Integrating interesting and widely used concepts of financial engineering into traditional statistics courses, this introduction illustrates the role and scope of statistics and probability in various fields. Linking probability theory with statistical inference, it presents many application examples from engineering, computer performance analysis,
Integrating interesting and widely used concepts of financial engineering into traditional statistics courses, Introduction to Probability and Statistics for Science, Engineering, and Finance illustrates the role and scope of statistics and probability in various fields.
The text first introduces the basics needed to understand and create tables and graphs produced by standard statistical software packages, such as Minitab, SAS, and JMP. It then takes students through the traditional topics of a first course in statistics. Novel features include:
Applications of standard statistical concepts and methods to the analysis and interpretation of financial data, such as risks and returns
Cox–Ross–Rubinstein (CRR) model, also called the binomial lattice model, of stock price fluctuations
An application of the central limit theorem to the CRR model that yields the lognormal distribution for stock prices and the famous Black–Scholes option pricing formula
An introduction to modern portfolio theory
Mean-standard deviation diagram of a collection of portfolios
Computing a stock’s betavia simple linear regression
As soon as he develops the statistical concepts, the author presents applications to engineering, such as queuing theory, reliability theory, and acceptance sampling; computer science; public health; and finance. Using both statistical software packages and scientific calculators, he reinforces fundamental concepts with numerous examples.
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