My major is meteorology and deal with time series data, which have huge amount of data number. This book is very helpful to me. I have to determine the statistical significance of my data and check up the assumptions both in time and frequency domain, before deciding the statistical tests. I read a few this kinds of books but they were only concentrated on the explanation of simple statistics theory and then came to stop after showing only a few simple examples. So it is difficult to apply to my raw data because the assumptions that were applied to the theory were invalid in real situation. For example, ¡°Introduction to probability and statistics for engineers and scientist, by Sheldon M. Ross¡± is too theoretical to me and the application of theories is biased to the persons who are interested in small data set. Additionally, many books assumed the normal distribution and did not refer how we could test these assumptions. In analyzing the time series, not only is important the data analysis in time domain, but also the analysis in frequency domain. ¡°Applied Statistical time series analysis by Shumway¡± dealt with it. However, ¡°Random data by Bendat and Piersol¡± more will be helpful to the people who deal with time series, want to design statistical filters and to do the statistical tests and need more profound and systematic theories and understanding.