Introduction to Probability and Statistics for Science, Engineering, and Finance

Book Description 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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Content

1. Data Analysis
2. Probability Theory
3. Discrete Random Variables and Their Distribution Functions
4. Continuous Random Variables and Their Distribution Functions
5. Multivariate Probability Distributions
6. Sampling Distribution Theory
7. Point and Interval Estimation
8. Hypothesis Testing
9. Statistical Analysis of Categorical Data
10. Linear Regression and Correlation
11. Multiple Linear Regression
12. Single Factor Experiments: Analysis of Variance
13. Design and Analysis of Multi-Factor Experiments
14. Statistical Quality Control