Statistics at Square Two 2e: Understanding Modern Statistical Applications in Medicine - Softcover

Über die Autorin bzw. den Autor

M J Campbell, Professor of Medical Statistics, University of Sheffield, UK

Von der hinteren Coverseite

Updated companion volume to the ever popular Statistics at Square One (SS1)

Statistical methods used in medical literature are increasingly complex but software available to apply these methods is becoming easier to use. The result is that consumers of the literature now have to grasp sophisticated methods which may be inappropriately applied.

This book covers the sophisticated statistical methods and goes beyond the basics of SS1. It reviews the most commonly used modern statistical methods and highlights common misunderstandings. It is easy to read, with annotated computer outputs and a minimum of formulas.

It provides worked examples of methods such as multiple and logistical regression and each chapter concludes with exercises to reinforce learning.

This is for all those who need to understand statistics in clinical research papers and apply them in their own research, such as junior doctors and general practitioners.

Aus dem Klappentext

Updated companion volume to the ever popular Statistics at Square One (SS1)

Statistical methods used in medical literature are increasingly complex but software available to apply these methods is becoming easier to use. The result is that consumers of the literature now have to grasp sophisticated methods which may be inappropriately applied.

This book covers the sophisticated statistical methods and goes beyond the basics of SS1. It reviews the most commonly used modern statistical methods and highlights common misunderstandings. It is easy to read, with annotated computer outputs and a minimum of formulas.

It provides worked examples of methods such as multiple and logistical regression and each chapter concludes with exercises to reinforce learning.

This is for all those who need to understand statistics in clinical research papers and apply them in their own research, such as junior doctors and general practitioners.