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We will teach a review of linear algebra, including matrix notation, which underlies many of the current tools for analyzing large-dimensional data. We will then use linear models to represent differences between experimental units and perform statistical inference on these differences.

Topics:

• Linear algebra: matrix notation, matrix operations
• Linear models: comparing groups of samples, multiple variables, contrasts, and interaction terms.

This class was supported in part by NIH grant R25GM114818.

This course is part of a larger set of 8 total courses running Self-Paced through September 15th, 2015:

PH525.1x: Statistics and R for the Life Sciences

PH525.2x: Introduction to Linear Models and Matrix Algebra

PH525.3x: Advanced Statistics for the Life Sciences

PH525.4x: Introduction to Bioconductor

PH525.5x: Case study: RNA-seq data analysis

PH525.6x: Case study: Variant Discovery and Genotyping

PH525.7x: Case study: ChIP-seq data analysis

PH525.8x: Case study: DNA methylation data analysis

HarvardX pursues the science of learning. By registering as an online learner in an HX course, you will also participate in research about learning. Read our research statement to learn more.

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