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001 978-3-319-46162-5
003 DE-He213
005 20210118114613.0
007 cr nn 008mamaa
008 170127s2016 gw | s |||| 0|eng d
020 _a9783319461625
_9978-3-319-46162-5
024 _a10.1007/978-3-319-46162-5
_2doi
050 _aQA276-280
072 _aPBT
_2bicssc
072 _aMAT029000
_2bisacsh
072 _aPBT
_2thema
082 _a519.5
_223
100 _aHeumann, Christian.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 _aIntroduction to Statistics and Data Analysis
_h[electronic resource] :
_bWith Exercises, Solutions and Applications in R /
_cby Christian Heumann, Michael Schomaker, Shalabh.
250 _a1st ed. 2016.
264 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2016.
300 _aXIII, 456 p. 89 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 _aPart 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.
520 _aThis 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.
650 _aStatistics .
650 _aEconometrics.
650 _aMacroeconomics.
650 _aStatistical Theory and Methods.
_0https://scigraph.springernature.com/ontologies/product-market-codes/S11001
650 _aStatistics for Business, Management, Economics, Finance, Insurance.
_0https://scigraph.springernature.com/ontologies/product-market-codes/S17010
650 _aEconometrics.
_0https://scigraph.springernature.com/ontologies/product-market-codes/W29010
650 _aMacroeconomics/Monetary Economics//Financial Economics.
_0https://scigraph.springernature.com/ontologies/product-market-codes/W32000
700 _aSchomaker, Michael.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 _aShalabh.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 _aSpringerLink (Online service)
773 _tSpringer Nature eBook
776 _iPrinted edition:
_z9783319461601
776 _iPrinted edition:
_z9783319461618
776 _iPrinted edition:
_z9783319834566
856 _uhttps://doi.org/10.1007/978-3-319-46162-5
912 _aZDB-2-SMA
912 _aZDB-2-SXMS
999 _c9365
_d9365