Difference between revisions of "Statistics for life and health sciences EBook"
| (One intermediate revision by the same user not shown) | |||
| Line 31: | Line 31: | ||
==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 Parametric Models and Inference== |
| − | |||
| − | |||
* Hypothesis testing foundations | * Hypothesis testing foundations | ||
* Type I and II errors, Power, sensitivity, specificity | * Type I and II errors, Power, sensitivity, specificity | ||
| + | * Parametric Assumptions | ||
===One sample inference=== | ===One sample inference=== | ||
| Line 58: | Line 62: | ||
* Paired samples | * Paired samples | ||
| − | ==Chapter | + | ==Chapter VI: Limiting Theorems== |
* 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 | + | ==Chapter VII: 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 76: | ||
* Genome-wide association studies (GWAS) | * Genome-wide association studies (GWAS) | ||
| − | ==Chapter IX:== | + | ==Chapter VIII: Multinomial Experiments and Inference== |
| + | * Chi-square | ||
| + | |||
| + | ==Chapter IX: Parameter Estimation== | ||
| + | * MOM | ||
| + | * MLE | ||
| + | |||
| + | ==Chapter X: Bayesian Inference== | ||
| + | |||
| + | ==Chapter XI: Dimensionality Reduction== | ||
| + | * PCA | ||
| + | * ICA | ||
| + | |||
| + | ==Chapter XII: Classification Methods== | ||
| + | * Supervised classification methods (Support Vector Machines, SVM, ADABOOST) | ||
| + | * Unsupervised (K-means clustering, hierarchical clustering) | ||
| − | ==Chapter | + | == Chapter XIII: Survival Analysis== |
| − | ==Chapter | + | == Chapter XIV: Mixture modeling== |
| − | ==Chapter | + | == Chapter XV: 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}} | ||
Latest revision as of 18:57, 18 March 2013
Welcome to the UCLA Statistics for the Biomedical and Health Sciences (Stats 13) electronic book (EBook).
Contents
- 1 Preface
- 2 Chapter I: Introduction to Statistics
- 3 Chapter II: Data and variability
- 4 Chapter III: Randomization-based statistical inference
- 5 Chapter IV: Probability Models
- 6 Chapter V: Statistical Parametric Models and Inference
- 7 Chapter VI: Limiting Theorems
- 8 Chapter VII: Multivariate Statistics
- 9 Chapter VIII: Multinomial Experiments and Inference
- 10 Chapter IX: Parameter Estimation
- 11 Chapter X: Bayesian Inference
- 12 Chapter XI: Dimensionality Reduction
- 13 Chapter XII: Classification Methods
- 14 Chapter XIII: Survival Analysis
- 15 Chapter XIV: Mixture modeling
- 16 Chapter XV: Causality
- 17 Appendix
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 Parametric Models and Inference
- Hypothesis testing foundations
- Type I and II errors, Power, sensitivity, specificity
- Parametric Assumptions
One sample inference
- T-Test
- Normal Z-test
- Confidence intervals
Two sample inference
- Independent samples
- Paired samples
Chapter VI: 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 VII: 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 VIII: Multinomial Experiments and Inference
- Chi-square
Chapter IX: Parameter Estimation
- MOM
- MLE
Chapter X: Bayesian Inference
Chapter XI: Dimensionality Reduction
- PCA
- ICA
Chapter XII: Classification Methods
- Supervised classification methods (Support Vector Machines, SVM, ADABOOST)
- Unsupervised (K-means clustering, hierarchical clustering)
Chapter XIII: Survival Analysis
Chapter XIV: Mixture modeling
Chapter XV: Causality
Appendix
Translate this page:
