A first course in statistical inference
Gillard, Jonathan
A first course in statistical inference - Cham : Springer, 2020 - x, 164 p. ; ill., (some col.), 24 cm - Springer undergraduate mathematics series .
Includes index.
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.
9783030395605
Acceptance region
Central Limit Theorem
Chi-squared distribution
Cumulative distribution function
Discrete random variable
Null hypothesis
p-value
Poisson distribution
Probability density function
Probability mass function
Randomsample
Significance level
Student's t-distribution
519.5 / GIL
A first course in statistical inference - Cham : Springer, 2020 - x, 164 p. ; ill., (some col.), 24 cm - Springer undergraduate mathematics series .
Includes index.
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.
9783030395605
Acceptance region
Central Limit Theorem
Chi-squared distribution
Cumulative distribution function
Discrete random variable
Null hypothesis
p-value
Poisson distribution
Probability density function
Probability mass function
Randomsample
Significance level
Student's t-distribution
519.5 / GIL