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  • Sách/Book


  • Authors: Michael H. Kutner (2005)

  • A text and reference on statistical modeling, this work includes brief introductory and review material, and then proceeds through regression and modeling for the first half, and through ANOVA and Experimental Design in the second half. It provides a use of computing and graphical analysis.

  • Sách/Book


  • Authors: Paul Deitel; Harvey Deitel (2017)

  • Interesting, entertaining, and challenging exercises encourage students to make a difference and use computers and the Internet to work on problems. To keep readers up-to-date with leading-edge computing technologies, the Tenth Edition conforms to the C++11 standard and the new C++14 standard.

  • Sách/Book


  • Authors: Richard A. Johnson (2014)

  • This market leader offers a readable introduction to the statistical analysis of multivariate observations. Gives readers the knowledge necessary to make proper interpretations and select appropriate techniques for analyzing multivariate data. Starts with a formulation of the population models, delineates the corresponding sample results, and liberally illustrates everything with examples.

  • Sách/Book


  • Authors: Ian Pointer (2019)

  • This book is here to help you get your job done. In general, if example code is offered with this book, you may use it in your programs and documentation. You do not need to contact us for permission unless you’re reproducing a significant portion of the code. For example, writing a program that uses several chunks of code from this book does not require permission.

  • Tập bài giảng/Lecture


  • Authors: Cao Kim Ánh (2021)

  • Tổng quan về kĩ nghệ phần mềm. Các mô hình phát triển phần mềm. Các khái niệm về quản lí dự án. Kĩ nghệ phần mềm truyền thống. Kỹ nghệ phần mềm hướng sự vật. Các chủ đề nâng cao trong kĩ nghệ phần mềm

  • Sách/Book


  • Authors: Wolfgang Karl Härdle (2024)

  • Now in its sixth edition, this textbook presents the tools and concepts used in multivariate data analysis in a style accessible for non-mathematicians and practitioners. Each chapter features hands-on exercises that showcase applications across various fields of multivariate data analysis.