國立虎尾科技大學 |

Statistics for Health Data Science = An Organic Approach /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Statistics for Health Data Science/ by Ruth Etzioni, Micha Mandel, Roman Gulati.
其他題名:	An Organic Approach /
作者:	Etzioni, Ruth.
其他作者:	Gulati, Roman.
面頁冊數:	XXII, 222 p. 65 illus., 43 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Public Health. -
電子資源:	https://doi.org/10.1007/978-3-030-59889-1
ISBN:	9783030598891

Statistics for Health Data Science = An Organic Approach /
Etzioni, Ruth.

Statistics for Health Data ScienceAn Organic Approach /[electronic resource] :by Ruth Etzioni, Micha Mandel, Roman Gulati. - 1st ed. 2020. - XXII, 222 p. 65 illus., 43 illus. in color.online resource. - Springer Texts in Statistics,1431-875X. - Springer Texts in Statistics,.

Students and researchers in the health sciences are faced with greater opportunity and challenge than ever before. The opportunity stems from the explosion in publicly available data that simultaneously informs and inspires new avenues of investigation. The challenge is that the analytic tools required go far beyond the standard methods and models of basic statistics. This textbook aims to equip health care researchers with the most important elements of a modern health analytics toolkit, drawing from the fields of statistics, health econometrics, and data science. This textbook is designed to overcome students’ anxiety about data and statistics and to help them to become confident users of appropriate analytic methods for health care research studies. Methods are presented organically, with new material building naturally on what has come before. Each technique is motivated by a topical research question, explained in non-technical terms, and accompanied by engaging explanations and examples. In this way, the authors cultivate a deep (“organic”) understanding of a range of analytic techniques, their assumptions and data requirements, and their advantages and limitations. They illustrate all lessons via analyses of real data from a variety of publicly available databases, addressing relevant research questions and comparing findings to those of published studies. Ultimately, this textbook is designed to cultivate health services researchers that are thoughtful and well informed about health data science, rather than data analysts. This textbook differs from the competition in its unique blend of methods and its determination to ensure that readers gain an understanding of how, when, and why to apply them. It provides the public health researcher with a way to think analytically about scientific questions, and it offers well-founded guidance for pairing data with methods for valid analysis. Readers should feel emboldened to tackle analysis of real public datasets using traditional statistical models, health econometrics methods, and even predictive algorithms. Accompanying code and data sets are provided in an author site: https://roman-gulati.github.io/statistics-for-health-data-science/.

ISBN: 9783030598891

Standard No.: 10.1007/978-3-030-59889-1doiSubjects--Topical Terms:

592982
Public Health.

LC Class. No.: QA276-280

Dewey Class. No.: 519.5

Statistics for Health Data Science = An Organic Approach /
LDR:03693nam a22004095i 4500 001 1031074
003 DE-He213
005 20210104215343.0
007 cr nn 008mamaa
008 210318s2020 gw | s |||| 0|eng d
020 $a 9783030598891 $9 978-3-030-59889-1
024 7 $a 10.1007/978-3-030-59889-1 $2 doi
035 $a 978-3-030-59889-1
050 4 $a QA276-280
072 7 $a PBT $2 bicssc
072 7 $a MED090000 $2 bisacsh
072 7 $a PBT $2 thema
072 7 $a MBNS $2 thema
082 0 4 $a 519.5 $2 23
100 1 $a Etzioni, Ruth. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1328130
245 1 0 $a Statistics for Health Data Science $h [electronic resource] : $b An Organic Approach / $c by Ruth Etzioni, Micha Mandel, Roman Gulati.
250 $a 1st ed. 2020.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a XXII, 222 p. 65 illus., 43 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Springer Texts in Statistics, $x 1431-875X
520 $a Students and researchers in the health sciences are faced with greater opportunity and challenge than ever before. The opportunity stems from the explosion in publicly available data that simultaneously informs and inspires new avenues of investigation. The challenge is that the analytic tools required go far beyond the standard methods and models of basic statistics. This textbook aims to equip health care researchers with the most important elements of a modern health analytics toolkit, drawing from the fields of statistics, health econometrics, and data science. This textbook is designed to overcome students’ anxiety about data and statistics and to help them to become confident users of appropriate analytic methods for health care research studies. Methods are presented organically, with new material building naturally on what has come before. Each technique is motivated by a topical research question, explained in non-technical terms, and accompanied by engaging explanations and examples. In this way, the authors cultivate a deep (“organic”) understanding of a range of analytic techniques, their assumptions and data requirements, and their advantages and limitations. They illustrate all lessons via analyses of real data from a variety of publicly available databases, addressing relevant research questions and comparing findings to those of published studies. Ultimately, this textbook is designed to cultivate health services researchers that are thoughtful and well informed about health data science, rather than data analysts. This textbook differs from the competition in its unique blend of methods and its determination to ensure that readers gain an understanding of how, when, and why to apply them. It provides the public health researcher with a way to think analytically about scientific questions, and it offers well-founded guidance for pairing data with methods for valid analysis. Readers should feel emboldened to tackle analysis of real public datasets using traditional statistical models, health econometrics methods, and even predictive algorithms. Accompanying code and data sets are provided in an author site: https://roman-gulati.github.io/statistics-for-health-data-science/.
650 2 4 $a Public Health. $3 592982
650 2 4 $a Statistical Theory and Methods. $3 671396
650 2 4 $a Big Data/Analytics. $3 1106909
650 1 4 $a Statistics for Life Sciences, Medicine, Health Sciences. $3 670172
650 0 $a Epidemiology. $3 635923
650 0 $a Public health. $3 560998
650 0 $a Big data. $3 981821
650 0 $a Biostatistics. $3 783654
650 0 $a Statistics . $3 1253516
700 1 $a Gulati, Roman. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1328132
700 1 $a Mandel, Micha. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1328131
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030598884
776 0 8 $i Printed edition: $z 9783030598907
776 0 8 $i Printed edition: $z 9783030598914
830 0 $a Springer Texts in Statistics, $x 1431-875X $3 1257998
856 4 0 $u https://doi.org/10.1007/978-3-030-59889-1
912 $a ZDB-2-SMA
912 $a ZDB-2-SXMS
950 $a Mathematics and Statistics (SpringerNature-11649)
950 $a Mathematics and Statistics (R0) (SpringerNature-43713)