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A First Course in Statistical Inference - Jonathan Gillard

A First Course in Statistical Inference

Buch | Softcover
X, 164 Seiten
2020 | 1st ed. 2020
Springer International Publishing (Verlag)
978-3-030-39560-5 (ISBN)
CHF 59,90 inkl. MwSt
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.

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.

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.

Erscheinungsdatum
Reihe/Serie Springer Undergraduate Mathematics Series
Zusatzinfo X, 164 p. 24 illus., 7 illus. in color.
Verlagsort Cham
Sprache englisch
Maße 155 x 235 mm
Gewicht 279 g
Themenwelt Mathematik / Informatik Mathematik Computerprogramme / Computeralgebra
Mathematik / Informatik Mathematik Statistik
Mathematik / Informatik Mathematik Wahrscheinlichkeit / Kombinatorik
Schlagworte Confidence interval • Data Science • Distribution Theory • hypothesis testing • R • Statistical Inference
ISBN-10 3-030-39560-X / 303039560X
ISBN-13 978-3-030-39560-5 / 9783030395605
Zustand Neuware
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