Geschäftskunden Kundenprogramme
Vor Ort
Merkzettel Warenkorb
Produktbild: Afifi, A: Practical Multivariate Analysis

Afifi, A: Practical Multivariate Analysis

Fr. 102.00

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

12.07.2011

Abbildungen

farbige, schwarzweisse Abbildungen

Verlag

Taylor & Francis

Seitenzahl

537

Maße (L/B/H)

24.1/16.5/3.5 cm

Gewicht

885 g

Auflage

5. Auflage

Sprache

Englisch

ISBN

978-1-4398-1680-6

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

12.07.2011

Abbildungen

farbige, schwarzweisse Abbildungen

Verlag

Taylor & Francis

Seitenzahl

537

Maße (L/B/H)

24.1/16.5/3.5 cm

Gewicht

885 g

Auflage

5. Auflage

Sprache

Englisch

ISBN

978-1-4398-1680-6

Kundinnen und Kunden meinen

0 Bewertungen

Informationen zu Bewertungen

Zur Abgabe einer Bewertung ist eine Anmeldung im Konto notwendig. Die Authentizität der Bewertungen wird von uns nicht überprüft. Wir behalten uns vor, Bewertungstexte, die unseren Richtlinien widersprechen, entsprechend zu kürzen oder zu löschen.

Die Bewertungen sind nach Format, Anzahl Sterne und Datum sortiert.

Verfassen Sie die erste Bewertung zu diesem Artikel

Helfen Sie anderen Kund*innen durch Ihre Meinung

Kundinnen und Kunden meinen

0 Bewertungen filtern
Die Leseprobe wird geladen.
  • Produktbild: Afifi, A: Practical Multivariate Analysis
  • PREPARATION FOR ANALYSIS What Is Multivariate Analysis? Defining multivariate analysis Examples of multivariate analyses Multivariate analyses discussed in this book Organization and content of the book Characterizing Data for Analysis Variables: their definition, classification, and use Defining statistical variables Stevens's classification of variables How variables are used in data analysis Examples of classifying variables Other characteristics of data Preparing for Data Analysis Processing data so they can be analyzed Choice of a statistical package Techniques for data entry Organizing the data Example: depression study Data Screening and Transformations Transformations, assessing normality and independence Common transformations Selecting appropriate transformations Assessing independence Selecting Appropriate Analyses Which analyses to perform? Why selection is often difficult Appropriate statistical measures Selecting appropriate multivariate analyses APPLIED REGRESSSION ANALYSIS Simple Regression and Correlation Chapter outline When are regression and correlation used? Data example Regression methods: fixed-X case Regression and correlation: variable-X case Interpretation: fixed-X case Interpretation: variable-X case Other available computer output Robustness and transformations for regression Other types of regression Special applications of regression Discussion of computer programs What to watch out for Multiple Regression and Correlation Chapter outline When are regression and correlation used? Data example Regression methods: fixed-X case Regression and correlation: variable-X case Interpretation: fixed-X case Interpretation: variable-X case Regression diagnostics and transformations Other options in computer programs Discussion of computer programs What to watch out for Variable Selection in Regression Chapter outline When are variable selection methods used? Data example Criteria for variable selection A general F test Stepwise regression Subset regression Discussion of computer programs Discussion of strategies What to watch out for Special Regression Topics Chapter outline Missing values in regression analysis Dummy variables Constraints on parameters Regression analysis with multicollinearity Ridge regression MULTIVARIATE ANALYSIS Canonical Correlation Analysis Chapter outline When is canonical correlation analysis used? Data example Basic concepts of canonical correlation Other topics in canonical correlation Discussion of computer program What to watch out for Discriminant Analysis Chapter outline When is discriminant analysis used? Data example Basic concepts of classification Theoretical background Interpretation Adjusting the dividing point How good is the discrimination? Testing variable contributions Variable selection Discussion of computer programs What to watch out for Logistic Regression Chapter outline When is logistic regression used? Data example Basic concepts of logistic regression Interpretation: Categorical variables Interpretation: Continuous variables Interpretation: Interactions Refining and evaluating logistic regression Nominal and ordinal logistic regression Applications of logistic regression Poisson regression Discussion of computer programs What to watch out for Regression Analysis with Survival Data Chapter outline When is survival analysis used? Data examples Survival functions Common survival distributions Comparing survival among groups The log-linear regression model The Cox regression model Comparing regression models Discussion of computer programs What to watch out for Principal Components Analysis Chapter outline When is principal components analysis used? Data example Basic concepts Interpretation Other uses Discussion of computer programs What to watch out for Factor Analysis Chapter outline When is factor analysis used? Data example Basic concepts Initial extraction: principal components Initial extraction: iterated components Factor rotations Assigning factor scores Application of factor analysis Discussion of computer programs What to watch out for Cluster Analysis Chapter outline When is cluster analysis used? Data example Basic concepts: initial analysis Analytical clustering techniques Cluster analysis for financial data set Discussion of computer programs What to watch out for Log-Linear Analysis Chapter outline When is log-linear analysis used? Data example Notation and sample considerations Tests and models for two-way tables Example of a two-way table Models for multiway tables Exploratory model building Assessing specific models Sample size issues The logit model Discussion of computer programs What to watch out for Correlated Outcomes Regression Chapter outline When is correlated outcomes regression used? Data example Basic concepts Regression of clustered data Regression of longitudinal data Other analyses of correlated outcomes Discussion of computer programs What to watch out for Appendix References Index A Summary and Problems appear at the end of each chapter.