Applied Multivariate Techniques

finally! Your Students Don’t Have To Be Mathematicians To Learn Multivariate Techniques! Multivariate Techniques Are Fundamental To Research Analysis—but Their Mathematical Derivations Are Often Overwhelming To Students. This Exciting New Book Takes A Gentle Approach That Introduces Students To The Various Multivariate Techniques Used In Business Without Intimidation: the Author Uses Geometry To Illustrate Concepts So Students Understand The Various Techniques And Their Application. Matrix Algebra And Proofs Can Also Be Used By Referring To The Appendices Of The Chapters. each Technique Is Discussed Analytically Using A Small Data Set And Hand Calculations. This Gives Students A Clearer Understanding Of The Technique. computer Outputs From Sas And Spss Are Annotated And Discussed. Students Learn How To Interpret The Results From These Two Popular Statistical Packages. the Chapter-ending Pedagogy Includes Exercises With Data Sets That Reinforce Concepts. instructor’s Manual: 0-471-13061-3 Also Available With This Text Is A Carefully Developed Teaching Tool For Instructors. It Offers Detailed Answers To All End Of Chapter Exercises, Including Computer Output For Questions Which Require Students To Perform Data Analysis. An Additional Set Of Examination Questions With Answers Are Also Provided In The Manual. booknews a Graduate Course Text For Students Of Business, And Natural And Behavioral Sciences Who Want To Learn How To Use The Multivariate Data Analytic Techniques Without Getting Bogged Down With Derivations Or Rigorous Proofs, But Would Like A Broader Understanding Than Is Provided By The Simple Cookbook Approaches. Focuses On When To The Use The Various Techniques And How To Interpret The Results, Rather Than The Mathematics. Indeed No Knowledge Of Matrix Algebra Is Assumed Or Required. An Enclosed 3.5 Disk Contains The Data Sets Used In The Text And Exercises. A Teacher's Manual Is Also Available. Annotation C. Book News, Inc., Portland, Or (booknews.com) This book focuses on when to use the various analytic techniques and how to interpret the resulting output from the most widely used statistical packages (e.g. , SAS, SPSS).