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

by Rosenkrantz; Walter A.

• ISBN13: 9781584888123
• ISBN10: 1584888121
• Format: Hardcover
• Copyright: 2008-07-10
• Publisher: Chapman & Hall/

Summary

Integrating interesting and widely used concepts of financial engineering into traditional statistics courses, Introduction to Probability and Statistics for Science, Engineering, and Financeillustrates 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 famousBlack'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.

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

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