Browsing by Subject "Mathematical statistics"

Now showing items 1-20 of 24

    • 40 puzzles and problems in probability and mathematical statistics 

      Schwarz, Wolfgang (Springer, 2008)

    • All of statistics : a concise course in statistical inference 

      Wasserman, Larry A. (Springer, 2004)
      This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book ...

    • An introduction to mathematical statistics and its applications 

      Larsen, Richard J.; Marx, Morris L. (Pearson, 2018)
      This book provides students who have already taken three or more semesters of calculus with the background to apply statistical principles. Meaty enough to guide a two-semester course, the book touches on both statistics ...

    • Applied linear statistical models 

      Kutner, Michael H.; Nachtsheim, Christopher J.; Neter, John; Li, William (McGraw-Hill, 2005)
      This book is the long established leading authoritative text and reference on statistical modeling. For students in most any discipline where statistical analysis or interpretation is used, ALSM serves as the standard work. ...

    • Applied predictive modeling 

      Kuhn, Max; Johnson, Kjell (Springer, 2013)
      This book covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous ...

    • Applied statistics : using SPSS, STATISTICA, MATLAB and R 

      Marques de Sá, Joaquim P. (Springer, 2007)

    • Data analysis using SAS Enterprise guide 

      Meyers, Lawrence S.; Gamst, Glenn; Guarino, A. J. (Cambridge University Press, 2009)
      This book presents the basic procedures for using SAS Enterprise Guide to analyse statistical data.

    • Econometric analysis of stochastic dominance : concepts, methods, tools, and applications 

      Yoon-Jae, Whang (Cambridge University Press, 2019)
      This book offers an up-to-date, comprehensive coverage of stochastic dominance and its related concepts in a unified framework. A method for ordering probability distributions, stochastic dominance has grown in importance ...

    • Foundations and applications of statistics : an introduction using R 

      Pruim, Randall J. (American Mathematical Society, 2018)
      Engaging and accessible, this book is useful to undergraduate students with a wide range of backgrounds and career goals. The exposition immediately begins with statistics, presenting concepts and results from probability ...

    • Handbook of spatial statistics 

      Gelfand, Alan E. (editor); Diggle, Peter J. (editor); Fuentes, Montserrat (editor) (CRC Press, 2010)
      The handbook of spatial statistics presents a comprehensive treatment of both classical and state-of-the-art aspects of this maturing area. It takes a unified, integrated approach to the material, providing cross-references ...

    • Introduction to mathematical statistics 

      Hogg, Robert V.; McKean, Joseph W.; Craig, Allen T. (Pearson, 2019)
      This book enhances student comprehension and retention with numerous, illustrative examples and exercises. Classical statistical inference procedures in estimation and testing are explored extensively, and the text’s ...

    • Introduction to probability and statistics for engineers and scientists 

      Ross, Sheldon M. (Elsevier, 2004)
      This book, 3rd edition, provides an introduction to applied probability and statistics for engineering or science majors . This updated text emphasizes the manner in which probability yields insight into statistical problems, ...

    • Introduction to probability and statistics for engineers and scientists 

      Ross, Sheldon M. (Academic Press, 2021)
      This book, 6th edition, uniquely emphasizes how probability informs statistical problems, thus helping readers develop an intuitive understanding of the statistical procedures commonly used by practicing engineers and ...

    • Introduction to probability and statistics for engineers and scientists 

      Ross, Sheldon M. (Elsevier, 2009)
      This updated text provides a superior introduction to applied probability and statistics for engineering or science majors. Ross emphasizes the manner in which probability yields insight into statistical problems; ultimately ...

    • Introduction to probability and statistics for engineers and scientists 

      Ross, Sheldon M. (Elsevier, 2014)
      This book provides a superior introduction to applied probability and statistics for engineering or science majors. Ross emphasizes the manner in which probability yields insight into statistical problems; ultimately ...

    • Mathematical statistics 

      Shao, Jun (Springer, 2003)

    • Principles of managerial statistics and data science 

      Rivera, Roberto (John Wiley & Sons, Inc., 2020)
      "This book introduces the topics of Big Data, data analytics and data science and features the use of open source data. Among the statistical topics described in this book are: data visualization, descriptive measures, ...

    • Principles of statistics for engineers and scientists 

      Navidi, William (McGraw-Hill, 2021)
      This book emphasizes statistical methods and how they can be applied to problems in science and engineering. The book contains many examples that feature real, contemporary data sets, both to motivate students and to show ...

    • Python for probability, statistics, and machine learning 

      Unpingco, José (Springer, 2016)
      This textbook, fully updated to feature Python version 3.7, covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules. The entire text, including all the figures and ...

    • Quantitative risk management : concepts, techniques, and tools 

      McNeil, Alexander J.; Frey, Rudiger; Embrechts, Paul (Princeton University Press, 2005)
      This book provides a comprehensive treatment of the theoretical concepts and modelling techniques of quantitative risk management and equips readers--whether financial risk analysts, actuaries, regulators, or students of ...