SPSS 15.0 statistical procedures companion

著者

書誌事項

SPSS 15.0 statistical procedures companion

Marija J. Norušis

Prentice Hall, c2006

大学図書館所蔵 件 / 全3件

この図書・雑誌をさがす

注記

Includes bibliographical references (p. 607-611) and index

System requirements of accompanying CD-ROM: Windows 98, Me, XP or Windows 2000

Accompanying CD-ROM contains sample data files

内容説明・目次

内容説明

You have all of the essential components: software with enough options to make a whirling dervish take pause; a data set of questionable virtue cast in a starring role; and a plot waiting to unfold. Directing this production to a satisfying conclusion isn't easy. The goal of the SPSS 15.0 Statistical Procedures Companion is to point you in the right direction. See the SPSS 15.0 Advanced Statistical Procedures Companion for statistical introductions to some of the more advanced procedures in SPSS, including loglinear and logit analysis for categorical data, multinomial, two-stage and weighted least-squares regression, Generalized Estimating Equations, Generalized Linear Model, Kaplan-Meier, actuarial and Cox models for analysis of time-to-event data, variance components analysis, and ALSCAL. For additional information, go to This site offers a detailed Table of Contents, features, examples included in the book, and a sample chapter for download.

目次

  • Contents at a glance: Preparing data for analysis: Introduction to SPSS
  • the data file
  • defining the data
  • creating new variables
  • transforming existing variables
  • checking data definitions
  • cleaning data Describing data: Tables
  • graphs
  • OLAP cubes
  • measures of central tendency and dispersion
  • standard scores
  • the normal distribution
  • measures of association Testing simple hypotheses: Basics of hypothesis testing
  • t-tests
  • oneway analysis of variance
  • multiple comparisons
  • nonparametric tests
  • chi square tests
  • correlation
  • partial correlation Building models: Bivariate and multiple linear regression
  • loglinear models
  • discriminant analysis
  • binary logistic regression
  • ordinal regression, factor analysis
  • cluster analysis Using the General Linear Model: Univariate models
  • multivariate models
  • repeated measures Analyzing scales: Reliability analysis

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