This book offers a modern and accessible introduction to Statistical Inference, the science of inferring key information from data. Aimed at beginning undergraduate students in mathematics, it presents the concepts underpinning frequentist statistical theory.
Written in a conversational and informal style, this concise text concentrates on ideas and concepts, with key theorems stated and proved. Detailed worked examples are included and each chapter ends with a set of exercises, with full solutions given at the back of the book. Examples using R are provided throughout the book, with a brief guide to the software included. Topics covered in the book include: sampling distributions, properties of estimators, confidence intervals, hypothesis testing, ANOVA, and fitting a straight line to paired data.
Based on the author's extensive teaching experience, the material of the book has been honed by student feedback for over a decade. Assuming only some familiarity with elementary probability, this textbook has been devised for a one semester first course in statistics.

Provides a concise and self-contained introduction to statistical inference for beginning undergraduates Includes over 50 solved exercises and examples, including using R Key concepts and ideas are described in lucid terms without sacrificing mathematical rigor

Autorentext

Dr Jonathan Gillard is a Reader in Statistics at Cardiff University, Senior Fellow of the Higher Education Academy, and a member of the Statistics Interest Group of sigma: the UK network for excellence in mathematics and statistics support. He has taught statistical inference to mathematics undergraduates and postgraduates for over 10 years. Jonathan maintains a strong interest in innovative teaching methods, being an editorial board member of MSOR Connections. He is an active researcher of the theory of statistics and is currently working on a number of collaborative projects with the Office for National Statistics and National Health Service. His recent publications have included work on using regression in large dimensions, novel methods for forecasting, and new approaches for learning about the performance of machine learning algorithms.

Inhalt
1 Recap of Probability Fundamentals.- 2 Sampling and Sampling Distributions.- 3 Towards Estimation.- 4 Confidence Intervals.- 5 Hypothesis Testing.- 6 One-way Analysis of Variance (ANOVA).- 7 Regression: Fitting a Straight Line.- A brief introduction to R.- Solutions to Exercises.- Statistical Tables.- Index.