多元数据分析

出版时间:2011-6  出版社:机械工业出版社  作者:Joseph F. Hair,William C. Black,Barry J. Babin,Rolph E. Anderson  页数:800  
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内容概要

  《多元数据分析(英文版)(第7版)》是一本面向应用的经典多元数据分析教材,自1979年出版第1版至今,深受读者好评。《多元数据分析(英文版)(第7版)》循序渐进地介绍了各种多元统计分析方法,并通过丰富的实例演示了这些方法的应用。书中不仅涵盖多元数据分析的基本方法,而且还介绍了一些新方法,如结构方程建模和偏最小二乘法等。

作者简介

作者:(美国)海尔(Joseph F.Hair.Jr.) (美国)William C.Black (美国)Barry J.Babin 等海尔(Joseph F Hair,Jr.),于1971获得佛罗里达大学市场营销博士学位.现为肯尼索州立大学市场营销系教授。他出版了四十多本书,包括《Marketing》、《Marketing Essentials》等。他是美国市场营销协会、市场营销科学学会、西南市场营销协会和南方市场营销学会委员。2004年他被美国市场营销科学学会授予杰出教育奖,2007年被市场管理协会授予创新性市场营销人才。William C.Black,于1980年获得德州大学奥斯汀分校博士学位,现为路易斯安那州立大学工商管理学院市场营销系教授。他的研究兴趣包括多元统计、应用信息技术,以及与电子商务相关的市场原理的进展。他是《Journal of BusinessResearch》编审委员会成员。Barry J.Babin于1991年获得路易斯安那州立大学工商管理学博士学位,现为路易斯安那理工大学市场营销与定量分析学教授、商学院Max P.Watson教授。他主要研究零售的各个方面和服务管理。他还曾被美国市场营销科学研究院和市场营销学会评为杰出研究员。Rolph E.Anderson,拥有佛罗里达大学博士学位,现为Drexei大学工商管理学院R0yal H.Gibson Sr教授。他曾两次获得Drexel大学优秀教师奖,并获得过《Journal of Personal Selling&Sales Management》杰出评论奖、Drexel大学商学院科研成就奖等。

