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Practical Multivariate Statistics is an essential textbook tailored for postgraduate and doctoral students across disciplines such as Pure Statistics, Agricultural Statistics, and Biological Social Sciences. It serves as a valuable resource for individuals preparing for competitive examinations like ISS, IES, SSS, A.S.R.B., State PSC, NET, and IAS. With over three decades of teaching experience, authors K.S. Kushwaha and Rajesh Kumar provide a comprehensive guide covering 12 meticulously organized chapters. The book delves deeply into multivariate analysis, regression analysis, multicollinearity, and stochastic processes, offering a perfect blend of theoretical insights and practical applications. Each chapter includes three types of questions designed to reinforce learning, ensuring students gain a solid understanding of complex multivariate techniques. Whether you are a student, educator, or professional in statistics, this hardback book will equip you with the necessary knowledge and skills to excel in your field and exams.
Key Features
| Features | Description |
|---|---|
| Target Audience | P.G. and Ph.D. students, faculty members, and competitive exam candidates. |
| Chapters | 12 chapters covering critical multivariate topics. |
| Teaching Experience | Compiled from 33 years of instructional expertise in various statistical disciplines. |
| Question Types | Includes three types of questions per chapter to facilitate understanding. |
| Core Topics | Covers Multivariate Analysis, Regression Analysis, Multicollinearity, and Stochastic Processes. |
| Attributes | Description |
|---|---|
| Authors | K.S. Kushwaha & Rajesh Kumar |
| Format | Hardback |
| Language | English |
| Total Chapters | 12 |
| Focus Areas | Statistics, Agricultural Statistics, Biological Sciences |
*Disclaimer: The above description has been AI-generated and has not been audited or verified for accuracy. It is recommended to verify product details independently before making any purchasing decisions.
The book is intended for postgraduate and doctoral students, faculty in statistics, and individuals preparing for competitive examinations.
The main topics include Multivariate Normal Distribution, Regression Analysis, Multicollinearity, and Stochastic Processes.
Each chapter features three types of questions that stimulate critical thinking and reinforce the material covered.
The authors have over 33 years of combined teaching experience in various disciplines related to statistics.
Yes, the structured format and comprehensive explanations make it ideal for self-study as well as classroom learning.
Brand: nipa
Country Of Origin: India
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 PSC, 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:
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:
Contents of the book Practical Multivariate Statistics
1. Multivariate Normal Distribution 2. Hotelling 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_x000D_
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