000			03003nam a22002177a 4500
005			20220616163859.0
008			220616b |||||||| |||| 00| 0 eng d
020			_a9789389342239 _c955.00
040			_aS.X.U.K
041			_aEnglish
082			_aR 519.535 TAB(USI)Ed7
100			_aTabachnick, Barbara G.
245			_aUsing multivariate statistics _cBarbara G. Tabachnick, Linda S. Fidell
250			_a7th ed.
260			_aNoida _bPearson _cc2020
300			_a832p _bP.B.
500			_aTable of Content "1. Introduction 2. A Guide to Statistical Techniques: Using the Book 3. Review of Univariate and Bivariate Statistics 4. Cleaning Up Your Act: Screening Data Prior to Analysis 5. Multiple Regression 6. Analysis of Covariance 7. Multivariate Analysis of Variance and Covariance 8. Profile Analysis: The Multivariate Approach to Repeated Measures 9. Discriminant Analysis 10. Logistic Regression 11. Survival/Failure Analysis 12. Canonical Correlation 13. Principal Components and Factor Analysis 14. Structural Equation Modeling by Jodie B. Ullman 15. Multilevel Linear Modeling 16. Multiway Frequency Analysis 17. Time-­Series Analysis 18. An Overview of the General Linear Model" Salient Features "New - All output is up to date, showing tables from IBM SPSS version 24 and SAS version 9.4. The output in the book matches the output of the user's program, so they know what to look for and how to use it. Updated - References in all chapters have been updated; for references prior to 2000, only classic citations are included. New - References and online facilities for sample size and power analysis are shown. Once considered mysterious and difficult, these analyses can now be done using online programs in many cases; the authors demonstrate where and how to address these facilities. New - Work on relative importance has been incorporated in multiple regression, canonical correlation, and logistic regression analysis, complete with demonstrations. This post hoc analysis takes effect size a step further by indicating relative importance for each significant variable as a percentage of the solution. Updated - Procedures for multiple imputation of missing data are updated, included and illustrated. This powerful method of estimating the values of missing data can be used even with repeated measures type data. It allows users to keep the data set intact, despite missing data points on several variables. New - The automated time-series example takes advantage of an IBM SPSS expert modeler that replaces previous tea-leaf reading aspects of the analysis. Hands-on guidelines for conducting numerous types of multivariate statistical analyses are provided. A practical approach focuses on the benefits and limitations of applications of a technique to a data set -when, why, and how to do it. " Includes index
650			_aAPPLIED MATHEMATICS _aMULTIVARIATE ANALYSIS _aSTATISTICS
700			_4auth. _aFidell, Linda S.
942			_cMBA REF
999			_c7027 _d7027