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＜電子ブック＞
Introduction to Statistics and Data Analysis : With Exercises, Solutions and Applications in R / by Christian Heumann, Michael Schomaker, Shalabh

版	1st ed. 2016.
出版者	(Cham : Springer International Publishing : Imprint: Springer)
出版年	2016
本文言語	英語
大きさ	XIII, 456 p. 89 illus : online resource
著者標目	*Heumann, Christian author Schomaker, Michael author Shalabh author SpringerLink (Online service)
件　名	LCSH:Statistics  LCSH:Econometrics LCSH:Macroeconomics FREE:Statistical Theory and Methods FREE:Statistics in Business, Management, Economics, Finance, Insurance FREE:Econometrics FREE:Macroeconomics and Monetary Economics
一般注記	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 methodsto their own applications HTTP:URL=https://doi.org/10.1007/978-3-319-46162-5

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