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The Exchange Market Pressure Index (EMPI) is an indicator of pressure on a currency. Because of the presence of serial correlation, financial time series may not be normally distributed even for large sample sizes. They may have undefined parameters and hence parametric tests of normality may give misleading results. In this paper, we look at the time series of EMPI of eleven countries of the world, put the data to normality check using tests suggested by various scholars. We also apply a test used exclusively for serially correlated data. No one has used this test earlier. In this context, we also compare the power of these statistical tests, which is another novel contribution of this paper. On the basis of these tests the EMPI time series is found to be non-normal. Two tests are found to be the most powerful. The test which is designed exclusively for time series data is found to be powerful only for China and South Korea, the countries which had the lowest EMPI- standard- deviation in the group of all the eleven countries studied in this paper. [ABSTRACT FROM AUTHOR]

A radio frequency (RF) repeater is a wireless electronic device that transmits signals from a base transceiver station to a mobile station. When inspecting RF repeaters, various items are required to be tested to ensure their quality. In this paper, we propose a systematic procedure for the inspection by using a multiple linear regression method. The basic idea is to predict the inspection result without real inspection. In particular, a multicollinearity problem is considered in the regression analysis. Two case studies are conducted for validating the proposed method with an RF repeater production company in Korea. [ABSTRACT FROM AUTHOR]