Statistical Regression With Measurement Error (Kendall'S Library Of Statistics),Used

Statistical Regression With Measurement Error (Kendall'S Library Of Statistics),Used

In Stock
SKU: SONG0340614617
Brand: Hodder Education Publishers
Sale price$64.00 Regular price$91.43
Save $27.43
Quantity
Add to wishlist
Add to compare

Processing time: 1-3 days

US Orders Ships in: 3-5 days

International Orders Ships in: 8-12 days

Return Policy: 15-days return on defective items

Payment Option
Payment Methods

Help

If you have any questions, you are always welcome to contact us. We'll get back to you as soon as possible, withing 24 hours on weekdays.

Customer service

All questions about your order, return and delivery must be sent to our customer service team by e-mail at yourstore@yourdomain.com

Sale & Press

If you are interested in selling our products, need more information about our brand or wish to make a collaboration, please contact us at press@yourdomain.com

Product Description Providing A General Survey Of The Theory Of Measurement Error Models, Including The Functional, Structural, And Ultrastructural Models, This Book Is Written In The Style Of The Kendall And Stuart Advanced Theory Of Statistics Set And, Like That Series, Includes Exercises At The End Of Thechapters. The Goal Is To Emphasize The Ideas And Practical Implications Of The Theory In A Style That Does Not Concentrate On The Theoremproof Format. Review 'Overall, The Presentation Is Inviting And The Reader Can Expect To Gain A Good Working Knowledge Of The Subject Without Much Tedious Effort. The Presentation Is Also Kept Simple By Avoiding The Useof Tedious Mathematical Notation. The Authors Have Succeeded Well In Their Goal Of 'Providing A Comprehensive Coverage Of The Subject That Emphasizes The Ideas And The Practical Implementation Of The Theory Without Too Great An Emphasis On The Theoremproof Format.' This Book Is On The Top Of My List For Anyone Interested In Linear And Polynomial Measurement Error Models.' Sudhir Gupta, Technometrics, Nov 2000, Vol 42, No 4 From The Back Cover Providing A General Survey Of The Theory Of Measurement Error Models, Including The Functional, Structural, And Ultrastructural Models, This Book Is Written In The Of The Kendall And Stuart Advanced Theory Of Statistics Set And, Like That Series, Includes Exercises At The End Of The Chapters. The Goal Is To Emphasize The Ideas And Practical Implications Of The Theory In A Style That Does Not Concentrate On The Theoremproof Format. About The Author Chilun Cheng Is At Institute Of State Science, Taiwan. John W. Van Ness Is At University Of Texas.

⚠️ WARNING (California Proposition 65):

This product may contain chemicals known to the State of California to cause cancer, birth defects, or other reproductive harm.

For more information, please visit www.P65Warnings.ca.gov.

  • Q: What is the main focus of 'Statistical Regression with Measurement Error'? A: The book primarily focuses on the theory of measurement error models, providing a comprehensive survey of functional, structural, and ultrastructural models.
  • Q: Who is the author of this book? A: The author of 'Statistical Regression with Measurement Error' is Chi-Lun Cheng.
  • Q: When was this book published? A: This book was published on May 13, 1999.
  • Q: What is the format of this book? A: The book is available in hardcover format.
  • Q: How many pages does the book contain? A: The book contains a total of 288 pages.
  • Q: What is the condition of the item? A: The item is listed in 'Very Good' condition.
  • Q: Does the book include exercises? A: Yes, the book includes exercises at the end of each chapter to reinforce learning.
  • Q: What category does this book fall under? A: The book falls under the category of Probability & Statistics.
  • Q: Is this book suitable for beginners? A: The book is written in a style aligned with advanced theory, so it may be more suitable for readers with some background in statistics.
  • Q: What edition of the book is available? A: This is the first edition of 'Statistical Regression with Measurement Error'.

Recently Viewed