Preface

To the Student:

We have put together a packet of materials containing a few of the basic tools and techniques you will need to begin your work with scientific data. We suspect that some of these ideas will be challenging in the beginning, but as you continue taking your math courses at KAMSC, you will gradually encounter more of the mathematical concepts that underlay the proper techniques for handling experimental data.

A common question that students ask of their math instructors is, "Why do we need to learn this stuff anyway?" Well, part of the answer is in this packet. Having this packet in your hands should alert you to some of the things you will need to know as you progress through your courses. The techniques discussed here are very common throughout the scientific, engineering, and business communities. One of the most convincing demonstrations of this is the fact that most of today's graphing calculators (such as the very ones you are required to have here at KAMSC) have built-in routines that can do most of the data crunching that is discussed in this packet. These routines wouldn't be there if there weren't a strong demand for them. Likewise, many statistics and data handling programs for the computer can be purchased almost anywhere, and the prices are low enough to make them accessible to anyone who uses a computer. Most of you will eventually (if not already) have access to a spread sheet program such as Excel or Lotus. These programs contain many good tools for handling data. It is well worth your time to become familiar with them.

One of the biggest advantages of modern graphing calculators and computer programs is that they remove the burden of routine data handling, and allow you to concentrate on the reasons for which data are collected. However, they do not relieve you of the responsibility of understanding the data handling techniques and using them appropriately. The built-in routines allow you to cycle through data much more rapidly than was possible in the past. The happy result is that you are able to do a better job of analyzing your data, and can get a dynamic feel for the way the data behave as well.

Probably a good way to use this packet is to read it regularly as you make your way through your science and math courses. That way you will have some focus as you approach these topics in your studies. You may not have all of the math background initially, but you will be acquiring more of it with time. And as you study new ideas in math, you will not be as likely to forget them after you move on to new topics, because you will have seen how some of these ideas are used in science and other areas.

Dr. Michael Elzinga
Dr. John Goudie