书籍目录

preface iii
about the authors v
 chapter 1 introduction: methods and model building
  what is multivariate analysis?
  multivariate analysis in statistical terms
  some basic concepts of multivariate analysis
  the variate
  measurement scales
  measurement error and multivariate measurement
  statistical significance versus statistical power
  types of statistical error and statistical power
  impacts on statistical power
  using power with multivariate techniques
  a classification of multivariate techniques
  dependence techniques
  interdependence techniques
  types of multivariate techniques
  principal components and common factor analysis
  multiple regression
  multiple discriminant analysis and logistic regression
  canonical correlation
  multivariate analysis of variance and covariance
  conjoint analysis
  cluster analysis
  perceptual mapping
  correspondence analysis
  structural equation modeling and confirmatory factor
analysis
  guidelines for multivariate analyses and interpretation
  establish practical significance as well as statistical
  significance
  recognize that sample size affects all results
  know your data
  strive for model parsimony
  look at your errors
  validate your results
  a structured approach to multivariate model building
  stage 1: define the research problem, objectives,
  and multivariate technique to be used
  stage 2: develop the analysis plan
  stage 3: evaluate the assumptions underlying the multivariate
technique
  stage 4: estimate the multivariate model and assess overall model
fit
  stage 5: interpret the variate(s)
  stage 6: validate the multivariate model
  a decision flowchart
  databases
  primary database
  other databases
  organization of the remaining chapters
section i: understanding and preparing for multivariate
analysis
section ii: analysis using dependence techniques
section iii: interdependence techniques
section iv: structural equations modeling
  summary 28 . questions 30 . suggested readings
  references
section i understanding and preparing for multivariate
analysis
 chapter 2 cleaning and transforming data
  introduction
  graphical examination of the data
  univariate profiling: examining the shape of the
distribution
  bivariate profiling: examining the relationship between
variables
  bivariate profiling: examining group differences
  multivariate profiles
  missing data
  the impact of missing data
  a simple example of a missing data analysis
  a four-step process for identifying missing data and applying
remedies
  an illustration of missing data diagnosis with the four-step
process
  outliers
  detecting and handling outliers
  an illustrative example of analyzing outliers
  testing the assumptions of multivariate analysis
  assessing individual variables versus the variate
  four important statistical assumptions
  data transformations
  an illustration of testing the assumptions underlying
multivariate analysis
  incorporating nonmetric data with dummy variables
  summary 88 . questions 89 . suggested readings
  references
 chapter 3 factor analysis
  what is factor analysis?
  a hypothetical example of factor analysis
  factor analysis decision process
  stage 1: objectives of factor analysis
  specifying the unit of analysis
  achieving data summarization versus data reduction
  variable selection
  using factor analysis with other multivariate techniques
  stage 2: designing a factor analysis
  correlations among variables or respondents
  variable selection and measurement issues
  sample size
  summary
  stage 3: assumptions in factor analysis
  conceptual issues
  statistical issues
  summary
  stage 4: deriving factors and assessing overall fit
  selecting the factor extraction method
  criteria for the number of factors to extract
  stage 5: interpreting the factors
  the three processes of factor interpretation
  rotation of factors
  judging the significance of factor loadings
  interpreting a factor matrix
  stage 6: validation of factor analysis
  use of a confirmatory perspective
  assessing factor structure stability
  detecting influential observations
  stage 7: additional uses of factor analysis results
  selecting surrogate variables for subsequent analysis
  creating summated scales
  computing factor scores
  selecting among the three methods
  an illustrative example
  stage 1: objectives of factor analysis
  stage 2: designing a factor analysis
  stage 3: assumptions in factor analysis
  component factor analysis: stages 4 through 7
  common factor analysis: stages 4 and 5
  a managerial overview of the results
  summary 148 . questions 150 . suggested readings
  references
section ii analysis using dependence techniques
 chapter 4 simple and multiple regression
  what is multiple regression analysis?
  an example of simple and multiple regression
  prediction using a single independent variable:
  simple regression
  prediction using several independent variables:
  multiple regression
  summary
  a decision process for multiple regression analysis
  stage 1: objectives of multiple regression
  research problems appropriate for multiple regression
  specifying a statistical relationship
  selection of dependent and independent variables
  stage 2: research design of a multiple regression analysis
  sample size
  creating additional variables
  stage 3: assumptions in multiple regression analysis
  assessing individual variables versus the variate
  methods of diagnosis
  linearity of the phenomenon
  constant variance of the error term
  independence of the error terms
  normality of the error term distribution
  summary
  stage 4: estimating the regression model and assessing overall
model fit
  selecting an estimation technique
  testing the regression variate for meeting the regression
assumptions
  examining the statistical significance of our model
