Taking the fear out of data analysis : a step-by-step approach
著者
書誌事項
Taking the fear out of data analysis : a step-by-step approach
Business Press, Thomson Learning, c2000
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注記
Includes bibliographical references and index
"First edition published 1997 by Harcourt Brace & Company Limited Reprinted by Thomson Learning 2000"--T.p. verso
内容説明・目次
内容説明
This text is aimed at students who may not like statistics but who need to understand the subject in order to complete their course or gain employment. It is a comprehensive but digestible introduction to statistics, guiding the reader through the number crunching of data analysis without memorising formulae. It is designed to help students understand the theory of data analysis. The text is divided into three parts - understanding data, preparing data for analysis and carrying out analysis - which link the stages of the data analysis process. Examples are used throughout and hints and warnings emphasise key points. This text is suitable for all students studying quantitative methods.
目次
Introduction. PART 1: UNDERSTANDING DATA. 1. What is Data (And Should You Lose Any Sleep Over It)? 2. Does Sampling Have A Purpose Other than Providing Employment for Statisticians? 3. Why Should You be Concerned about Measurement and are Some Measures Better than Others? PART 2: PREPARING DATA FOR ANALYSIS. 4. Have You Cleaned Your Data and Found the Mistakes You Made? 5. Do You Have Access to a Computer Package or Must You Employ A Computer Whiz-Kid? 6. Why Do You Need to Know Your Objective Before You Fail to Achieve It? 7. Why Not Take it Easy Initially and Describe Your Data? 8. Can You Use Few Numbers in Place of Many to Summarise Your Data? 9. What about Using Estimation to See What the Population Looks Like? PART 3: CARRYING OUT THE ANALYSIS. 10. How about Sitting Back and Hypothesising? 11. Simple Things First: One Variable, One Sample. 12. Getting Experienced: Making Comparisons. 13. Getting Adventurous: Searching for Relationships. PART 4: PRESENTING THE ANALYSIS. 14. A Look into A Multivariate Future. 15. Its All Over, or Is It? Appendices. Index.
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