Includes detailed PowerPoints for 20 lectures for topics including generalized linear models, logistic regression, and random effects models.
Includes detailed PowerPoints for 20 lectures for topics including generalized linear models, logistic regression, and random effects models.
A TI graphing calculator emulator. Emulates the TI-82, TI-83, TI-83 Plus, TI-85, TI-86, TI-89, TI-92, TI-92 II, and TI-92 Plus. Features a graphical debugger, grayscale, send/receive, black-link, parallel link and more. User must transfer calculator's ROM to the computer through TI-Graph Link.
This is a graduate level introduction to statistics including topics such as probabilty/sampling distributions, confidence intervals, hypothesis testing, ANOVA, and regression. Perfect for students and teachers wanting to learn/acquire materials for this topic.
This course covers methodology, major software tools and applications in data mining. By introducing principal ideas in statistical learning, the course will help students to understand conceptual underpinnings of methods in data mining. It focuses more on usage of existing software packages (mainly in R) than developing the algorithms by the students. The topics include statistical learning; resampling methods; linear regression; variable selection; regression shrinkage; dimension reduction; non-linear methods; logistic regression, discriminant analysis; nearest-neighbors; decision trees; bagging; boosting; support vector machines; principal components analysis; clustering. Perfect for students and teachers wanting to learn/acquire materials for this topic.
The emphasis in this course will be understanding statistical testing and estimation in the context of "omics" data so that you can appropriately design and analyze a high-throughput study. Since the measurement technologies are evolving rapidly, important objectives of the course are for students to gain a basic understanding of statistical principles and familiarity with flexible software tools so that you can continue to assess and use new statistical methodology as it is developed for new types of data.
By the end of the course, you should be able to tailor the analysis of your data to your needs while maintaining statistical validity. You should come out of the course with insight so that you can assess the validity of new statistical methodologies as they are introduced as well as understand appropriate statistical analyses for data types not discussed in the class.
Perfect for students and teachers wanting to learn/acquire materials for this topic.
The objective of this course is to learn and apply statistical methods for the analysis of data that have been observed over time. Our challenge in this course is to account for the correlation between measurements that are close in time. Perfect for students and teachers wanting to learn/acquire materials for this topic.
This resource is designed to provide new users to R, RStudio, and R Markdown with the introductory steps needed to begin their own reproducible research. Many screenshots and screencasts (with no audio) will be included, but if further clarification is needed on these or any other aspect of the book, please create a GitHub issue here or email me with a reference to the error/area where more guidance is necessary. It is recommended that you have R version 3.3.0 or later, RStudio Desktop version 1.0 or higher, and rmarkdown R package version 1.0 or higher.
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.
Everyday, massive amounts of data are generated in every part of our lives. That makes data fluency an indispensable skill to help you succeed - no matter what industry you’re in -- and DataCamp is here to help. With bite-sized lessons in how to use R, Python, and SQL for data science, DataCamp allows you to learn in a way that fits your schedule, on any device.
9 courses are available for those who create a free account, and 123 courses are available for individuals who sign up for a $29/month membership ($25/month is you pay yearly). Businesses can also pay $300 per member per year for employees to learn these skills.
swirl is a software package for the R programming language that turns the R console into an interactive learning environment. Users receive immediate feedback as they are guided through self-paced lessons in data science and R programming.