May 13, 2019: 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
Schedule
Wednesday, May 1, 2019
 12:30 p.m. Registration
 1:30 p.m. Lecture: Introduction to Statistics, Olga Vitek
 3:00 p.m. Refreshments
 3:30 p.m. Introduction to R and RStudio, Laurent Gatto
 5:00 p.m. R markdown, Laurent Gatto
 6:00 p.m. Dinner
Thursday, May 2, 2019
 8:00 a.m. Q&A
 9:00 a.m. Data exploration, Laurent Gatto
 10:30 a.m. Refreshments
 11:00 a.m. Data exploration 2 (dplyr), Laurent Gatto
 12:30 p.m. Lunch
 1:30 p.m. Lecture : Principal of experimental design and statistical inference, Olga Vitek
 3:00 p.m. Refreshments
 3:30 p.m. Data visualization, Laurent Gatto
 5:00 p.m. Extra practice, Laurent Gatto
 6:00 p.m. Adjourn
Friday, May 3, 2019
 8:00 a.m. Q&A
 9:00 a.m. Basic statistics – randomization, statistical summaries, confidence interval, Meena Choi
 10:30 a.m. Refreshments
 11:00 a.m. Lecture : sample size, linear regression, and categorical data, Olga Vitek
 12:30 p.m. Lunch
 1:30 p.m. Statistical hypothesis test, analysis of categorical data, Meena Choi
 3:00 p.m. Refreshments
 3:30 p.m. sample size calculation, linear model and correlation, Msnbase, Meena Choi/Laurent Gatto
 5:00 p.m. Wrapup