order to discover a standard index that was able to synthesize the aforementioned research papers and to measure the quantifiable effects of manipulated variable on the dependent variable. Regarding this article the author presented and explained Longford’s estimation of effect size which is one of the most efficient and recent estimations. The study proved the background and functional model of the estimator of effect size. In addition the calculation examples following the estimator of Longford’s effect size were demonstrated by meta-analysis.

This research proposes the variance estimation method of Pareto data and to compare three variance estimation methods. Those methods are simple method the adjusted variance method with term (n - 1)/n and the adjusted variance method with term (n - 2)/n. The research was considered by the absolute of bias (|Bias|) the mean absolute errors (MAE) and the mean square errors (MSE) of variance estimator. This research used Monte Carlo Simulation method. Results of this research show that the |Bias| of simple method is the lowest for almost all conditions. We can arrange the methods as follows: the adjusted variance method with term (n - 1)/n and the adjusted variance method with term (n - 2)/n respectively. For all situations the MAE and MSE the of adjusted variance method with term (n - 2)/n is the lowest. We can arrange the methods as follows: the adjusted variance method with term (n - 1)/n and simple method respectively.

The objectives of this research are to propose a proper model for defective products in an autoparts manufacturing industry to apply the proposed model to the defective products data to investigate the factors related to the machines producing defective products and to compare the proposed model with the generalized estimating equations model (GEE). The proposed model is a generalized linear mixed model (GLMM) in which the dependent variables have a Poisson distribution. The parameter are estimated using a Bayesian method. The data were collected from 12 machines 15 days for each machine from an autoparts manufacturing factory. The data collection dates vary depending on the working steps and the product types. The results show that the proposed model fits the data. The factors related to the machines producing defective products are working steps workers and product types. According to the estimates of the factors effects the working step producing the largest number of defective products is the working step 2 followed by the working steps 3 1 and 4 respectively. The worker producing the largest of defective products is the worker 4 followed by the workers 10 5 8 6 2 3 1 7 and 9 respectively. The product type 3 has the largest number of defective products followed by the product types 2 and 1 respectively. If the population-averaged effects are focused the GEE is applied. However the subject-specific is focused the GLMM is applied.

The objective of this research was to study environment changes in orchards by using wireless sensor network and spatial data interpolation technique. The study was conducted by distributing several client sensors into the two study areas for recoding temperature himudity and light internsity. These data are send to a server using 433 MHz radio signal. The results from the first study area in Thachang District shew that the light intensities were significantly changed during 6:00-6:59 11:00-14:00 and 18:00-19:59. For the humidities they were significantly changed during 7:00-8:59 17:00-19:59 and 22:00-23:00 which were quite different from the changing of light intensity. Lastly the temperatures were mostly changed during 7:00-7:59 10:00-14:59 and 18:00-19:59 which were highly related to the changes of light intensity. For the second study area in Angkhiri District light intensities were significantly changed during 7:00-7:59 11:00-14:00 and 18:00-19:59 while the changing durations for the humidities were 9:00-9:59 12:00-15:00 and 17:00-19:59. Very similarly to the light intensity the temperatures were changed during 8:00-8:59 11:00-14:59 and 15:00-17:59. For the communiation distances the maximum distance for transfering data in an open area was 100 meter and 25 meters with obstables.