BERD Resource by Statistical Package

Type of Resource Title URL Description - Provide helpful comments about this resource Keywords - separate by semicolons Programming knowledge required (1=none to 6 professional programmer) Statistical knowledge level required (1=none, 4=masters level 6=PhD level) Is there a cost associated with this resource StatPackage
Website Michael Friendly's personal website A variety resources and code for producing graphs and analyzing data Graphics; Linear Models; Structural Equation Models; Categorical Models 3 3 No SAS, R
Online Interactive Course Statistical Learning with Applications in R Reference: (Book) (Chapter 2) An Introduction to Statistical Learning with Applications in R (Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani) Statistical learning methods 4 4 No R
Online Interactive Course Penn State Online Applied Statistics Penn State offers both a certificate and a masters degree program in applied statistics. All coursework can be taken online. Individual courses can also be taken. masters programs; certificate program 2 2 Yes SAS, R
Software to Download G*Power G*Power is a tool to compute statistical power analyses for many different t tests, F tests, chi-sq tests, z tests and some exact tests. G*Power can also be used to compute effect sizes and to display graphically the results of power analyses. power;sample size;software 2 4 No Any, SAS, SPSS, R,
Video Instruction Statistical Learning Self Paced Course The active course run for Statistical Learning has ended, but the course is now available in a self paced mode. You are welcome to join the course and work through the material and exercises at your own pace. When you have completed the exercises with a score of 50% or higher, you can generate your Statement of Accomplishment from within the course. The course will remain available for an extended period of time. We anticipate the content will be available until at least December 31, 2020. You will be notified by email of any changes to content availability beforehand. Statistical Learning 4 4 No R