Statistics for life and health sciences EBook

From Socr

Revision as of 23:22, 18 March 2013 by IvoDinov (Talk | contribs)
Jump to: navigation, search

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

Chapter II: Data and variability

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

Chapter III: Randomization-based statistical inference

  • Samples, Populations, Repeated Samples, Resampling
  • Bootstrapping
  • Testing 1, 2 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

More then two samples

Chapter VII: Limiting Theorems

  • CLT
  • LLN

Chapter VIII: Linear Modeling

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

Chapter IX:

Chapter II:

Chapter II:

Chapter II:

Chapter II:




Translate this page:

(default)

Deutsch

Español

Français

Italiano

Português

日本語

България

الامارات العربية المتحدة

Suomi

इस भाषा में

Norge

한국어

中文

繁体中文

Русский

Nederlands

Ελληνικά

Hrvatska

Česká republika

Danmark

Polska

România

Sverige

Personal tools