Introduction to Statistics for the Life and Biomedical Sciences has been written to be used in conjunction with a set of self-paced learning labs. These labs guide students through learning how to apply statistical ideas and concepts discussed in the text with the R computing language.The text discusses the important ideas used to support an interpretation (such as the notion of a confidence interval), rather than the process of generating such material from data (such as computing a confidence interval for a particular subset of individuals in a study). This allows students whose main focus is understanding statistical concepts to not be distracted by the details of a particular software package. In our experience, however, we have found that many students enter a research setting after only a single course in statistics. These students benefit from a practical introduction to data analysis that incorporates the use of a statistical computing language.In a classroom setting, we have found it beneficial for students to start working through the labs after having been exposed to the corresponding material in the text, either from self-reading or through an instructor presenting the main ideas. The labs are organized by chapter, and each lab corresponds to a particular section or set of sections in the text.There are traditional exercises at the end of each chapter that do not require the use of computing. In the current posting, Chapters 1 - 5 have end-of-chapter exercises. More complicated methods, such as multiple regression, do not lend themselves to hand calculation and computing is necessary for gaining practical experience with these methods. The lab exercises for these later chapters become an increasingly important part of mastering the material.An essential component of the learning labs are the "Lab Notes" accompanying each chapter. The lab notes are a detailed reference guide to the R functions that appear in the labs, written to be accessible to a first-time user of a computing language. They provide more explanation than available in the R help documentation, with examples specific to what is demonstrated in the labs.
Introductory Statistics for the Life and Biomedical Sciences (FSE)
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Using a refreshingly clear and encouraging reader-friendly approach, this book helps students understand how to choose, carry out, interpret and report the results of complex statistical analyses, critically evaluate the design of ...
Pearson's Coefficient of Correlation • First, click the cell you want to fill, then click the paste function icon, f*, which will give you – in a box – a list of Excel functions available for your use. • The item you need in this list ...
This text uses an abundance of real data in the exercises and examples to minimize computation, so that students can focus on the statistical concepts and issues, not the mathematics.
Molinaro, A.M., Simon,R., and Pfeiffer, R.M. (2005). Predictionerror estimation:A comparison ofresampling methods. Bioinformatics, 21: 309– 313. Petricoin, E.F., et al. (2002). Useof proteomic patters in serumto identify ovarian cancer.
The Pearson residuals are defined as the contribution of the ith subject to the Pearson XP statistic , r ? ( y ; - î ; ) Nv ( a ) 10.0 + 7.5 5.0 Pearson Residual 2.5 0 -2.5 +. so that the X2 = ( 77 ) Both the Pearson and deviance ...
Now in its third edition, this title teaches an often intimidating and difficult subject in a way that is informative, personable, and clear.
Requiring no hand calculations, this highly applied book helps readers “get the story” from their data. They learn by doing, completing practice exercises at the end of each chapter.
Introductory Statistics includes innovative practical applications that make the text relevant and accessible, as well as collaborative exercises, technology integration problems, and statistics labs.