May 24, 2018: Beginner’s statistics in R
Description
This course has two objectives. First, it will introduce the participants to the basics of statistical experimental design, data analysis, and statistical inference. It will cover topics such as optimal allocation of resources, confidence intervals, hypothesis testing, and linear regression. Second, it will introduce the practical steps of statistical analysis using the opensource environment R. In addition to discussing basic data management tasks in R, such as reading in data and performing basic analysis, it also contains introduction to reproducible research using R markdown. The course will contain both lectures and practical handson exercises.
Target audience
 Target audience are experimental scientists with no prior knowledge of statistics or R. The course will use two textbooks:

 Bremer & Doerge. ‘Using R at the Bench: StepbyStep Data Analytics for Biologists’, Cold Spring Harbor LaboratoryPress, 2015
 Diez, Barr, & CetinkayaRundel. ‘OpenIntro Statistics’, free online
Reference
‘Points of Significance’ in Nature Methods
Speakers
 Meena Choi, Laurent Gatto, Olga Vitek
Tentative schedule
Wednesday, May 2, 2018
 12:30 p.m. Registration
 1:30 p.m. Lecture : Introduction to Statistics
 3:00 p.m. Refreshments
 3:30 p.m. Introduction to R and RStudio
 5:00 p.m. R markdown
Thursday, May 3, 2018
 8:00 a.m. Q&A
 9:00 a.m. Data exploration
 10:30 a.m. Refreshments
 11:00 a.m. Data visualization
 12:30 p.m. Lunch
 1:30 p.m. Lecture : Statistical inference
 3:00 p.m. Refreshments
 3:30 p.m. Basic statistics – randomization, statistical summaries, error bars and confidence
 5:00 p.m. Extra practice
 6:00 p.m. Adjourn
Friday, May 4, 2018
 8:00 a.m. Q&A
 9:00 a.m. Lecture : sample size, linear regression and categorical data
 10:30 a.m. Refreshments
 11:00 a.m. Statistical hypothesis test
 12:30 p.m. Lunch
 1:30 p.m. Sample size calculation, analysis of categorical data
 3:00 p.m. Refreshments
 3:30 p.m. Linear model and correlation
 5:00 p.m. Wrapup