Mathematical statistics + Data processing
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Mathematical statistics + Data processing
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Mathematical statistics + Data processing
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Incoming Resources
- Interactive data analysis, a practical primer, Donald R. McNeil
- Introduction to the use of computer packages for statistical analyses, Richard W. Moore
- Essentials of MATLAB programming, Stephen J. Chapman
- Using R and RStudio for data management, statistical analysis, and graphics, Nicholas J. Horton, Department of Mathematics and Statistics, Amherst College, Massachusetts, U.S.A., Ken Kleinman, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts, U.S.A
- Guide to intelligent data analysis, how to intelligently make sense of real data, Michael R. Berthold ... [and three others]
- Computer intensive methods in statistics, Silvelyn Zwanzig, Behrang Mahjani
- The Stata survival manual, Pevalin and Robson
- Smoothing techniques, with implementation in S, Wolfgang Härdle
- Learning data mining with R, develop key skills and techniques with R to create and customize data mining algorithms, Bater Makhabel
- Computational statistics, Geof H. Givens, Jennifer A. Hoeting
- Data analysis using SAS Enterprise guide, Lawrence S. Meyers, Glenn Gamst, A.J. Guarino
- Intermediate statistics using SPSS, Herschel Knapp
- Statistics with Stata, updated for version 9, Lawrence C. Hamilton
- Elements of statistical computing, Ronald A. Thisted
- Modern applied statistics with S, W.N. Venables, B.D. Ripley
- Mathematical statistics with resampling and R, Laura M. Chihara ; Tim C. Hesterberg
- The Mata book, a book for serious programmers and those who want to be, William W. Gould
- The data science design manual, Steven S. Skiena
- Data analysis using SAS, C.Y. Joanne Peng
- A step-by-step guide to exploratory factor analysis with Stata, Marley W. Watkins
- An introduction to Stata for health researchers, Svend Juul
- A handbook of statistical analyses using Stata, Sophia Rabe-Hesketh, Brian S. Everitt
- A handbook of statistical analyses using Stata, Sophia Rabe-Hesketh, Brian Everitt
- Practical data science cookbook, 89 hands-on recipes to help you complete real-world data science projects in R and Python, Tony Ojeda ... [and others]
- SAS for mixed models, Ramon C. Littell ... [and others]
- SAS/GRAPH software, introduction, version 6
- Applied statistics using SPSS, STATISTICA, MATLAB and R, Joaquim P. Marques de Sá
- Statistics using R, an integrative approach, Sharon Lawner Weinberg, Daphna Harel, Sarah Knapp Abramowitz
- Using statistics in the social and health sciences with SPSS and Excel, Martin Lee Abbott
- R cookbook, proven recipes for data analysis, statistics, and graphics, J.D. Long & Paul Teetor
- Beginning R 4, from beginner to pro, Matt Wiley, Joshua F. Wiley
- SAS programming for elementary statistics, Carla L. Goad
- Practical data science with R, Nina Zumel, John Mount
- Advanced R statistical programming and data models, analysis, machine learning, and visualization, Matt Wiley, Joshua F. Wiley
- The R software, fundamentals of programming and statistical analysis, Pierre Lafaye de Micheaux, Rémy Drouilhet, Benoit Liquet
- Data analysis with open source tools, Philipp K. Janert
- Symbolic computation for statistical inference, D.F. Andrews and J.E. Stafford
- Statistics Using R, Sudha G. Purohit, Sharad D. Gore, Shailaja R. Deshmukh
- Statistics using Stata, an integrative approach, Sharon Lawner Weinberg ; Sarah Knapp Abramowitz
- SPSS data analysis for univariate, bivariate, and multivariate statistics, Daniel J. Denis
- Complex surveys, a guide to analysis using R, Thomas Lumley
- Mathematical statistics with applications in R, Kandethody M. Ramachandran, Chris P. Tsokos
- Deep learning with R, François Chollet ; with J.J. Allaire
- Computational statistics handbook with MATLAB, Wendy L. Martinez, Angel R. Martinez
- Discovering statistics using SAS, (and sex and drugs and rock 'n' roll), Andy Field and Jeremy Miles
- SAS/STAT 9.1 user's guide, [edited by Virginia Clark]
- Numerical methods of statistics, John F. Monahan
- Just Enough R!, An Interactive Approach to Machine Learning and Analytics, Richard J. Roiger
- An introduction to secondary data analysis with IBM SPSS statistics, John MacInnes
- Modern data science with R, Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton