There are more than a dozen different fit statistics researchers use to assess their confirmatory factor analyses and structural equation models. Here we have assembled a list of the most popular fit statistics used and recommended cut-offs that indicate a good fit.
RStudio Cloud makes it easy for professionals, hobbyists, trainers, teachers and students to do, share, teach and learn data science using R. Create analyses using RStudio directly from your browser - there is no software to install and nothing to configure on your computer. Share your projects - and access those of others - without worrying about data transfer or package installation. Each project defines its own environment, and RStudio Cloud automatically reproduces that environment whenever anyone accesses the project. It’s easy to share analyses with the world - but it’s also simple to collaborate with a select group in a private space. You control who can enter a space - and via roles, you have fine grained control over what each user can do. There are also many learning materials available: interactive tutorials covering the basics of data science, cheatsheets for working with popular R packages, links to Datacamp courses, and a guide to using RStudio Cloud.
The aim of this course is to cover sampling design and analysis methods that would be useful for research and management in many field. A well designed sampling procedure ensures that we can summarize and analyze data with a minimum of assumptions and complications. Perfect for both students and teachers wanting to learn/acquire materials for this topic.
Epidemiology is the study of the distribution and determinants of human disease and health outcomes, and the application of methods to improve human health. This course examines the methods used in epidemiologic research, including the design of epidemiologic studies and the collection and analysis of epidemiological data. Perfect for students and teachers wanting to learn/acquire materials for this topic.
Those who complete this course will be able to select appropriate methods of multivariate data analysis, given multivariate data and study objectives; write SAS and/or Minitab programs to carry out multivariate data analyses; and interpret results of multivariate data analyses. Perfect for students and teachers wanting to learn/acquire materials for this topic.
This is a graduate level survey course that stresses the concepts of statistical design and analysis in biomedical research, with special emphasis on clinical trials. Perfect for students and teachers wanting to learn/acquire materials for this topic.
Visual ANOVA is a simple little program that lets you put all this theory we've been describing into a simple visual whole. It assumes that you've read the Meanings and Intuitions section and have have understood the the general ideas at least. Even if your understanding of the previous section is incomplete at this time, it is worth playing with Visual ANOVA since that may clear up the big picture of ANOVA for you.
Random is a website devoted to probability, mathematical statistics, and stochastic processes, and is intended for teachers and students of these subjects. The site consists of an integrated set of components that includes expository text, interactive web apps, data sets, biographical sketches, and an object library.
The focus of this class is a multivariate analysis of discrete data. We will learn basic statistical methods and discuss issues relevant for the analysis of some discrete distribution, cross-classified tables of counts, (i.e., contingency tables), success/failure records, questionnaire items, judge's ratings, etc. Being familiar with matrix algebra is helpful in completing this course. Perfect for students and teachers wanting to learn/acquire materials for this topic.
Statistics is often taught as though the design of the data collection and the data cleaning have already been done in advance. However, as most practicing statisticians quickly learn, typically problems that arise at the analysis stage, could have been avoided if the experimenter had consulted a statistician before the experiment was done and the data were conducted. This course is created to provide an understanding of how experiments should be designed so that when the data are collected, these shortcomings are avoided. Perfect for students and teachers wanting to learn/acquire materials for this topic.