Foundations of Applied Statistical Methods

Driven by real-world examples, this book covers applied statistical methods in a concise and easily accessible way. Coverage includes essential probability models, inference of means, proportions, correlations and regressions, and sample size determination.

This is a text in methods of applied statistics for researchers who design and conduct experiments, perform statistical inference, and write technical reports. These research activities rely on an adequate knowledge of applied statistics. The reader both builds on basic statistics skills and learns to apply it to applicable scenarios without over-emphasis on the technical aspects. Demonstrations are a very important part of this text. Mathematical expressions are exhibited only if they are defined or intuitively comprehensible. This text may be used as a self review guidebook for applied researchers or as an introductory statistical methods textbook for students not majoring in statistics. Discussion includes essential probability models, inference of means, proportions, correlations and regressions, methods for censored survival time data analysis, and sample size determination.

The author has over twenty years of experience on applying statistical methods to study design and data analysis in collaborative medical research setting as well as on teaching. He received his PhD from University of Southern California Department of Preventive Medicine, received a post-doctoral training at Harvard Department of Biostatistics, has held faculty appointments at UCLA School of Medicine and Harvard Medical School, and currently a biostatistics faculty member at Massachusetts General Hospital and Harvard Medical School in Boston, Massachusetts, USA.

Covers applied statistical methods in a concise and easily accessible way Driven by real-world examples not just mathematical derivations Includes valuable review exercises and 7 appendices

Autorentext
The author has over twenty years of experience on applying statistical methods to study design and data analysis in collaborative medical research setting as well as on teaching. He received his PhD from University of Southern California Department of Preventive Medicine, received a post-doctoral training at Harvard Department of Biostatistics, has held faculty appointments at UCLA School of Medicine and Harvard Medical School, and currently a biostatistics faculty member at Massachusetts General Hospital and Harvard Medical School in Boston, Massachusetts, USA.

Inhalt
Warming Up-Descriptive Statistics and Essential Probability Models.- Statistical Inference Focusing on a Single Mean or Proportion.- Inference Using t-tests for Comparing Two Means.- Inference Using Analysis of Variance for Comparing Multiple Means.- Inference Using Correlation and Regression.- Normal Distribution Assumption Free Non-Parametric Inference.- Methods for Censored Survival Time Data Analysis and Inference.- Sample Size Determination for Inference.- Review Exercise Problems.- Probability of Standard Normal Distribution.- Percentiles of t-Distributions.- Upper 95th and 99th Percentiles of Chi-square Distributions.- Upper 95th Percentiles of F-Distributions.- Upper 99th Percentiles of F-Distributions.- Sample Sizes for Independent Samples t-tests (normal approximation).- Index.