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 |
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Website | Michael Friendly's personal website | http://www.datavis.ca/courses/index.php | A variety resources and code for producing graphs and analyzing data | Graphics; Linear Models; Structural Equation Models; Categorical Models | 3 | 3 | No | SAS, R |

Website | Introduction to R Seminar | https://stats.idre.ucla.edu/r/seminars/intro/ | Tips about R programming. | R; R programming | . | . | No | R |

Website | Introduction to the Cochran-Mantel-Haenszel Test | https://cran.r-project.org/web/packages/samplesizeCMH/vignettes/samplesizeCMH-introduction.html | The Cochran-Mantel-Haenszel test (CMH) is an inferential test for the association between two binary variables, while controlling for a third confounding nominal variable. A good introduction to the test along with R code for various aspects of the test including sample size/power calculations. | Cochran-Mantel-Haenszel test; power | 3 | 3 | No | R |

Website | Psych Networks | http://psych-networks.com/ | Rich website for the use of network analysis, particularly for analyses in psychology by one of the chief proponents of the technique. In many aspects, the analyses are similar to those of factor analysis, but with differing underlying assumptions. Longitudinal models are included. | network analysis; factor analysis | 4 | 4 | No | R |

Website | UCLA Institute for Digital Research and Education | https://stats.idre.ucla.edu/ | Lots of resources like software (SAS, R, STATA, SPSS, M+), data analysis examples, codes and output, SAS macros, how to choose correct statistical tests, etc. | SAS; R; STATA; SPSS; M+; data analysis examples; SAS code; SAS macros; choose correct statistical tests | 4 | 4 | No | SAS, SPSS, R, M+, |

Website | UCLA Institute for Digital Research & Education Data Analysis Examples | https://stats.idre.ucla.edu/other/dae/ | This page contains links to examples illustrating the application of different statistical analysis techniques using different statistical packages. | regression models; count models; censored and truncated regression; multivariate analysis; mixed effect models; power analysis | 3 | 4 | No | SAS, SPSS, R, M+, |

Book | R for Data Science | http://r4ds.had.co.nz/ | This is the website has all of the content of the book "R for Data Science". It will teach you how to do data science with R: You'll learn how to get your data into R, get it into the most useful structure, transform it, vizualise it and model it. In this book, you will find a practicum of skills for data science. Just as a chemist learns how to clean test tubes and stock a lab, you'll learn how to clean data and draw plots-and many other things besides. These are the skills that allow data science to happen, and here you will find the best practices for doing each of these things with R. You'll learn how to use the grammar of graphics, literate programming, and reproducible research to save time. You'll also learn how to manage cognitive resources to facilitate discoveries when wrangling, vizualising, and exploring data. | data science; R programming; data visualization; R workflow; data transformations; R markdown; | 3 | 3 | No | Any, R |

Book | Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling | https://www.amazon.com/Multilevel-Analysis-Introduction-Advanced-Modeling/dp/184920201X/ref=mt_paperback?_encoding=UTF8&me= | Introduction to multilevel analysis, topics include hierarchical linear model, random coefficients models, how to calculate ICC, how much does a model explain, etc. | multilevel analysis; hierarchical linear model; random coefficients models; intra-class correlation coefficient; | 4 | 5 | Yes | SAS, SPSS, R, M+, |

Online Interactive Course | Statistical Learning with Applications in R | https://www.youtube.com/watch?v=3jQs02dbfrI&list=PL06ytJZ4Ak1rXmlvxTyAdOEfiVEzH00IK | Reference: (Book) (Chapter 2) An Introduction to Statistical Learning with Applications in R (Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani) http://www-bcf.usc.edu/~gareth/ISL/IS... | Statistical learning methods | 4 | 4 | No | R |

Online Interactive Course | Penn State Online Applied Statistics | https://onlinecourses.science.psu.edu/statprogram/ | 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 |

Online Interactive Course | Introduction to R | https://www.datacamp.com/courses/free-introduction-to-r | Learning the basics in R programming | R; R programming | . | . | No | R |

Online Interactive Course | R studio | https://www.rstudio.com/online-learning/ | R studio online learning | R; R programming | . | . | No | R |

Online Interactive Course | Introduction to R for Data Science | https://www.edx.org/course/introduction-r-data-science-microsoft-dat204x-7 | Master the basics in R | R; R programming | . | . | No | R |

Online Interactive Course | R Basics - R Programming Language Introduction | https://www.udemy.com/r-basics/ | learn about the basic structure of R including packages | R; R programming | . | . | No | R |

Online Interactive Course | Try R | https://www.codeschool.com/courses/try-r | Learn the R programming language for data analysis and visualization. This software programming language is great for statistical computing and graphics. | R; R programming | . | . | No | R |

Online Interactive Course | Statistics with R Specialization | https://www.coursera.org/specializations/statistics | Per the course website, this non degree course offered by Duke University helps to learn to analyze and visualize data in R and create reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform frequentist and Bayesian statistical inference and modeling to understand natural phenomena and make data-based decisions, communicate statistical results correctly, effectively, and in context without relying on statistical jargon, critique data-based claims and evaluated data-based decisions, and wrangle and visualize data with R packages for data analysis. | Statistics; R | . | . | Yes | R |

Online Interactive Course | R Programming | https://www.coursera.org/learn/r-programming | Per the course website, this course prepared by JHU will help learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples. | R | . | . | Yes | R |

Online Interactive Course | Mastering Software Development in R Specialization | https://www.coursera.org/specializations/r | Per the course website, this is a 5-course specialiation prepared by JHU. This Specialization covers R software development for building data science tools. As the field of data science evolves, it has become clear that software development skills are essential for producing useful data science results and products. You will obtain rigorous training in the R language, including the skills for handling complex data, building R packages and developing custom data visualizations. You will learn modern software development practices to build tools that are highly reusable, modular, and suitable for use in a team-based environment or a community of developers. | R; software development | . | . | Yes | R |

Online Interactive Course | DataCamp - courses for Data Science | https://www.datacamp.com/home | This provides high quality courses on Data Science at both an introductory and advanced level using either R or Python. Some courses are free, others require a monthly fee to access. | Data Science; SQL; importing and cleaning data; time series; machine learning; | 2 | 1 | Yes | R, Other |

Software to Download | G*Power | http://www.gpower.hhu.de/ | 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 | https://lagunita.stanford.edu/courses/HumanitiesSciences/StatLearning/Winter2016/about | 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 |