Preface
I Introduction
1 Introduction of R
2 Plot in R
3 Correlation
II Statistics
4 Standard deviation vs Standard error
- 4.1 Reference
5 T test
6 Linear model
7 MRG
- 7.1 Read the data
8 Multiple linear regression
- 8.1
- 8.2
9 Bayesian newwork in R
- 9.1 Practical experiments
  - 9.1.1 Analysis of the MAGIC population in Scutari et al., Genetics (2014)
  - 9.1.2 Reference
10 The rationale for using negative binomial distribution to model read count in RNA-seq data
11 Multiple test corretion
12 Working with missing values
13 Data wrangling
III Machine learning
14 PCA
15 Random forest
- 15.1 Practical experiments
  - 15.1.1 Random forest for prediction of iris
16 QTL analysis
- 16.1 Reference
References
17 Doing Bayesian statistics in R
- 17.1 Reference
18 Transformation of data
- 18.1 Log transformation
  - 18.1.1 The rationale of using log2 transformation
  - 18.1.2 Reference
19 Goodness of fit in R
- 19.1 Reference
20 Overdispersion
- 20.1 Recognising (and testing for) overdispersion
- 20.2 Refefernce
21 Package management
22 Permutation and combination
- 22.1 Reference
23 Partial least square analysis
24 Scale
- 24.1 Reference
25 Web scraping
- 25.1 Download Content from NCBI Databases using RISmed
  - 25.1.1 Search author name
  - 25.1.2 Search keyword
26 Linear mixed model in R

Notes of R for Bioinformatics

4 Standard deviation vs Standard error

4.1 Reference

https://datascienceplus.com/standard-deviation-vs-standard-error/