Drawing with Math vs Learn Poisson Binomial Normal

Our “Drawing with Math (Vector and Pixel Art)” app combines two main components: * uniquely versatile image editors, * various methods of linear transformations of both user-entered and system images. Image editors are available in vector and pixelated forms. The editors offer such options as: * free drawing, * entry of all standard shapes, * entry of regular Cartesian, polar, or parametric function into image editor. List of several visually appealing functions is provided. The App stores 10 most recent user entries for each type. Vector images can be linearly transformed in a variety of user-defined ways: * 2x2 matrix, * eigenvalues and eigenvectors, * rotation by degree, * reflection across a given vector. Pixelated images can be compressed by segments to user-selected rank. Mathematical context for the above is provided, whenever possible. Explanation of image compression algorithms requires significant understanding of Linear Algebra, and is available in our “Matrix Solver Step by Step” app. Several bonus images with animations and editable greeting cards are also available. Suggestions and any other feedback are welcome! Write to us at graphmath@aol.com.

Explore three key probability distributions (Poisson, Binomial, Normal) using hands-on and visual learning! Designed by your friendly neighborhood math instructor of 25 years, this app is classroom tested and student approved. Includes mini-lessons explaining how each works, broken into digestible chunks for tabbing through. With these as background info, exploring with the applets will quickly build a full grasp of what is happening! Got hurricanes making landfall? Customers arriving in a queue? Use Poisson. Rolling twelve dice? Surveying 30 students? Use binomial. Weighing a wombat wandomly [sic] taken from the woods? Use normal. But what kinds of results can you expect, in each case? This app provides the insights! Recommended for students in an introductory statistics course, or any other course where probability distributions are important.