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AGE 100: INTRODUCTION TO STATISTICS, MAP ANALYSIS AND CARTOGRAPHY
| Institution | KENYATTA UNIVERSITY |
| Course | BACHELOR OF EDUCATIO... |
| Year | 1st Year |
| Semester | Unknown |
| Posted By | stephen |
| File Type | |
| Pages | 108 Pages |
| File Size | 1.28 MB |
| Views | 2739 |
| Downloads | 0 |
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Description
This is an introductory Section to statistics, which provides the definition and types of statistics. It also gives the purpose of statistical analyses, especially the meaning of statistical significance in geography. It demonstrates that even proper use of statistics does not guarantee useful results. Other important aspects introduced in this Section are a number of new terms, concepts and symbols that are used quite often in statistics.
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Design & Analysis of Algorithm
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We study data structures so that we can learn to write more efficient programs. But
why must programs be efficient when new computers are faster every year? The
reason is that our ambitions grow with our capabilities. Instead of rendering efficiency needs obsolete, the modern revolution in computing power and storage capability merely raises the efficiency stakes as we computerize more complex tasks.
The quest for program efficiency need not and should not conflict with sound
design and clear coding. Creating efficient programs has little to do with “programming tricks” but rather is based on good organization of information and good algorithms. A programmer who has not mastered the basic principles of clear design
is not likely to write efficient programs. Conversely, “software engineering” cannot
be used as an excuse to justify inefficient performance. Generality in design can
and should be achieved without sacrificing performance, but this can only be done
if the designer understands how to measure performance and does so as an integral
part of the design and implementation process. Most computer science curricula
recognize that good programming skills begin with a strong emphasis on fundamental software engineering principles. Then, once a programmer has learned the
principles of clear program design and implementation, the next step is to study the
effects of data organization and algorithms on program efficiency
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