  identifying influential observations
  stage 5: interpreting the regression variate
  using the regression coefficients
  assessing multicollinearity
  stage 6: validation of the results
  additional or split samples
  calculating the press statistic
  comparing regression models
  forecasting with the model
  illustration of a regression analysis
  stage 1: objectives of multiple regression
  stage 2: research design of a multiple regression analysis
  stage 3: assumptions in multiple regression analysis
  stage 4: estimating the regression model and assessing overall
model fit
  stage 5: interpreting the regression variate
  stage 6: validating the results
  evaluating alternative regression models
  a managerial overview of the results
  summary 231 . questions 234 . suggested readings
  references
 chapter 5 canonical correlation
  what is canonical correlation?
  hypothetical example of canonical correlation
  developing a variate of dependent variables
  estimating the first canonical function
  estimating a second canonical function
  relationships of canonical correlation analysis to other
multivariate techniques
  stage 1: objectives of canonical correlation analysis
  selection of variable sets
  evaluating research objectives
  stage 2: designing a canonical correlation analysis
  sample size
  variables and their conceptual linkage
  missing data and outliers
  stage 3: assumptions in canonical correlation
  linearity
  normality
  homoscedasticity and multicollinearity
  stage 4: deriving the canonical functions and assessing overall
fit
  deriving canonical functions
  which canonical functions should be interpreted?
  stage 5: interpreting the canonical variate
  canonical weights
  canonical loadings
  canonical cross-loadings
  which interpretation approach to use
  stage 6: validation and diagnosis
  an illustrative example
  stage 1: objectives of canonical correlation analysis
  stages 2 and 3: designing a canonical correlation analysis and
testing the assumptions
  stage 4: deriving the canonical functions and assessing overall
fit
  stage 5: interpreting the canonical variates
  stage 6: validation and diagnosis
  a managerial overview of the results
  summary 258 . questions 259 . references
 chapter 6 conjoint analysis
  what is conjoint analysis?
  hypothetical example of conjoint analysis
  specifying utility, factors, levels, and profiles
  gathering preferences from respondents
  estimating part-worths
  determining attribute importance
  assessing predictive accuracy
  the managerial uses of conjoint analysis
  comparing conjoint analysis with other multivariate methods
  compositional versus decompositional techniques
  specifying the conjoint variate
  separate models for each individual
  flexibility in types of relationships
  designing a conjoint analysis experiment
  stage 1: the objectives of conjoint analysis
  defining the total utility of the object
  specifying the determinant factors
  stage 2: the design of a conjoint analysis
  selecting a conjoint analysis methodology
  designing profiles: selecting and defining factors and
levels
  specifying the basic model form
  data collection
  stage 3: assumptions of conjoint analysis
  stage 4: estimating the conjoint model and assessing overall
fit
  selecting an estimation technique
  estimated part-worths
  evaluating model goodness-of-fit
  stage 5: interpreting the results
  examining the estimated part-worths
  assessing the relative importance of attributes
  stage 6: validation of the conjoint results
  managerial applications of conjoint analysis
  segmentation
  profitability analysis
  conjoint simulators
  alternative conjoint methodologies
  adaptive/self-explicated conjoint: conjoint with
  a large number of factors
  choice-based conjoint: adding another touch of realism
  overview of the three conjoint methodologies
  an illustration of conjoint analysis
  stage 1: objectives of the conjoint analysis
  stage 2: design of the conjoint analysis
  stage 3: assumptions in conjoint analysis
  stage 4: estimating the conjoint model and assessing overall
model fit
  stage 5: interpreting the results
  stage 6: validation of the results
  a managerial application: use of a choice simulator
  summary 327 . questions 330 . suggested readings
  references
 chapter 7 multiple discriminant analysis and logistic
regression
  what are discriminant analysis and logistic regression?
  discriminant analysis
  logistic regression
  analogy with regression and manova
  hypothetical example of discriminant analysis
  a two-group discriminant analysis: purchasers versus
nonpurchasers
  a geometric representation of the two-group discriminant
function
  a three-group example of discriminant analysis: switching
intentions
  the decision process for discriminant analysis
  stage 1: objectives of discriminant analysis
  stage 2: research design for discriminant analysis
  selecting dependent and independent variables
  sample size
  division of the sample
  stage 3: assumptions of discriminant analysis
  impacts on estimation and classification
  impacts on interpretation
  stage 4: estimation of the discriminant model and assessing
overall fit
  selecting an estimation method
  statistical significance
  assessing overall model fit
  casewise diagnostics
  stage 5: interpretation of the results
  discriminant weights
  discriminant loadings
  partial f values
  interpretation of two or more functions
  which interpretive method to use?
  stage 6: validation of the results
  validation procedures
  profiling group differences
  a two-group illustrative example
  stage 1: objectives of discriminant analysis
  stage 2: research design for discriminant analysis
  stage 3: assumptions of discriminant analysis
  stage 4: estimation of the discriminant model and assessing
overall fit
