Keywords
practical multivariate statistics, p g students, ph d students, pure statistics, agricultural statistics, biological sciences, social sciences, competitive examinations, department of statistics, indian universities, multivariate analysis, multivariate regression analysis, multicollinearity, sampling distributions, stochastic processesPractical Multivariate Statistics
authored by: K.S.Kushwaha & Rajesh KumarLength: 152 mm | Breadth: 21.6 mm | Height: 229 mm | Imprint: NIPA | Weight: 750 GMS
The book "Practical Multivariate Statistics" has been designed for P.G. and Ph.D. students studying "Pure Statistics," "Agricultural Statistics," "Biological & Social Sciences," as well as individuals preparing for competitive examinations, such as ISS, IES, SSS, A.S.R.B., State's PSC's, NET, and IAS. Additionally, this book is beneficial for faculty members in the "Department of Statistics" at Indian universities. The book is the result of 33 years of teaching experience in U.G., P.G., and Ph.D. programs across various disciplines of Agriculture, Agril. Engg., and Agril Statistics.
The book is composed of 12 chapters, the first five of which focus on "Multivariate Analysis," while chapters six through nine cover "Multivariate Regression Analysis." Chapters ten and eleven address "Multicollinearity" and "Sampling Distributions of Partial & Multiple Correlation," respectively, and chapter twelve discusses "Elements of Stochastic Processes." Each chapter features three types of questions: "True/False," "Fill in the Blanks," and "Multiple Choice Questions" along with their corresponding "Key Answers."
K.S. Kushwaha: M.Sc., P.S.C.C., Ph.D. (Statistics), Recipient of Dr. Radhakrishanan Award (1992), Associate Professor (Statistics), Department of Mathematics & Statistics, Jawaharlal Nehru Krishi Vishwavidayalaya, Jabalpur, Madhya Pradesh, India,
Rajesh Kumar: M.Sc., P.S.C.C., Ph.D., (Statistics), Principal Scientist (Statistics), Indian Institute of Sugarcane Research (ICAR), Lucknow, Uttar Pradesh. India
1. Multivariate Normal Distribution 2. Hotelling’s T2 and D2 Statistics 3. Wishart Distribution 4. Discriminant Function 5. Principal Component Analysis, Factor Analysis and Canonical Analysis 6. Model Adequancy in Multivariate Regression Analysis 7. Multivariate Regression in Matrix Approach 8. Selecting Best Regression Equation 9. Examination of Residuals in Regression Analysis 10. Multicollinearity and Reasons for its Existence 11. Sampling Distribution of Partial and Multiple Correlation 12. Elements of Stochastic Processess