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 variables
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
10
Analysis of variance (ANOVA)
Linear regression
11
Introduction to linear models
12
Linear models: regression and classification
13
Common cases
Exercises (introduction to linear models)
Exercises (model diagnostics)
Exercises (regularization)
Exercises (regression coefficients)
Principal Component Analysis
14
Background
15
Exercises: Principal component analysis
Clustering
16
Clustering: art of finding groups
Exercises
Generalized Linear Models
17
Generalized linear models
Mixed models
18
Simple linear regression
19
Mixed models
20
Mixed models (mathematical details)
21
Introduction to Mixed Models
22
Mixed models II
Survival Analysis
23
Introduction
24
Regression with survival response
25
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)
14
Background