Читать книгу Statistics - David W. Scott - Страница 7

The field of statistics has a rich history that has become tightly integrated into the emerging field of data sciences. Collaboration with computer scientists, numerical analysts, and decision makers characterizes the field. The role of statistics and statisticians is to find actionable information in a noisy collection of data. Every field of academic endeavor encounters this problem: from the electrical engineer trying to find a signal in a noisy channel to an English professor trying to determine the authorship of a contested newly discovered manuscript.

There are two basic tasks for the statistician. First is to characterize the distribution of possible outcomes using a batch of representative data. An actuary may be asked to find a dollar loss for car accidents that is not exceeded 99.999% of the time. An economist may be asked to provide useful summaries of a collection of income data. The histogram is our primary tool here, an idea that did not appear until the 17th century; see Graunt (1662), who analyzed death records during height of the plague outbreak in Europe.

The second task is that of prediction. A bank may wish to understand how credit risk is related to other information that may be available. A mechanical engineer may wish to understand the risk inherent in a new design under extreme conditions. Methods for performing this task underlie many algorithms today, for example, translating foreign languages or image recognition.

The mathematical backbone of all of our statistical methods is probability theory. Thus we study the basics of probability theory and random variables in the first part of this course. Statistical methods and the basics of statistical decision theory form the core of the middle third of this course. Specific tests and data analysis approaches finish our study.