大葉大學圖書館 |

Statistical thinking in epidemiology[electronic resource] /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	614.40727
書名/作者:	Statistical thinking in epidemiology/ Yu-Kang Tu, Mark S. Gilthorpe.
作者:	Tu, Yu-Kang.
其他作者:	Gilthorpe, Mark S.
出版者:	Boca Raton : : CRC Press,, 2011.
面頁冊數:	1 online resource (xii, 219 p.) : : ill.
附註:	"A Chapman & Hall book."
標題:	Epidemiology - Statistical methods.
ISBN:	9781420099928 (electronic bk.)
ISBN:	1420099922 (electronic bk.)
書目註:	Includes bibliographical references (p. 189-202) and index.
內容註:	1. Introduction -- 2. Vector geometry of linear models of epidemiologists -- 3. Path diagrams and directed acyclic graphs -- 4. Mathematical coupling and regression to the mean in the relation between change and initial value -- 5. Analysis of change in pre-/post-test studies -- 6. Collinearity and multicollinearity -- 7. Is 'reversal paradox' a paradox? -- 8. Testing statistical interaction -- 9. Finding growth trajectories in lifecourse research -- 10. Partial least squares regression for lifecourse research -- 11. Concluding remarks.
摘要、提要註:	"While biomedical researchers may be able to follow instructions in the manuals accompanying the statistical software packages, they do not always have sufficient knowledge to choose the appropriate statistical methods and correctly interpret their results. Statistical Thinking in Epidemiology examines common methodological and statistical problems in the use of correlation and regression in medical and epidemiological research: mathematical coupling, regression to the mean, collinearity, the reversal paradox, and statistical interaction. Statistical Thinking in Epidemiology is about thinking statistically when looking at problems in epidemiology. The authors focus on several methods and look at them in detail: specific examples in epidemiology illustrate how different model specifications can imply different causal relationships amongst variables, and model interpretation is undertaken with appropriate consideration of the context of implicit or explicit causal relationships. This book is intended for applied statisticians and epidemiologists, but can also be very useful for clinical and applied health researchers who want to have a better understanding of statistical thinking. Throughout the book, statistical software packages R and Stata are used for general statistical modeling, and Amos and Mplus are used for structural equation modeling"--Provided by publisher.
電子資源:	http://www.crcnetbase.com/doi/book/10.1201/b11045

Statistical thinking in epidemiology[electronic resource] /
Tu, Yu-Kang.

Statistical thinking in epidemiology[electronic resource] /Yu-Kang Tu, Mark S. Gilthorpe. - Boca Raton :CRC Press,2011. - 1 online resource (xii, 219 p.) :ill.

"A Chapman & Hall book."

Includes bibliographical references (p. 189-202) and index.

1. Introduction -- 2. Vector geometry of linear models of epidemiologists -- 3. Path diagrams and directed acyclic graphs -- 4. Mathematical coupling and regression to the mean in the relation between change and initial value -- 5. Analysis of change in pre-/post-test studies -- 6. Collinearity and multicollinearity -- 7. Is 'reversal paradox' a paradox? -- 8. Testing statistical interaction -- 9. Finding growth trajectories in lifecourse research -- 10. Partial least squares regression for lifecourse research -- 11. Concluding remarks.

"While biomedical researchers may be able to follow instructions in the manuals accompanying the statistical software packages, they do not always have sufficient knowledge to choose the appropriate statistical methods and correctly interpret their results. Statistical Thinking in Epidemiology examines common methodological and statistical problems in the use of correlation and regression in medical and epidemiological research: mathematical coupling, regression to the mean, collinearity, the reversal paradox, and statistical interaction. Statistical Thinking in Epidemiology is about thinking statistically when looking at problems in epidemiology. The authors focus on several methods and look at them in detail: specific examples in epidemiology illustrate how different model specifications can imply different causal relationships amongst variables, and model interpretation is undertaken with appropriate consideration of the context of implicit or explicit causal relationships. This book is intended for applied statisticians and epidemiologists, but can also be very useful for clinical and applied health researchers who want to have a better understanding of statistical thinking. Throughout the book, statistical software packages R and Stata are used for general statistical modeling, and Amos and Mplus are used for structural equation modeling"--Provided by publisher.

ISBN: 9781420099928 (electronic bk.)Subjects--Topical Terms:

394610
Epidemiology
--Statistical methods.

LC Class. No.: RA652.2.M3 / T8 2011

Dewey Class. No.: 614.40727 / T883

Statistical thinking in epidemiology[electronic resource] /
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245 1 0 $a Statistical thinking in epidemiology $h [electronic resource] / $c Yu-Kang Tu, Mark S. Gilthorpe.
260 $a Boca Raton : $b CRC Press, $c 2011.
300 $a 1 online resource (xii, 219 p.) : $b ill.
500 $a "A Chapman & Hall book."
504 $a Includes bibliographical references (p. 189-202) and index.
505 0 $a 1. Introduction -- 2. Vector geometry of linear models of epidemiologists -- 3. Path diagrams and directed acyclic graphs -- 4. Mathematical coupling and regression to the mean in the relation between change and initial value -- 5. Analysis of change in pre-/post-test studies -- 6. Collinearity and multicollinearity -- 7. Is 'reversal paradox' a paradox? -- 8. Testing statistical interaction -- 9. Finding growth trajectories in lifecourse research -- 10. Partial least squares regression for lifecourse research -- 11. Concluding remarks.
520 $a "While biomedical researchers may be able to follow instructions in the manuals accompanying the statistical software packages, they do not always have sufficient knowledge to choose the appropriate statistical methods and correctly interpret their results. Statistical Thinking in Epidemiology examines common methodological and statistical problems in the use of correlation and regression in medical and epidemiological research: mathematical coupling, regression to the mean, collinearity, the reversal paradox, and statistical interaction. Statistical Thinking in Epidemiology is about thinking statistically when looking at problems in epidemiology. The authors focus on several methods and look at them in detail: specific examples in epidemiology illustrate how different model specifications can imply different causal relationships amongst variables, and model interpretation is undertaken with appropriate consideration of the context of implicit or explicit causal relationships. This book is intended for applied statisticians and epidemiologists, but can also be very useful for clinical and applied health researchers who want to have a better understanding of statistical thinking. Throughout the book, statistical software packages R and Stata are used for general statistical modeling, and Amos and Mplus are used for structural equation modeling"--Provided by publisher.
650 0 $a Epidemiology $x Statistical methods. $3 394610
700 1 $a Gilthorpe, Mark S. $3 587069
856 4 0 $u http://www.crcnetbase.com/doi/book/10.1201/b11045