Introduction to Linear Models and Matrix Algebra

By: Harvard University
Free
Intermediate,
Duration: 4 Weeks

Course Highlights

  • Matrix algebra notation
  • Linear models
  • Introduction to the QR decomposition

Learn to use R programming to apply linear models to analyze data in life sciences.

Matrix Algebra underlies many of the current tools for experimental design and the analysis of high-dimensional data.

Check Out  Harvard Course on Productivity Tools
Course ByInstructor
Harvard UniversityRafael Irizarry, Michael Love

Course Details

In this introductory data analysis course, we will use matrix algebra to represent the linear models that commonly used to model differences between experimental units. We perform statistical inference on these differences. Throughout the course, we will use the R programming language.



What you will learn?

  • Matrix algebra notation
  • Matrix algebra operations
  • Application of matrix algebra to data analysis
  • Linear models
  • Brief introduction to the QR decomposition

Other Details

Course Instructors

Rafael Irizarry: Professor of Biostatistics, T.H. Chan School of Public Health
Michael Love: Assistant Professor, Departments of Biostatistics and Genetics, UNC Gillings School of Global Public Health

Check Course Content, Faqs, Rating and other important information about this course.

Details here


Disclaimer

We have tried to provide the best updated information about this Introduction to Linear Models and Matrix Algebra course. However, if you find this course is not available or if there are any changes to this free course of Introduction to Linear Models and Matrix Algebra then do let us know. Our team will make the necessary changes.



0 0 vote
Article Rating
Subscribe
Notify of
0 Comments
Inline Feedbacks
View all comments