Using R for Introductory Statistics
by John Verzani
Publisher: Chapman & Hall/CRC 2004
Number of pages: 114
The author presents a self-contained treatment of statistical topics and the intricacies of the R software. The book treats exploratory data analysis with more attention than is typical, includes a chapter on simulation, and provides a unified approach to linear models. This text lays the foundation for further study and development in statistics using R.
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by Robert B. Ash - University of Illinois
These notes are based on a course that the author gave at UIUC. No prior knowledge of statistics is assumed. A standard first course in probability is a prerequisite, but the first 8 lectures review results that are important in statistics.
by Brian S Blais - Save The Broccoli Publishing
This is a new approach to an introductory statistical inference textbook, motivated by probability theory as logic. It is targeted to the typical Statistics 101 college student, and covers the topics typically covered in the first semester.
by Douglas McNair (ed.) - IntechOpen
Bayesian networks have recently experienced increased interest and diverse applications in numerous areas, including economics, risk analysis, assets and liabilities management, AI and robotics, transportation systems planning and optimization, etc.
by Henry Lewis Rietz - Open Court Pub. Co
The book shifts the emphasis in the study of statistics in the direction of the consideration of the underlying theory involved in certain important methods of statistical analysis, and introduces mathematical statistics to a wider range of readers.