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  1. Proceedings of the conference on APL '99 : On track to the 21st century (APL '99)
  2. When bears are blue and bulls are red
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Generalization of Pick's theorem for surface of polyhedra
When bears are blue and bulls are red
Dynamic systems simulation using APL2
Functions and data can dance as equal partners
The Zark library of utility functions
Choices in server-side programming: a comparative programming exercise
Regions: an abstraction for expressing array computation
Accelerating APL programs with SAC
Sparse arrays in J
INFO: interactive APL documentation
A retro/prospective on APL Graphpak
Teaching J as a computer notation for secondary mathematics
An object-oriented approach to educational software in building physics
APL-generated teaching and testing items to enhance a student's ability to discover functional relationships
GFSR pseudorandom number generation using APL

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When bears are blue and bulls are red

Content Provider ACM Digital Library
Author Alvord, Linda Traberman, Tama
Abstract Analysts deal with real numerical data. Often the data is collected over time. Because numbers are abstract, analysts often use graphs to make numerical data more meaningful. Real data typically fluctuates in some random fashion. However, patterns often exist in the data. An example would be the seasonal fluctuations in commodities. Analysts use a variety of techniques to help them find patterns that have the potential to improve their skill in predicting future trends. This paper suggests some ways to make some of these patterns more obvious.We are here presenting techniques to enhance traditional methods of providing information in graphs. By taking advantage of current computer technology to provide graphic images and to draw graphs, new and exciting visual images are possible. In addition, animation strategies allow us to simulate the passage of time. Finally, we add a whimsical touch.By animating the graphs of mathematical functions we illuminate aspects of the graphing process which might otherwise go unnoticed. The focus of this paper is on curve fitting. APL and J provide the matrix divide function for finding the coefficients of the constant, linear, quadratic, cubic or nth degree polynomial which best fit a collection of data.Each of these polynomials also has a polynomial derivative. These derivatives are useful for three reasons. Select a point on the x-axis. The y-value of the polynomial is the height of the polynomial for that x-value. But the y-value of the derived polynomial for the chosen x-value provides information about the original polynomial in three ways. First the y-value for the chosen x-value is the slope of the original polynomial at that point. The second useful aspect of the derivative is that positive and negative values indicate when the original polynomial is increasing or decreasing. A derivative is one degree less than the original polynomial so for example, a cubic polynomial has a quadratic polynomial derivative.Applications will demonstrate how the slope of the tangent together with the use of images and color can enhance the reader's ability to assimilate data. These enhancements give additional meaning to graphs and add life to the data.
Starting Page 13
Ending Page 19
Page Count 7
File Format PDF
ISBN 1581131267
DOI 10.1145/312627.312686
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 1999-08-01
Publisher Place New York
Access Restriction Subscribed
Content Type Text
Resource Type Article
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