Multivariate Data Analysis

		Author Professor Kim H. Esbensen   ISBN 82-993330-3-2   Language English   Price $130 + Shipping       Features Practical applications of multivariate techniques such as PCA, PCR, and PLS Non-mathematical approach - ideal for analysts with little or no background in statistics Step by step introduction of new concepts and techniques Theory supported by practical hands-on exercises based on real-world data Cheque     Select Country to Ship Afghanistan Albania Algeria American Samoa Andorra Angola Anguilla Antigua and Barbuda Argentina Armenia Aruba Ashmore and Cartier Islands Australia Austria Azerbaijan Bahrain Baker Island Bangladesh Barbados Bassas da India Belarus Belgium Belize Benin Bermuda Bhutan Bolivia Bosnia and Herzegovina Botswana Bouvet Island Brazil British Indian Ocean Territory British Virgin Islands Brunei Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Cape Verde Cayman Islands Central African Republic Chad Chile China Christmas Island Clipperton Island Cocos (Keeling) Islands Colombia Comoros Congo, Democratic Republic of the Congo, Republic of the Cook Islands Coral Sea Islands Costa Rica Cote d'Ivoire Croatia Cuba Cyprus Czech Republic Denmark Djibouti Dominica Dominican Republic East Timor Ecuador Egypt El Salvador Equatorial Guinea Eritrea Estonia Ethiopia EU Europa Island Falkland Islands (Islas Malvinas) Faroe Islands Fiji Finland France France, Metropolitan French Guiana French Polynesia French Southern and Antarctic Lands Gabon Gaza Strip Georgia Germany Ghana Gibraltar Glorioso Islands Greece Greenland Grenada Guadeloupe Guam Guatemala Guernsey Guinea Guinea-Bissau Guyana Haiti Heard Island and McDonald Islands Holy See (Vatican City) Honduras Hong Kong Howland Island Hungary Iceland India Indonesia Iran Iraq Ireland Israel Italy Jamaica Jan Mayen Japan Jarvis Island Jersey Johnston Atoll Jordan Juan de Nova Island Kazakhstan Kenya Kingman Reef Kiribati Korea, North Korea, South Kuwait Kyrgyzstan Laos Latvia Lebanon Lesotho Liberia Libya Liechtenstein Lithuania Luxembourg Macau Macedonia, The Former Yugoslav Republic of Madagascar Malawi Malaysia Maldives Mali Malta Man, Isle of Marshall Islands Martinique Mauritania Mauritius Mayotte Mexico Micronesia, Federated States of Midway Islands Moldova Monaco Mongolia Montenegro Montserrat Morocco Mozambique Myanmar (Burma) Namibia Nauru Navassa Island Nepal Netherlands Netherlands Antilles New Caledonia New Zealand Nicaragua Niger Nigeria Niue Norfolk Island Northern Mariana Islands Norway Oman Pakistan Palau Palmyra Atoll Panama Papua New Guinea Paracel Islands Paraguay Peru Philippines Pitcairn Islands Poland Portugal Puerto Rico Qatar Reunion Romania Russia Rwanda Saint Helena Saint Kitts and Nevis Saint Lucia Saint Pierre and Miquelon Saint Vincent and the Grenadines San Marino Sao Tome and Principe Saudi Arabia Senegal Serbia Serbia and Montenegro Seychelles Sierra Leone Singapore Slovakia Slovenia Solomon Islands Somalia South Africa South Georgia and the South Sandwich Islands Spain Spratly Islands Sri Lanka Sudan Suriname Svalbard Swaziland Sweden Switzerland Syria Taiwan Tajikistan Tanzania Thailand The Bahamas The Gambia Togo Tokelau Tonga Trinidad and Tobago Tromelin Island Tunisia Turkey Turkmenistan Turks and Caicos Islands Tuvalu Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Vanuatu Venezuela Vietnam Virgin Islands (US) Wake Island Wallis and Futuna West Bank Western Sahara Western Samoa Yemen Yugoslavia Zambia Zimbabwe
	Includes sample datasets & The Unscrambler® (Training version).

Contents

1. Introduction to Multivariate Data Analysis overview
2. Getting Started - with Descriptive Statistics
3. Principal Component Analysis (PCA) – Introduction
4. Principal Component Analysis (PCA) - In Practice
5. PCA Exercises - Real-World Application Examples
6. Multivariate Calibration (PCR/PLS)
7. Validation: Mandatory Performance Testing
8. How to Perform PCR and PLS-R
9. Multivariate Data Analysis - in Practice: Miscellaneous Issues
10. PLS (PCR) Exercises: Real-World Application Examples - I
11. PLS (PCR) Multivariate Calibration - In Practice
12. PLS (PCR) Exercises: Real-World Applications - II
13. Master Data Sets: Interim Examination
14. Uncertainty Estimates, Significance and Stability (Martens' Uncertainty Test)
15. SIMCA: An Introduction to Classification
16. Introduction to Experimental Design
17. Complex Experimental Design Problems
18. Comparison of Methods for Multivariate Data Analysis - And their Validation
19. Literature
20. Appendix: Algorithms
21. Appendix: Software Installation and User Interface

Tutorial exercises,based on data drawn from real-world applications are used throughout the book to illustrate the use of the techniques introduced.This provides the reader with a working knowledge of modern Multivariate Data Analysis. All exercises use The Unscrambler®. The Introducer is an excellent self-study text for scientists, chemists and engineers of all disciplines- statisticians or non-statisticians - wishing to exploit the power of modern multivariate methods.