Introduction to probability and statistics for science, engineering, and finance
著者
書誌事項
Introduction to probability and statistics for science, engineering, and finance
CRC Press, c2009
- : hbk
- タイトル別名
-
Probability and statistics for science, engineering, and finance
大学図書館所蔵 全10件
注記
Includes bibliographical references and index
内容説明・目次
内容説明
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.
目次
Data Analysis. Probability Theory. Discrete Random Variables and Their Distribution Functions. Continuous Random Variables and Their Distribution Functions. Multivariate Probability Distributions. Sampling Distribution Theory. Point and Interval Estimation. Hypothesis Testing. Statistical Analysis of Categorical Data. Linear Regression and Correlation. Multiple Linear Regression. Single-Factor Experiments: Analysis of Variance. Design and Analysis of Multi-Factor Experiments. Statistical Quality Control. Appendix. Index.
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