FREE SHIPPING BOTH WAYS

ON EVERY ORDER!

LIST PRICE:

$145.00

Sorry, this item is currently unavailable.

ISBN: 9781118464045 | 1118464044

Format: HardcoverPublisher: Wiley

Pub. Date: 5/6/2013

Because Knetbooks knows college students. Our rental program is designed to save you time and money. Whether you need a textbook for a semester, quarter or even a summer session, we have an option for you. Simply select a rental period, enter your information and your book will be on its way!

- We have the lowest prices on thousands of popular textbooks
- Free shipping both ways on ALL orders
- Most orders ship within 48 hours
- Need your book longer than expected? Extending your rental is simple
- Our customer support team is always here to help

This introductory text covers applied statistics and probability for undergraduate students in engineering and the natural sciences. The book begins at a level that is suitable for those with no previous exposure to probability and statistics and carries readers through to a level of proficiency in various techniques of statistics. Both descriptive and inferential statistics are emphasized, and the authors uniquely discuss descriptive statistics first and then continue with an elementary discussion of the fundamentals of probability theory underlying many of the presented statistical techniques. A wide range of statistical techniques are addressed, including statistical estimation, regression methods, statistical quality control (with emphasis on phase I and phase II control charts), process capability indices, non-parametric methods, and elements of reliability theory. Design of experiments is also addressed and includes discussion on randomized block designs, one- and two-way designs, latin square designs, response surface designs, and 2k factorial designs. All statistical concepts are supported by a large number of examples using data encountered in real-world situations. In addition, the authors illustrate how the statistical packages Minitab (R) and Microsoft Office Excel(R) can be used to aid in the analysis of various data sets.