Introduction To Statistical Analysis In R

This workshop will focus on choosing an appropriate statistical test given data and hypothesis. Specific topic include the following: t-tests, ANOVA, non-parametric tests, Chi-square test and Fisher’s exact test.

Introduction to GraphPad Prism, data tables, and data entry, formatting data, specifying groups, or entire data sets, changing the range and measurements scales as we as exporting.

An introduction to the R programming language and the components of programming including syntax, data types, dataframes and basic data analysis.

Crash course in R and BioConductor: Introduction to R and how to program R, create graphs and figures and write scripts.

Automating biostatistics workflows using R-based webtools

R programming training for building better graphs with best practices and hand on demos to take your graphs to the next level.

Graphpad Prism Charting & Statistics including training on grouped bar charts, XY plots, two columns layouts and customizing your charts.

Overview of statistics: Statistical testing, data handling and data quality

Design of experiments, introduces the statistical considerations and the process of planning, completing and analyzing data. Classis DOE attempts to coerce all experiments into a handful of well know designs. Modern statistics software is covered that enables flexible but sensible designs.

Description of common categorical models including contingency tables, Fisher's exact test, contingency tables, and linear models.

Intro to JMP for statistics including JMP file types, setup, importing data, working with rows and columns, etc.