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Introduction to statistics and data analysiswith exercises, solutions and applications in R /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Introduction to statistics and data analysisby Christian Heumann, Michael Schomaker, Shalabh.
Reminder of title:	with exercises, solutions and applications in R /
Author:	Heumann, Christian.
other author:	Schomaker, Michael.
Published:	Cham :Springer International Publishing :2022.
Description:	xvii, 584 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Statistics.
Online resource:	https://doi.org/10.1007/978-3-031-11833-3
ISBN:	9783031118333$q(electronic bk.)

Introduction to statistics and data analysiswith exercises, solutions and applications in R /
Heumann, Christian.

Introduction to statistics and data analysiswith exercises, solutions and applications in R /[electronic resource] :by Christian Heumann, Michael Schomaker, Shalabh. - Second edition. - Cham :Springer International Publishing :2022. - xvii, 584 p. :ill. (some col.), digital ;24 cm.

Part I Descriptive Statistics: Introduction and Framework -- Frequency Measures and Graphical Representation of Data -- Measures of Central Tendency and Dispersion -- Association of Two Variables -- Part I Probability Calculus: Combinatorics -- Elements of Probability Theory -- Random Variables -- Probability Distributions -- Part III Inductive Statistics: Inference -- Hypothesis Testing -- Linear Regression -- Logistic Regression -- Part IV Additional Topics Simple Random Sampling and Bootstrapping -- Causality -- Part V Appendices: Introduction to R -- Solutions to Exercises -- Technical Appendix -- Visual Summaries.

Now in its second edition, this introductory statistics textbook conveys the essential concepts and tools needed to develop and nurture statistical thinking. It presents descriptive, inductive and explorative statistical methods and guides the reader through the process of quantitative data analysis. This revised and extended edition features new chapters on logistic regression, simple random sampling, including bootstrapping, and causal inference. The text is primarily intended for undergraduate students in disciplines such as business administration, the social sciences, medicine, politics, and macroeconomics. It features a wealth of examples, exercises and solutions with computer code in the statistical programming language R, as well as supplementary material that will enable the reader to quickly adapt the methods to their own applications.

ISBN: 9783031118333$q(electronic bk.)

Standard No.: 10.1007/978-3-031-11833-3doiSubjects--Topical Terms:

182057
Statistics.

LC Class. No.: QA276

Dewey Class. No.: 519.5

Introduction to statistics and data analysiswith exercises, solutions and applications in R /
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245 1 0 $a Introduction to statistics and data analysis $h [electronic resource] : $b with exercises, solutions and applications in R / $c by Christian Heumann, Michael Schomaker, Shalabh.
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505 0 $a Part I Descriptive Statistics: Introduction and Framework -- Frequency Measures and Graphical Representation of Data -- Measures of Central Tendency and Dispersion -- Association of Two Variables -- Part I Probability Calculus: Combinatorics -- Elements of Probability Theory -- Random Variables -- Probability Distributions -- Part III Inductive Statistics: Inference -- Hypothesis Testing -- Linear Regression -- Logistic Regression -- Part IV Additional Topics Simple Random Sampling and Bootstrapping -- Causality -- Part V Appendices: Introduction to R -- Solutions to Exercises -- Technical Appendix -- Visual Summaries.
520 $a Now in its second edition, this introductory statistics textbook conveys the essential concepts and tools needed to develop and nurture statistical thinking. It presents descriptive, inductive and explorative statistical methods and guides the reader through the process of quantitative data analysis. This revised and extended edition features new chapters on logistic regression, simple random sampling, including bootstrapping, and causal inference. The text is primarily intended for undergraduate students in disciplines such as business administration, the social sciences, medicine, politics, and macroeconomics. It features a wealth of examples, exercises and solutions with computer code in the statistical programming language R, as well as supplementary material that will enable the reader to quickly adapt the methods to their own applications.
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