Description

Book Synopsis
This textbook teaches statistics in a different way. It is aimed at undergraduate students in the life sciences, and it will also be invaluable for many graduate students. It makes the powerful methods of model formulae and the General Linear Model accessible to undergraduates for the first time. The computer revolution has finally made it possible to teach life sciences undergraduates how to use the statistics they really need to know - this book provides the course materials needed to fulfil that possibility. This text presents the fundamental statistical concepts without being tied to any one statistical package.

Trade Review
'The book is well laid out and concepts are very well explained by making effective use of diagrams and geometric representations. There are many analyses of example data sets to ilustrate the application the methods and the interpretation of the output'. Biometrics 59, 200-209, March 2003.
"it is a stepping-stone between one's first statistics course and what one really needs as a professional biologist. That said, it is the best stepping-stone on the market". Trends in Ecology and Evolution, 2003.
"Grafen and Hails have written a very nice book...many examples also serve to highlight design or analysis errors that are commonly made and encourage constructive critism: learning from mistakes is, I think, a very powerful approach." Animal Behaviour 2003

Table of Contents
Why use this book ; 1. An introduction to the analysis of variance ; 2. Regression ; 3. Models, parameters and GLMs ; 4. Using more than one explanatory variable ; 5. Designing experiments - keeping it simple ; 6. Combining continuous and categorical variables ; 7. Interactions - getting more complex ; 8. Checking the models A: Independence ; 9. Checking the models B: The other three assumptions ; 10. Model selection I: Principles of model choice and designed experiments ; 11. Model selection II: Data sets with several explanatory variables ; 12. Random effects ; 13. Categorical data ; 14. What lies beyond? ; Answers to exercises ; Revision section: The basics ; Appendix I: The meaning of p-values and confidence intervals ; Appendix II: Analytical results about variances of sample means ; Appendix III: Probability distributions ; Bibliography

Modern Statistics for the Life Sciences

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A Paperback by Alan Grafen, Rosie S. Hails

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    View other formats and editions of Modern Statistics for the Life Sciences by Alan Grafen

    Publisher: Oxford University Press
    Publication Date: 3/21/2002 12:00:00 AM
    ISBN13: 9780199252312, 978-0199252312
    ISBN10: 0199252319

    Description

    Book Synopsis
    This textbook teaches statistics in a different way. It is aimed at undergraduate students in the life sciences, and it will also be invaluable for many graduate students. It makes the powerful methods of model formulae and the General Linear Model accessible to undergraduates for the first time. The computer revolution has finally made it possible to teach life sciences undergraduates how to use the statistics they really need to know - this book provides the course materials needed to fulfil that possibility. This text presents the fundamental statistical concepts without being tied to any one statistical package.

    Trade Review
    'The book is well laid out and concepts are very well explained by making effective use of diagrams and geometric representations. There are many analyses of example data sets to ilustrate the application the methods and the interpretation of the output'. Biometrics 59, 200-209, March 2003.
    "it is a stepping-stone between one's first statistics course and what one really needs as a professional biologist. That said, it is the best stepping-stone on the market". Trends in Ecology and Evolution, 2003.
    "Grafen and Hails have written a very nice book...many examples also serve to highlight design or analysis errors that are commonly made and encourage constructive critism: learning from mistakes is, I think, a very powerful approach." Animal Behaviour 2003

    Table of Contents
    Why use this book ; 1. An introduction to the analysis of variance ; 2. Regression ; 3. Models, parameters and GLMs ; 4. Using more than one explanatory variable ; 5. Designing experiments - keeping it simple ; 6. Combining continuous and categorical variables ; 7. Interactions - getting more complex ; 8. Checking the models A: Independence ; 9. Checking the models B: The other three assumptions ; 10. Model selection I: Principles of model choice and designed experiments ; 11. Model selection II: Data sets with several explanatory variables ; 12. Random effects ; 13. Categorical data ; 14. What lies beyond? ; Answers to exercises ; Revision section: The basics ; Appendix I: The meaning of p-values and confidence intervals ; Appendix II: Analytical results about variances of sample means ; Appendix III: Probability distributions ; Bibliography

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