Principal Component Analysis
Statistical Methods for Life Sciences
Preface
Probability Theory
1
Introduction to probability
2
Discrete random variables
Exercises: Discrete random variables
3
Continuous random variable
4
Sampling and experimental design
Exercises: Continuous random variables
Statistical Inference
5
Introduction to hypothesis tests
6
Hypothesis testing using resampling
Exercises: Hypothesis tests, resampling
7
Parametric tests
8
Multiple testing
Exercises: Hypothesis tests, parametric
9
Point and interval estimates
Exercises: Point and interval estimates
Linear regression
10
Introduction to linear models
11
Linear models: regression and classification
12
Common cases
Exercises (introduction to linear models)
Exercises (model diagnostics)
Exercises (regularization)
Exercises (regression coefficients)
Principal Component Analysis
13
Background
14
Exercises: Principal component analysis
Clustering
15
Clustering: art of finding groups
Exercises
Generalized Linear Models
16
Generalized linear models
Mixed models
17
Simple linear regression
18
Mixed models
19
Mixed models (mathematical details)
Survival Analysis
20
Introduction
21
Regression with survival response
22
R examples
References
Table of contents
Learning outcomes
Principal Component Analysis
Author
Mun-Gwan Hong, Payam Emami
Learning outcomes
Understand the concept of principal component analysis (PCA)
Understand and be able to perform PCA
Understand the loading/score plot
Exercises (regression coefficients)
13
Background