About this course
Matrix Algebra underlies many of the current tools for experimental design and the analysis of high-dimensional data. In this introductory online course in data analysis, 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 to perform matrix operations.
About this course
Created specifically for those who are new to the study of probability, or for those who are seeking an approachable review of core concepts prior to enrolling in a college-level statistics course, Fat Chance prioritizes the development of a mathematical mode of thought over rote memorization of terms and formulae.
Through highly visual lessons and guided practice, this course explores the quantitative reasoning behind probability and the cumulative nature of mathematics by tracing probability and statistics back to a foundation in the principles of counting.
About this course
In this course, we go beyond the calculus textbook, working with practitioners in social, life and physical sciences to understand how calculus and mathematical models play a role in their work.
Through a series of case studies, you’ll learn:
- How standardized test makers use functions to analyze the difficulty of test questions;
- How economists model interaction of price and demand using rates of change, in a historical case of subway ridership;
- How an x-ray is different from a CT-scan, and what this has to do with integrals;
- How biologists use differential equation models to predict when populations will experience dramatic changes, such as extinction or outbreaks;
- How the Lotka-Volterra predator-prey model was created to answer a biological puzzle;
- How statisticians use functions to model data, like income distributions, and how integrals measure chance;
- How Einstein’s Energy Equation, E=mc2 is an approximation to a more complicated equation.