  stage 5: interpretation of the results
  stage 6: validation of the results
  a managerial overview
  a three-group illustrative example
  stage 1: objectives of discriminant analysis
  stage 2: research design for discriminant analysis
  stage 3: assumptions of discriminant analysis
  stage 4: estimation of the discriminant model and assessing
overall fit
  stage 5: interpretation of three-group discriminant analysis
results
  stage 6: validation of the discriminant results
  a managerial overview
  logistic regression: regression with a binary dependent
variable
  representation of the binary dependent variable
  sample size
  estimating the logistic regression model
  assessing the goodness-of-fit of the estimation model
  testing for significance of the coefficients
  interpreting the coefficients
  calculating probabilities for a specific value of the independent
variable
  overview of interpreting coefficients
  summary
  an illustrative example of logistic regression
  stages 1, 2, and 3: research objectives, research design, and
statistical assumptions
  stage 4: estimation of the logistic regression model and
assessing overall fit
  stage 5: interpretation of the results
  stage 6: validation of the results
  a managerial overview
  summary 434 . questions 437 . suggested readings
  references
 chapter 8 anova and manova
  manova: extending univariate methods for assessing group
differences
  multivariate procedures for assessing group differences
  a hypothetical illustration of manova
  analysis design
  differences from discriminant analysis
  forming the variate and assessing differences
  a decision process for manova
  stage 1: objectives of manova
  when should we use manova?
  types of multivariate questions suitable for manova
  selecting the dependent measures
  stage 2: issues in the research design of manova
  sample size requirements—overall and by group
  factorial designs—two or more treatments
  using covariates—ancova and mancova
  manova counterparts of other anova designs
  a special case of manova: repeated measures
  stage 3: assumptions of anova and manova
  independence
  equality of variance–covariance matrices
  normality
  linearity and multicollinearity among the dependent
variables
  sensitivity to outliers
  stage 4: estimation of the manova model and assessing overall
fit
  estimation with the general linear model
  criteria for significance testing
  statistical power of the multivariate tests
  stage 5: interpretation of the manova results
  evaluating covariates
  assessing effects on the dependent variate
  identifying differences between individual groups
  assessing significance for individual dependent variables
  stage 6: validation of the results
  summary
  illustration of a manova analysis
  example 1: difference between two independent groups
  stage 1: objectives of the analysis
  stage 2: research design of the manova
  stage 3: assumptions in manova
  stage 4: estimation of the manova model and assessing the overall
fit
  stage 5: interpretation of the results
  example 2: difference between k independent groups
  stage 1: objectives of the manova
  stage 2: research design of manova
  stage 3: assumptions in manova
  stage 4: estimation of the manova model and assessing overall
fit
  stage 5: interpretation of the results
  example 3: a factorial design for manova with two independent
variables
  stage 1: objectives of the manova
  stage 2: research design of the manova
  stage 3: assumptions in manova
  stage 4: estimation of the manova model and assessing overall
fit
  stage 5: interpretation of the results
  summary
  a managerial overview of the results
  summary 498 . questions 500 . suggested readings
  references
section iii analysis using interdependence techniques
 chapter 9 grouping data with cluster analysis
  what is cluster analysis?
  cluster analysis as a multivariate technique
  conceptual development with cluster analysis
  necessity of conceptual support in cluster analysis
  how does cluster analysis work?
  a simple example
  objective versus subjective considerations
  cluster analysis decision process
  stage 1: objectives of cluster analysis
  stage 2: research design in cluster analysis
  stage 3: assumptions in cluster analysis
  stage 4: deriving clusters and assessing overall fit
  stage 5: interpretation of the clusters
  stage 6: validation and profiling of the clusters
  an illustrative example
  stage 1: objectives of the cluster analysis
  stage 2: research design of the cluster analysis
  stage 3: assumptions in cluster analysis
  employing hierarchical and nonhierarchical methods
  step 1: hierarchical cluster analysis (stage 4)
  step 2: nonhierarchical cluster analysis (stages 4, 5, and
6)
  summary 561 . questions 563 . suggested readings
  references
 chapter 10 mds and correspondence analysis
  what is multidimensional scaling?
  comparing objects
  dimensions: the basis for comparison
  a simplified look at how mds works
  gathering similarity judgments
  creating a perceptual map
  interpreting the axes
  comparing mds to other interdependence techniques
  individual as the unit of analysis
  lack of a variate
  a decision framework for perceptual mapping
  stage 1: objectives of mds
  key decisions in setting objectives
  stage 2: research design of mds
  selection of either a decompositional (attribute-free)
  or compositional (attribute-based) approach
  objects: their number and selection
  nonmetric versus metric methods
  collection of similarity or preference data
  stage 3: assumptions of mds analysis
  stage 4: deriving the mds solution and assessing overall
fit
  determining an object’s position in the perceptual map
  selecting the dimensionality of the perceptual map
  incorporating preferences into mds
  stage 5: interpreting the mds results
  identifying the dimensions
