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Introduction to statistics and data ...

Heumann, Christian.

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Introduction to statistics and data analysis = with exercises, solutions and applications in R /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Introduction to statistics and data analysis/ by Christian Heumann, Michael Schomaker, Shalabh.
其他題名:	with exercises, solutions and applications in R /
作者:	Heumann, Christian.
其他作者:	Schomaker, Michael.
出版者:	Cham :Springer International Publishing : : 2016.,
面頁冊數:	xiii, 456 p. :ill., digital ;24 cm.
內容註:	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 -- Part IV Appendices: Introduction to R -- Solutions to Exercises -- Technical Appendix -- Visual Summaries.
Contained By:	Springer eBooks
標題:	Mathematical statistics. -
電子資源:	http://dx.doi.org/10.1007/978-3-319-46162-5
ISBN:	9783319461625

Introduction to statistics and data analysis = with 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. - Cham :Springer International Publishing :2016. - xiii, 456 p. :ill., 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 -- Part IV Appendices: Introduction to R -- Solutions to Exercises -- Technical Appendix -- Visual Summaries.

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. In the experimental sciences and interdisciplinary research, data analysis has become an integral part of any scientific study. Issues such as judging the credibility of data, analyzing the data, evaluating the reliability of the obtained results and finally drawing the correct and appropriate conclusions from the results are vital. The text is primarily intended for undergraduate students in disciplines like business administration, the social sciences, medicine, politics, macroeconomics, etc. 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 all methods to their own applications.

ISBN: 9783319461625

Standard No.: 10.1007/978-3-319-46162-5doiSubjects--Topical Terms:

516858
Mathematical statistics.

LC Class. No.: QA276

Dewey Class. No.: 519.5

Introduction to statistics and data analysis = with exercises, solutions and applications in R /
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520 $a 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. In the experimental sciences and interdisciplinary research, data analysis has become an integral part of any scientific study. Issues such as judging the credibility of data, analyzing the data, evaluating the reliability of the obtained results and finally drawing the correct and appropriate conclusions from the results are vital. The text is primarily intended for undergraduate students in disciplines like business administration, the social sciences, medicine, politics, macroeconomics, etc. 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 all methods to their own applications.
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