Statistics for life and health sciences EBook

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==Chapter I: Introduction to Statistics ==
==Chapter I: Introduction to Statistics ==
 +
* Natural Biomedical and Health Research Studies
 +
* Data-driven Statistics
 +
* Uses and Abuses of Statistics
 +
* Statistical Software Tools
==Chapter II: Data and variability==
==Chapter II: Data and variability==
* Data
* Data
 +
* Design of experiments
* R data management (Import and Export)
* R data management (Import and Export)
* Histograms, densities and summary statistics
* Histograms, densities and summary statistics
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* Samples, Populations, Repeated Samples, Resampling
* Samples, Populations, Repeated Samples, Resampling
* Bootstrapping
* Bootstrapping
-
* Testing 1, 2 or more samples
+
* Testing one, two or more samples
* Confidence intervals
* Confidence intervals
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* Independent samples
* Independent samples
* Paired samples
* Paired samples
-
 
-
===More then two samples===
 
==Chapter VII: Limiting Theorems==
==Chapter VII: Limiting Theorems==
-
* CLT
+
* Law of Large Numbers (First Fundamental Law of Probability Theory)
-
* LLN
+
* Central Limit Theorem (Second Fundamental Law of Probability Theory)
-
==Chapter VIII: Linear Modeling==
+
==Chapter VIII: Multivariate Statistics==
* Parametric (simple and multivatiate) regression
* Parametric (simple and multivatiate) regression
* Parametric ANOVA
* Parametric ANOVA
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* Non-parametric linear modeling
* Non-parametric linear modeling
* Randomization and Resampling based multivariate inference
* Randomization and Resampling based multivariate inference
 +
* Genome-wide association studies (GWAS)
==Chapter IX:==
==Chapter IX:==
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==Chapter II:==
==Chapter II:==
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{{translate|pageName=http://wiki.stat.ucla.edu/socr/index.php?title=Statistics_for_life_and_health_sciences_EBook}}

Revision as of 23:31, 18 March 2013

Welcome to the UCLA Statistics for the Biomedical and Health Sciences (Stats 13) electronic book (EBook).

Contents

Preface

This is an Internet-based probability and statistics for biomedical and health sciences EBook. The materials, tools and demonstrations presented in this EBook would are used for the UCLA Statistics 13 course. The EBook is developed, updated and manages by the UCLA Statistics faculty teaching this course over the years. Many other instructors, researchers, students and educators have contributed to this EBook.

There are four novel features of this Statistics EBook. It is community-built and allows easy modifications and customizations, completely open-access (in terms of use and contributions), blends information technology, scientific techniques, heterogeneous data and modern pedagogical concepts, and is multilingual.

Format

Each section in this EBook includes

  • Motivation
  • Concepts, definitions, formulations
  • Examples
  • Small (mock-up) and real (research-derived) data
  • Webapp demonstration with real data (HTML5)
  • R programming
  • Problems

Pedagogical Use

...

Copyright

The Probability and Statistics EBook is a freely and openly accessible electronic book for the entire community under CC-BY license ...

Chapter I: Introduction to Statistics

  • Natural Biomedical and Health Research Studies
  • Data-driven Statistics
  • Uses and Abuses of Statistics
  • Statistical Software Tools

Chapter II: Data and variability

  • Data
  • Design of experiments
  • R data management (Import and Export)
  • Histograms, densities and summary statistics

Chapter III: Randomization-based statistical inference

  • Samples, Populations, Repeated Samples, Resampling
  • Bootstrapping
  • Testing one, two or more samples
  • Confidence intervals

Chapter IV: Probability Models

Chapter V: Statistical Models

Chapter VI: Parametric Model-based Inference

  • Hypothesis testing foundations
  • Type I and II errors, Power, sensitivity, specificity

One sample inference

  • T-Test
  • Normal Z-test
  • Confidence intervals

Two sample inference

  • Independent samples
  • Paired samples

Chapter VII: Limiting Theorems

  • Law of Large Numbers (First Fundamental Law of Probability Theory)
  • Central Limit Theorem (Second Fundamental Law of Probability Theory)

Chapter VIII: Multivariate Statistics

  • Parametric (simple and multivatiate) regression
  • Parametric ANOVA
  • Parametric assumptions and model validation
  • Non-parametric linear modeling
  • Randomization and Resampling based multivariate inference
  • Genome-wide association studies (GWAS)

Chapter IX:

Chapter II:

Chapter II:

Chapter II:

Chapter II:




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