Statistics for life and health sciences EBook

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==Chapter II: Data and variability==
==Chapter II: Data and variability==
* Data
* Data
 +
* Measures of center, dispersion/variation, skewness, flatness
* Design of experiments
* Design of experiments
* R data management (Import and Export)
* R data management (Import and Export)
Line 42: Line 43:
==Chapter IV: Probability Models==
==Chapter IV: Probability Models==
 +
* Fundamentals
 +
* Rules for Computing Probabilities
 +
* Probabilities Simulations
 +
* Counting Principles
==Chapter V: Statistical Models==
==Chapter V: Statistical Models==
Line 61: Line 66:
* Law of Large Numbers (First Fundamental Law of Probability Theory)
* Law of Large Numbers (First Fundamental Law of Probability Theory)
* Central Limit Theorem (Second Fundamental Law of Probability Theory)
* Central Limit Theorem (Second Fundamental Law of Probability Theory)
 +
* Relations between Distributions (Distributome)
==Chapter VIII: Multivariate Statistics==
==Chapter VIII: Multivariate Statistics==
* Parametric (simple and multivatiate) regression
* Parametric (simple and multivatiate) regression
-
* Parametric ANOVA
+
* Parametric ANOVA/ANCOVA/MANCOVA
 +
* Logistic Regression
* Parametric assumptions and model validation
* Parametric assumptions and model validation
* Non-parametric linear modeling
* Non-parametric linear modeling
Line 70: Line 77:
* Genome-wide association studies (GWAS)
* Genome-wide association studies (GWAS)
-
==Chapter IX:==
+
==Chapter IX: Multinomial Experiments and Inference==
 +
* Chi-square
-
==Chapter II:==
+
==Chapter X: Parameter Estimation==
 +
* MOM
 +
* MLE
-
==Chapter II:==
+
==Chapter XI: Bayesian Inference==
-
==Chapter II:==
+
==Chapter XII: Dimensionality Reduction==
 +
* PCA
 +
* ICA
-
==Chapter II:==
+
==Chapter XIII: Classification Methods==
 +
* Supervised classification methods (Support Vector Machines, SVM, ADABOOST)
 +
* Unsupervised (K-means clustering, hierarchical clustering)
 +
 
 +
== Chapter XIV: Survival Analysis==
 +
 
 +
== Chapter XV: Mixture modeling==
 +
 
 +
== Chapter XVI: Causality==
 +
 
 +
==Appendix==
<hr>
<hr>
{{translate|pageName=http://wiki.stat.ucla.edu/socr/index.php?title=Statistics_for_life_and_health_sciences_EBook}}
{{translate|pageName=http://wiki.stat.ucla.edu/socr/index.php?title=Statistics_for_life_and_health_sciences_EBook}}

Revision as of 23:47, 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
  • Measures of center, dispersion/variation, skewness, flatness
  • 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

  • Fundamentals
  • Rules for Computing Probabilities
  • Probabilities Simulations
  • Counting Principles

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)
  • Relations between Distributions (Distributome)

Chapter VIII: Multivariate Statistics

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

Chapter IX: Multinomial Experiments and Inference

  • Chi-square

Chapter X: Parameter Estimation

  • MOM
  • MLE

Chapter XI: Bayesian Inference

Chapter XII: Dimensionality Reduction

  • PCA
  • ICA

Chapter XIII: Classification Methods

  • Supervised classification methods (Support Vector Machines, SVM, ADABOOST)
  • Unsupervised (K-means clustering, hierarchical clustering)

Chapter XIV: Survival Analysis

Chapter XV: Mixture modeling

Chapter XVI: Causality

Appendix




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