SPSS 16.0 guide to data analysis

著者

書誌事項

SPSS 16.0 guide to data analysis

Marija J. Norušis

Prentice Hall, 2008

大学図書館所蔵 件 / 全3件

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注記

Includes bibliographical references (p. 643-644) and index

内容説明・目次

内容説明

The SPSS 16.0 Guide to Data Analysis is a friendly introduction to both data analysis and SPSS, the world's leading desktop statistical software package. Easy-to-understand explanations and in-depth content make this guide both an excellent supplement to other statistics texts and a superb primary text for any introductory data analysis course. With the SPSS 16.0 Guide to Data Analysis, you get a jump-start on describing data, testing hypotheses, and examining relationships using SPSS. Author Marija Norusis incorporates a wealth of data, including the General Social Survey and studies of Internet usage, opinions of the criminal justice system, marathon running times, library patronage, and the importance of manners. These data files are supplied with the book and are used throughout the examples and expanded chapter exercises. This unique combination of examples, exercises, and contemporary data gives you hands-on experience in analyzing data and makes learning about data analysis and statistical software relevant, unintimidating, and even fun! Data CD-ROM included.

目次

  • Contents at a glance: Part 1 Getting started with SPSS: Setting up a data file
  • using the online tutorial and Help system
  • evaluating strategies for acquiring data Part 2 Describing Data: Histograms, bar charts, stem and leaf plots, boxplots
  • scatterplots
  • tables and summary statistics Part 3 Testing Hypotheses: One sample t-test
  • paired and two sample t-tests
  • oneway and twoway analysis of variance
  • chi-square tests
  • nonparametric tests Part 4 Examining Relationships: Measures of association
  • linear regression and correlation
  • multiple regression
  • examining residuals and other diagnostics Appendices Include detailed instructions for producing charts with SPSS, and a thorough overview of the facilities for transforming and selecting data prior to analysis.

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