Deepa Tyagi has been working as an Assistant Professor, Department of Statistics, Shaheed Rajguru College of Applied Sciences for Women, Delhi University, East Delhi. The area of research interest is Regression Analysis, Time Series Analysis and Econometrics. She is a strong research professional with a Doctorate (Ph.D.) in Statistics and a Master of Philosophy (M.Phil.) in Mathematical Science from Banasthali University, Rajasthan, India. She has done her Graduation (B.Sc.) & Post Graduation (M.Sc.) from Chaudhary Charan Singh University, Meerut, Uttar Pradesh. Dr Tyagi had experience as a Research Associate in ICAR- Indian Agricultural Statistics Research Institute (IASRI), Sample Survey Division, PUSA, New Delhi, and Junior Research Fellow (JRF) under the Department of Science and Technology (DST) project in the Centre for Mathematical Sciences (CMS), Banasthali University, Rajasthan. She has published numerous research articles in her field. She has expertise in many software tools based on Statistical Data analysis & Mathematical tools, such as EViews, SPSS, R, MATLAB, Mathematica, LaTeX, and Python.
Shalini Chandra has been working as a Professor & HOD in the Department of Mathematics and Statistics, Banasthali University, for over 20 years, including one year teaching at the Indian Statistical Institute, North East Centre, since 2002. She has completed her undergraduate B.Sc. and post graduate M.Sc. degrees from Lucknow University, Uttar Pradesh. She pursued her Doctorate (Ph.D.) from M.D. University, Rohtak, India. Prof Chandra also has experience as a Visiting Scientist Associate Professor, Indian Statistical Institute, North East Centre, Tezpur. The area of research interest is Regression Analysis, Time Series Analysis, Biostatistics and Econometrics. She has a strong research collaboration with National and International Academics like NIT Durgapur, ISI Kolkata, IIT Delhi, and Concordia University, Canada. She has published many articles and book chapters related to the area of interest. She has good expertise in software skills, which is based on Statistical Data analysis & Mathematical tools like SPSS, MATLAB, EViews, C/C++, R-software, LaTeX and Python.
Shrawan Kumar has been working as a Professor in the Department of Statistics, Kirori Mal College, University of Delhi, for over 30 years, since 1994. Prof Kumar has completed his undergraduate degree, B.A. (Hons) in Mathematical Statistics, and a postgraduate degree, M.A. in Statistics from Hindu College, Delhi University, Delhi. He has done his Doctorate (Ph.D.) & M.Phil. in Statistics from the Department of Statistics, University of Delhi, India. The area of research interest is Sampling Distributions, Statistical Inference, Reliability and Biostatistics. He has published many research articles related to the area of interest. He has good expertise in software skills, which is based on Statistical Data analysis tools like Microsoft Office, SPSS, C/C++ Language and R-software.
This book introduces statistical data analysis using R programming, covering tools like deillegalscriptive statistics, regression, ANOVA, and non-parametric tests. It covers essential statistical tools, including deillegalscriptive statistics, probability distributions, and hypothesis testing, with practical examples and solved exercises. It introduces both built-in library packages and manual coding solutions, offering flexibility and clarity for learners. Featuring numerous tables, diagrams, and hands-on programming exercises, this book ensures ease of understanding and practical mastery of R for statistical analysis.
- Comprehensive coverage of statistical tools, including deillegalscriptive statistics, regression, ANOVA, and non-parametric tests.
- Includes dual programming approach, in-built library packages and manual coding solutions.
- Focus on graphics and data visualisation for effective interpretation of results.
- Practical R code examples and solved exercises for hands-on learning.
This book is for undergraduate and postgraduate students, researchers, and professionals in fields such as statistics, computer science, business analytics, public health, psychology, economics, and environmental science.
1. Introduction and Preliminaries. 2. Deillegalscriptive Statistics and Graphics. 3. Probability Distributions. 4. One-Sample and Two-Sample Tests. 5. Regression and Correlation. 6. Analysis of Variance, Analysis of Covariance, and the Kruskal-Wallis Test. 7. Multiple Linear Regression. 8. Logistic Regression and Generalized Linear Models. 9. Stepwise Regression Analysis.
