This is a series of five books each covering a separate unit of the Advanced Higher course. This unit structure gives you the flexibility to put together a complete course or to offer separate units of study.

This is a series of five books each covering a separate unit of the Advanced Higher course. This unit structure gives you the flexibility to put together a complete course or to offer separate units of study.

This new book provides additional practice exercises matched precisely to the performance criteria for all four units of Higher Mathematics. It prepares students for internal Unit Tests and external Course Assessments in Mathematics and Statistics.

Developed for those following the Statistics 1 unit within the Mathematics and Applied Mathematics courses at Advanced Higher Level, this title is part of a series of five covering the Advanced Higher units in Mathematics and Statistics. All the books in the series pprovide: a straightforward route through the course with complete and ordered coverage of the units; numerous questions for practice and consolidation; thorough preparation for end-of-unit and end of course assessments.

This is a series of five books each covering a separate unit of the Advanced Higher course. This unit structure gives you the flexibility to put together a complete course or to offer separate units of study.

Full coverage of the Statistics unit is provided in a separate book which covers everything your students need for this option.

Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. 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 includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.