  stage 6: validating the mds results
  issues in validation
  approaches to validation
  overview of multidimensional scaling
  correspondence analysis
  distinguishing characteristics
  differences from other multivariate techniques
  a simple example of ca
  a decision framework for correspondence analysis
  stage 1: objectives of ca
  stage 2: research design of ca
  stage 3: assumptions in ca
  stage 4: deriving ca results and assessing overall fit
  stage 5: interpretation of the results
  stage 6: validation of the results
  overview of correspondence analysis
  illustrations of mds and correspondence analysis
  stage 1: objectives of perceptual mapping
  identifying objects for inclusion
  basing the analysis on similarity or preference data
  using a disaggregate or aggregate analysis
  stage 2: research design of the perceptual mapping study
  selecting decompositional or compositional methods
  selecting firms for analysis
  nonmetric versus metric methods
  collecting data for mds
  collecting data for correspondence analysis
  stage 3: assumptions in perceptual mapping
  multidimensional scaling: stages 4 and 5
  stage 4: deriving mds results and assessing overall fit
  stage 5: interpretation of the results
  overview of the decompositional results
  correspondence analysis: stages 4 and 5
  stage 4: estimating a correspondence analysis
  stage 5: interpreting ca results
  overview of ca
  stage 6: validation of the results
  a managerial overview of mds results
  summary 623 . questions 625 . suggested readings
  references
section iv structural equations modeling
 chapter 11 sem: an introduction
  what is structural equation modeling?
  estimation of multiple interrelated dependence
relationships
  incorporating latent variables not measured directly
  defining a model
  sem and other multivariate techniques
  similarity to dependence techniques
  similarity to interdependence techniques
  the emergence of sem
  the role of theory in structural equation modeling
  specifying relationships
  establishing causation
  developing a modeling strategy
  a simple example of sem
  the research question
  setting up the structural equation model for path analysis
  the basics of sem estimation and assessment
  six stages in structural equation modeling
  stage 1: defining individual constructs
  operationalizing the construct
  pretesting
  stage 2: developing and specifying the measurement model
  sem notation
  creating the measurement model
  stage 3: designing a study to produce empirical results
  issues in research design
  issues in model estimation
  stage 4: assessing measurement model validity
  the basics of goodness-of-fit
  absolute fit indices
  incremental fit indices
  parsimony fit indices
  problems associated with using fit indices
  unacceptable model specification to achieve fit
  guidelines for establishing acceptable and unacceptable fit
  stage 5: specifying the structural model
  stage 6: assessing the structural model validity
  structural model gof
  competitive fit
  comparison to the measurement model
  testing structural relationships
  summary 678 . questions 680 . suggested readings
  appendix 11a: estimating relationships using path analysis
  appendix 11b: sem abbreviations
  appendix 11c: detail on selected gof indices
  references
 chapter 12 applications of sem
  part 1: confirmatory factor analysis
  cfa and exploratory factor analysis
  a simple example of cfa and sem
  a visual diagram
  sem stages for testing measurement theory validation with
cfa
  stage 1: defining individual constructs
  stage 2: developing the overall measurement model
  unidimensionality
  congeneric measurement model
  items per construct
  reflective versus formative constructs
  stage 3: designing a study to produce empirical results
  measurement scales in cfa
  sem and sampling
  specifying the model
  issues in identification
  avoiding identification problems
  problems in estimation
  stage 4: assessing measurement model validity
  assessing fit
  path estimates
  construct validity
  model diagnostics
  summary example
  cfa illustration
  stage 1: defining individual constructs
  stage 2: developing the overall measurement model
  stage 3: designing a study to produce empirical results
  stage 4: assessing measurement model validity
  hbat cfa summary
  part 2: what is a structural model?
  a simple example of a structural model
  an overview of theory testing with sem
  stages in testing structural theory
  one-step versus two-step approaches
  stage 5: specifying the structural model
  unit of analysis
  model specification using a path diagram
  designing the study
  stage 6: assessing the structural model validity
  understanding structural model fit from cfa fit
  examine the model diagnostics
  sem illustration
  stage 5: specifying the structural model
  stage 6: assessing the structural model validity
  part 3: extensions and applications of sem
  reflective versus formative measures
  reflective versus formative measurement theory
  operationalizing a formative construct
  distinguishing reflective from formative constructs
  which to use—reflective or formative?
  higher-order factor analysis
  empirical concerns
  theoretical concerns
  using second-order measurement theories
  when to use higher-order factor analysis
  multiple groups analysis
  measurement model comparisons
  structural model comparisons
  measurement bias
  model specification
  model interpretation
  relationship types: mediation and moderation
  mediation
  moderation
  longitudinal data
  additional covariance sources: timing
  using error covariances to represent added covariance
  partial least squares
  characteristics of pls
  advantages and disadvantages of pls
  choosing pls versus sem
  summary 778 . questions 781 . suggested readings
references
index

