Statistical Methods for Continuous Data Analysis Using R
To Know
About this Class
What are common statistical analyses for continuous data? Can you check whether your continuous outcome is normally distributed? What are the methods when the data are not normal? How do you model the outcome with multiple predictors in regression?
This is a two-hour lecture intended for those doing basic data analysis using R. Basic R programming is a pre-requisite for this course, as is knowledge of basic statistical concepts, such as mean and p-value from statistical hypothesis testing. This class will be taught by the Clinical Center's Biostatistics and Clinical Epidemiology Service (CC/BCES).
The learning outcomes include:
- calculating and displaying descriptive statistics, such as center and spread of distribution and boxplots
- recognizing common continuous probability density functions
- estimating mean and confidence intervals for the center of normally and non-normally distributed data
- hypothesis testing for one-sample and two-sample
- linear regression
- the F-distribution and one-way ANOVA
R code snippets will be shared during the lecture and within lecture notes. The class will be recorded, so you can go back to the material as you begin to do your own modeling. During the class, time will be devoted to explaining the concepts, and code snippets and output and references will be provided for in-depth material.
Preclass Requirements: You must take the basic R programming and statistical inference – Part I classes as pre-requisite through the NIH Library or have acquired the equivalent knowledge elsewhere prior to registering for this class.
Statistical Software: We will be using R and RStudio for our statistical analysis. R is open source and free. There are versions for Mac OSX, Windows, and Linux. You can download it from https://cran.r-project.org/. Additionally, we will be using RStudio as a graphical interface for R. RStudio is free for everyone to download at https://posit.co/download/rstudio-desktop/. See above for pre-requisites in R programming.