This is the first volume in the Practical Statistics for Biologists Workbooks series. It provides biologists with an easy-to-follow introduction to conducting statistical analysis in R. It does this through a series of practical exercises based on easy-to-follow flow diagrams that show biologists exactly how to do a variety of key tasks.These include: 1. Importing data sets and preparing them for analysis; 2. Creating graphs from them; 3. Assessing and transforming their distributions; 4. Using…
This is the first volume in the Practical Statistics for Biologists Workbooks series. It provides biologists with an easy-to-follow introduction to conducting statistical analysis in R. It does this through a series of practical exercises based on easy-to-follow flow diagrams that show biologists exactly how to do a variety of key tasks.
These include: 1. Importing data sets and preparing them for analysis; 2. Creating graphs from them; 3. Assessing and transforming their distributions; 4. Using statistics to compare data between groups; 5. Conducting correlations and regressions.
Together, these represent the key tasks biologists need to be able to carry out to start analysing their data using R. This means it provides the perfect practical introduction for any biologist interested in learning how to use R to do statistics.
This book is also well-suited to act as a course text for those who wish to teach introductory statistics courses using R to undergraduate, masters or postgraduate students in any biological field. This is because it not only provides a practical guide suitable for accompanying the teaching of statistical theory, it also provides off-the-shelf exercises, complete with suitable data sets, that can be used as the basis for practical classes.
This is the first volume in the Practical Statistics for Biologists Workbooks series. It provides biologists with an easy-to-follow introduction to conducting statistical analysis in R. It does this through a series of practical exercises based on easy-to-follow flow diagrams that show biologists exactly how to do a variety of key tasks.
These include: 1. Importing data sets and preparing them for analysis; 2. Creating graphs from them; 3. Assessing and transforming their distributions; 4. Using statistics to compare data between groups; 5. Conducting correlations and regressions.
Together, these represent the key tasks biologists need to be able to carry out to start analysing their data using R. This means it provides the perfect practical introduction for any biologist interested in learning how to use R to do statistics.
This book is also well-suited to act as a course text for those who wish to teach introductory statistics courses using R to undergraduate, masters or postgraduate students in any biological field. This is because it not only provides a practical guide suitable for accompanying the teaching of statistical theory, it also provides off-the-shelf exercises, complete with suitable data sets, that can be used as the basis for practical classes.
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