章节摘录

版权页:插图:Missing data are termed missing at random (MAR) if the missing values of Y depend on X,  but not on Y In other words, the observed Y values represent a random sample of the actual Y  values for each value of X, but the observed data for Ydo not necessarily represent a truly ran-  dom sample of all Y values. Even though the missing data process is random in the sample, its  values are not generalizable to the population. Most often, the data are missing randomly  within subgroups, but differ in levels between subgroups. The researcher must determine the  factors determining the subgroups and the varying levels between groups.For example, assume that we know the gender of respondents (the X variable) and are  asking about household income (the Y variable). We find that the missing data are random for  both males and females but occur at a much higher frequency for males than females. Even  though the missing data process is operating in a random manner within the gender variable,  any remedy applied to the missing data will still reflect the missing data process because gen-  der affects the ultimate distribution of the household income values.A higher level of randomness is termed missing completely at random (MCAR). In these  instances the observed values of Y are truly a random sample of all Y values, with no underly-  ing process that lends bias to the observed data. In simple terms, the cases with missing data  are indistinguishable from cases with complete data.From our earlier example, this situation would be shown by the fact that the missing  data for household income were randomly missing in equal proportions for both males and  females. In this missing data process, any of the remedies can be applied without making  allowances for the impact of any other variable or missing data process.Diagnostic Tests for Levels of Randomness. As previously noted, the researcher must ascer-tain whether the missing data process occurs in a completely random manner. When the data set issmall, the researcher may be able to visually see such patterns or perform a set of simple calculations(such as in our simple example at the beginning of the chapter). However, as sample size and the num-ber of variables increases, so does the need for empirical diagnostic tests. Some statistical programsadd techniques specifically designed for missing data analysis (e.g., Missing Value Analysis in SPSS),which generally include one or both diagnostic tests.The first diagnostic assesses the missing data process of a single variable Y by forming twogroups: observations with missing data for Y and those with valid values of Y. Statistical testsare then performed to determine whether significant differences exist between the two groupson other variables of interest. Significant differences indicate the possibility of a nonrandommissing data process.Let us use our earlier example of household income and gender. We would first form twogroups of respondents, those with missing data on the household income question and thosewho answered the question. We would then compare the percentages of gender for each group.If one gender (e.g., males) was found in greater proportion in the missing data group, we wouldsuspect a nonrandom missing data process. If the variable being compared is metric (e.g., anattitude or perception) instead of categorical (gender), then t-tests are performed to determinethe statistical significance of the difference in the variable's mean between the two groups. Theresearcher should examine a number of variables to see whether any consistent patternemerges. Remember that some differences will occur by chance, but either a large number or asystematic pattern of differences may indicate an underlying nonrandom pattern.A second approach is an overall test of randomness that determines whether the missing datacan be classified as MCAR.

编辑推荐

《多元数据分析(英文版)(第7版)》特色:以循序渐进方式(流水线方式)组织内容:在内容组织上,各章集中概述一个论题,每章均从基础开始并讨论应用。后面各章逐步深入。扩展各种方法应用:对"经验法则"给出解释,包括像样本容量这类重要问题。重新组织结构方程建模这一重要内容,包括结构方程建模概述、验证-性因素分析、估计和检验结构模型的相关问题,以及验证性因素分析和结构方程建模的一些高级主题,如检验更高阶因子模型、群组模型、调节变量与中间变量。

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用户评论 (总计11条)

 
 

  •   这本是应用类多元里一本写得比较好的,很细致,结构我比较喜欢,但是做教材不行,毕竟是英文版。我觉得适合研究生和专业人士看,本科英语好的也可尝试。
  •   书本身质量很好,摸起来也很舒服。
  •   书很好,内容也给力!
  •   蛮好的书哦 原版的原汁原味些 希望能读懂啊
  •   作为一名市场营销的研究生,能够灵活地分析数据,得出决策判断的依据,很有必要学习一下!
  •   适合初学者自学,前提是英语基础不错,或愿意尝试
  •   这本书是国际版,章节安排上与美国本土版本有所不同,但内容基本一致。封面用的是本土版的封面,还是显得出版社不太诚实。这本书基本没有公式,实际上是想用文字语言把多元统计的原理说清楚。网上有配套材料,包括SPSS格式的数据和各章的SPSS SYNTAX文件。如果你的英语理解能力足够好,并且有SPSS和基础统计学的基础,这本书能用作自学,像有些网友称之为多元数据分析的“圣经”也不过分。但是,如果你缺乏这些基础,自学就比较难了。其实这样的书真适合有国外教育背景的统计学家或社会科学家来翻译。希望出版社能在这方面做些努力。
  •   挺好的一本书,就是全英文,理解有点难度,头脑一热买的
  •   多元数据分析工人的bible
  •   Nice book and very useful.
  •   《多元数据分析(英文版.第7版)》是一本面向应用的经典多元数据分析教材,自1979年出版第1版至今,深受读者好评。《多元数据分析(英文版.第7版)》循序渐进地介绍了各种多元统计分析方法,并通过丰富的实例演示了这些方法的应用。书中不仅涵盖多元数据分析的基本方法,而且还介绍了一些新方法,如结构方程建模和偏最小二乘法等。... 阅读更多
 

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