Introduction to Biostatistical Applications in Health Research with Microsoft Office Excel and R - Tapa dura

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ROBERT P. HIRSCH, PHD, is on the faculty at the Foundation for Advanced Education in the Sciences as well as a Medical Research Consultant with over thirty years of experience. He received his doctorate in Biology at Kansas State University. He was formerly Professor at the George Washington University - Columbian College of Arts & Science where he helped to develop the Epidemiology and Biostatistics Programs.

De la contraportada

Learn more about the foundations of statistical methods in health research with this authoritative new resource

The second edition of Introduction to Biostatistical Applications in Health Research delivers a thorough examination of the basic techniques and most commonly used statistical methods in health research. Retaining much of what was popular with the well-received first edition, the thoroughly revised second edition includes a new chapter on testing assumptions and how to evaluate whether those assumptions are satisfied and what to do if they are not.

The newest edition contains brand-new code examples for using the popular computer language R to perform the statistical analyses described in the chapters within. You'll learn how to use Excel to generate datasets for R, which can then be used to conduct statistical calculations on your data.

The book also includes a companion website with a new version of BAHR add-in programs for Excel. This new version contains new programs for nonparametric analyses, Student-Newman-Keuls tests, and stratified analyses. Readers will also benefit from coverage of topics like:

• Extensive discussions of basic and foundational concepts in statistical methods, including Bayes??? Theorem, populations, and samples
• A treatment of univariable analysis, covering topics like continuous dependent variables and ordinal dependent variables
• An examination of bivariable analysis, including regression analysis and correlation analysis
• An analysis of multivariate calculations in statistics and how testing assumptions, like assuming Gaussian distributions or equal variances, affect statistical outcomes

Perfect for health researchers of all kinds, Introduction to Biostatistical Applications in Health Research also belongs on the bookshelves of anyone who wishes to better understand health research literature. Even those without a great deal of mathematical background will benefit greatly from this text.

De la solapa interior

Learn more about the foundations of statistical methods in health research with this authoritative new resource

The second edition of Introduction to Biostatistical Applications in Health Research delivers a thorough examination of the basic techniques and most commonly used statistical methods in health research. Retaining much of what was popular with the well-received first edition, the thoroughly revised second edition includes a new chapter on testing assumptions and how to evaluate whether those assumptions are satisfied and what to do if they are not.

The newest edition contains brand-new code examples for using the popular computer language R to perform the statistical analyses described in the chapters within. You'll learn how to use Excel to generate datasets for R, which can then be used to conduct statistical calculations on your data.

The book also includes a companion website with a new version of BAHR add-in programs for Excel. This new version contains new programs for nonparametric analyses, Student-Newman-Keuls tests, and stratified analyses. Readers will also benefit from coverage of topics like:

• Extensive discussions of basic and foundational concepts in statistical methods, including Bayes Theorem, populations, and samples
• A treatment of univariable analysis, covering topics like continuous dependent variables and ordinal dependent variables
• An examination of bivariable analysis, including regression analysis and correlation analysis
• An analysis of multivariate calculations in statistics and how testing assumptions, like assuming Gaussian distributions or equal variances, affect statistical outcomes

Perfect for health researchers of all kinds, Introduction to Biostatistical Applications in Health Research also belongs on the bookshelves of anyone who wishes to better understand health research literature. Even those without a great deal of mathematical background will benefit greatly from this text.