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.

Course By | Instructor |
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Harvard University | Rafael Irizarry, Michael Love |

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.

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

**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

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