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This revised book provides an accessible presentation of concepts from probability theory, statistical methods, the design of experiments, and statistical quality control. It is shaped by the experience of the two teachers teaching statistical methods and concepts to engineering students. Practical examples and end-of-chapter exercises are the highlights of the text, as they are purposely selected from different fields. Statistical principles discussed in the book have a great relevance in several disciplines like economics, commerce, engineering, medicine, health care, agriculture, biochemistry, and textiles to mention a few.
Organised into 16 chapters, the revised book discusses four major topicsprobability theory, statistical methods, the design of experiments, and statistical quality control. A large number of students with varied disciplinary backgrounds need a course in basics of statistics, the design of experiments and statistical quality control at an introductory level to pursue their discipline of interest. No previous knowledge of probability or statistics is assumed, but an understanding of calculus is a prerequisite. The whole book also serves as a master level introductory course in all the three topics, as required in textile engineering or industrial engineering.
Covers the statistical methods, the design of experiments, and statistical quality control Supplies several practical and class-tested examples and end-of-chapter exercises Includes R software and examples to evaluate the statistical measures
Auteur
Dharmaraja Selvamuthu is Professor at the Department of Mathematics, Indian Institute of Technology Delhi, India. He also served as Head of the Department of Mathematics, Indian Institute of Technology, Delhi. He earned his M.Sc. degree in Applied Mathematics at Anna University, Chennai, India, in 1994 and Ph.D. degree in Mathematics from the Indian Institute of Technology Madras, India, in 1999. He has held visiting positions at Duke University, USA; Emory University, USA; University of Calgary, Canada; University of Los Andes, Bogota, Colombia; National University of Colombia, Bogota, Colombia; University of Verona, Italy; Sungkyunkwan University, Suwon, Korea; and Universita Degli Studi di Salerno, Fisciano, Italy. His research interests include applied probability, queueing theory, stochastic modeling, performance analysis of computer and communication systems and financial mathematics. He has published over 85 research papers in several international journals of repute and over40 research papers at various international conferences.
Dipayan Das is Professor at the Department of Textile Technology, Indian Institute of Technology Delhi, India. He obtained Ph.D. degree from the Technical University of Liberec, the Czech Republic, in 2005. His research interests are in the areas of modeling of fibrous structures and their properties, product and process engineering using statistical and mathematical techniques, and nonwoven products and processes. He has published four books including two monographs and over 100 research papers in scientific journals and conference proceedings. He is a recipient of the BIRAC-SRISTI Gandhian Young Technological Innovation (GYTI) Appreciation Award (in 2018), IIT Delhi Teaching Excellence Award (in 2017), and Kusuma Trust Outstanding Young Faculty Fellowship (from 2008 to 2013).
Contenu
Chapter 1 . Introduction.- Chapter 2. Basic Concepts of Probability.- Chapter 3. Random Variable and Distribution Function.- Chapter 4. Standard Deviation.- Chapter 5. Multiple Random Variable and Joint Distribution.- Chapter 6. Limiting Distributions.- Chapter 7. Descriptive Statistics.- Chapter 8. Sampling Distributions.- Chapter 9. Estimation.- Chapter 10. Testing of Hypothesis.- Chapter 11. Analysis of Correlation and Regression.- Chapter 12. Single Factor Experimental Design.- Chapter 13. Multi-Factor Experimental Designs.- Chapter 14. Response Surface Methodology.- Chapter 15. Acceptance Sampling.- Chapter 16. Control Charts. Appendix A. Statistical Tables.- Appendix B . Introduction to the R Software Program.