Statistical Investing , described in this book, is a collection of analytical techniques for evaluating the profitability of stock and option trades. It answers the investor’s four basic questions: What to buy, When to buy, What to sell, and When to sell. A PhD NASA engineer has applied advanced mathematical methods to investing in stocks and options. But the math has been reduced down to techniques that are explained simply and can be implemented as a spreadsheet. Traditional technical analysis techniques (moving averages, MACD, etc.) suffer from indicator lag - they tell you what has happened, not what is happening. This makes them very poor indicators for stock trading. Statistical Investing eliminates the lag and shows you what the stock is doing now . Buy and sell signals are based on up-to-date data, not ancient history. The book, Statistical Investing , describes five techniques you can use to evaluate the profitability (in percent) of specific investment opportunities. Statistical Trend Trading identifies stocks in a major uptrend or downtrend and evaluates the profitability of specific trades. Statistical Swing Trading identifies the peaks and valleys in a stock price and indicates when to buy, hold, and sell a stock. Statistical Channel Trading encloses the price curve in a channel that indicates when a stock is underpriced or overpriced. Statistical Day Trading locates the peaks and valleys in a stock price in real time to identify profitable short-term trades. Statistical Option Trading analyzes specific option trades to identify those with the most profit potential. And they all operate with no lag . This book lays out, in simple language, how to decide what to buy, when to buy it, what to sell, and when to sell it. Although it uses advanced methods of probability and statistics, these techniques are easy to understand and use. Spreadsheets that implement these techniques are available for download on the author’s website. Statistical Investing is a completely new approach based on the advanced mathematics used in science and engineering. But they are applied specifically to predicting stock and option future price performance. And they are explained in plain language and illustrated with easy-to-understand examples. Using these tools will give any investor a head start at picking winners. Dr. Kenneth R. Castleman, the author of Statistical Investing , was a Senior Scientist at NASA’s Jet Propulsion Laboratory for fifteen years, and has served on the faculties of Caltech, USC, UCLA, and The University of Texas. He is a Senior Life Member of the IEEE, a Fellow of the American Institute of Medical and Biological Engineering, and a member of the United States Space Foundation’s Space Technology Hall of Fame. He has published four college textbooks and more than sixty scientific research papers. He has served as a consultant to the National Institutes of Health, the Federal Bureau of Investigation, and numerous law firms, where he served as an expert witness in more than sixty legal cases, mostly involving patent litigation.