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Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
|---|---|---|---|---|---|
Searching... | 30000010215352 | QA273 R76 2009 | Open Access Book | Book | Searching... |
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Summary
Summary
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 regressionAs 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.
Reviews 1
Choice Review
Rosenkrantz (Univ. of Massachusetts, Amherst) states in the preface that his book is suitable for a one- or two-semester course in probability and statistics aimed at engineering, computer science, mathematics, economics, or finance majors who have completed two semesters of single-variable calculus as a mathematics prerequisite. The book provides a very well-written, comprehensive treatment of all the standard requirements for an introductory course. Chapters include "Data Analysis," "Probability Theory," "Statistical Quality Control," and more. Rosenkrantz has successfully achieved his main goal of integrating widely used concepts from finance into the course. Students would indeed be interested in the discussions of stock prices, the lognormal distribution for stock prices, the Black-Scholes option pricing formula, etc. The large number of problems (over 650) covering a variety of fields is particularly noteworthy. Overall, a truly excellent course resource. Summing Up: Highly recommended. Lower- and upper-division undergraduates, faculty, professionals, and two-year technical program students. R. Bharath emeritus, Northern Michigan University
Table of Contents
| 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 |
| Inferences about Population Means |
| Inferences about Population Proportions |
| Linear Regression and Correlation |
| Multiple Linear Regression |
| Single-Factor Experiments: Analysis of Variance |
| Design and Analysis of Multi-Factor Experiments |
| Statistical Quality Control |
