Unit 1: Introduction to Vectors
Unit 2: Introduction to Matrices
Unit 3: Geometric Interpretation of Vectors
Linear Algebra for Biostatistics
A comprehensive course designed to equip prospective MS-Biostatistics students with the essential linear algebra skills needed for statistical modeling, data analysis, and advanced biostatistical techniques.
Unit 1: Matrix Multiplication
Unit 2: Linear Transformations
Unit 3: Applications of Linear Transformations
Unit 1: Representing Systems of Equations in Matrix Form
Unit 2: Gaussian Elimination
Unit 3: Types of Solutions
Unit 1: Vector Spaces
Unit 2: Subspaces
Unit 3: Linear Independence, Span, and Basis
Unit 1: Calculating Eigenvalues
Unit 2: Calculating Eigenvectors
Unit 3: Diagonalization of Matrices
Unit 1: Formulating Linear Regression Models
Unit 2: Estimating Regression Coefficients
Unit 3: Assessing Model Fit
Unit 1: Introduction to PCA
Unit 2: Performing PCA
Unit 3: Interpreting Principal Components
Unit 1: Introduction to SVD
Unit 2: Applying SVD
Unit 3: Pseudoinverses
Unit 1: Linear Algebra in R
Unit 2: Linear Algebra in Python
Unit 3: Applications in Statistical Analysis
Unit 1: Understanding Ill-Conditioning
Unit 2: Numerical Stability
Unit 3: Mitigation Techniques
Unit 1: Multivariate Data Representation
Unit 2: Multivariate Analysis of Variance (MANOVA)
Unit 3: Canonical Correlation Analysis