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A First Course in Statistical Inference

A First Course in Statistical Inference
Catalogue Information
Field name Details
Dewey Class 519.5
Title A First Course in Statistical Inference ([EBook]) / by Jonathan Gillard.
Author Gillard, Jonathan
Other name(s) SpringerLink (Online service)
Edition statement 1st ed. 2020.
Publication Cham : : Springer International Publishing : : Imprint: Springer, , 2020.
Physical Details X, 164 p. 24 illus., 7 illus. in color. : online resource.
Series Springer undergraduate mathematics series 1615-2085
ISBN 9783030395612
Summary Note 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.:
Contents note 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.
Mode of acces to digital resource Digital book. Cham Springer Nature 2020. - Mode of access: World Wide Web. System requirements: Internet Explorer 6.0 (or higher) or Firefox 2.0 (or higher). Available as searchable text in PDF format
System details note - Online access to this digital book is restricted to subscription institutions through IP address (only for SISSA internal users).
Internet Site https://doi.org/10.1007/978-3-030-39561-2
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