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Zusatztext This is an appealing introduction that would be accessible to a variety of students at the college level. Its strengths are clarity and directness with an abundance of good examples and case studies. Informationen zum Autor Until his retirement in 2009, Graham Currell was a Principal Lecturer in physics at the University of the West of England. During his early career his particular interest was in the preparation of specialist training programmes to support staff in university science laboratories in Asia, the Middle East, Africa and Central America, but since 2000 he has concentrated on the development of data analysis modules and self-study materials for science students, and until summer 2014 became a part-time research fellow, in which he further explored the development of online learning resources. This book builds on Graham's development of teaching materials for maths and statistics, including screen-capture videos in forensic, chemical, biological, and environmental science for UWE and the Royal Society of Chemistry. The approach reflects his extensive experience of providing tutorial and training support for students and staff carrying out research projects across both the physical and life sciences. Klappentext Drawing on the author's extensive experience of supporting students undertaking projects, Scientific Data Analysis is a guide for any science undergraduate or beginning graduate who needs to analyse their own data, and wants a clear, step-by-step description of how to carry out their analysis in a robust, error-free way. Zusammenfassung Drawing on the author's extensive experience of supporting students undertaking projects, Scientific Data Analysis is a guide for any science undergraduate or beginning graduate who needs to analyse their own data, and wants a clear, step-by-step description of how to carry out their analysis in a robust, error-free way. Inhaltsverzeichnis Part I - Understanding the statistics 1: Statistical concepts 2: Regression analysis 3: Hypothesis testing 4: Comparing data Part II - Analysing experimental data 5: Project data analysis 6: Single response variables 7: Related variables 8: Frequency data 9: Multiple variables ...
