You will find here nine interesting topics that you won't learn in college classes. Most have interesting applications in business and elsewhere. They are not especially difficult, and I explain them in simple English. Yet they are not part of the traditional statistical curriculum, and even many data scientists with a PhD degree have not heard about some of these concepts.
The topics discussed in this article include:
- Random walks in one, two and three dimensions - With Video
- Estimation of the convex hull of a set of points - Application to clustering and oil industry
- Constrained linear regression on unusual domains - Application to food industry
- Robust and scale-invariant variances
- Distribution of arrival times of extreme events - Application to flood predictions
- The Tweedie distributions - Numerous applications
- The arithmetic-geometric mean - Fast computations of decimals of Pi
- Weighted version of the K-NN clustering algorithm
- Multivariate exponential distribution and storm modeling
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