A biostatistics toolbox for data analysis

By: Selvin, Steve
Material type: TextTextPublisher: Cambridge Cambridge University Press 2016Description: xvii, 560 p.ISBN: 9781107113084Subject(s): Medical statistics | Biometry | Technology | Medicine and healthDDC classification: 610.21 Summary: This sophisticated package of statistical methods is for advanced master's (MPH) and PhD students in public health and epidemiology who are involved in the analysis of data. It makes the link from statistical theory to data analysis, focusing on the methods and data types most common in public health and related fields. Like most toolboxes, the statistical tools in this book are organized into sections with similar objectives. Unlike most toolboxes, however, these tools are accompanied by complete instructions, explanations, detailed examples, and advice on relevant issues and potential pitfalls - conveying skills, intuition, and experience. The only prerequisite is a first-year statistics course and familiarity with a computing package such as R, Stata, SPSS, or SAS. Though the book is not tied to a particular computing language, its figures and analyses were all created using R. Relevant R code, data sets, and links to public data sets are available from www.cambridge.org/9781107113084. Statistical tools are complemented by complete instructions, explanations, detailed examples, and advice on relevant issues and potential pitfalls Students only require familiarity with a computing package such as R, Stata, SPSS, or SAS, as well as a first-year statistics course Online resources include relevant R code, data sets, and links to public data sets. http://admin.cambridge.org/aq/academic/subjects/statistics-probability/statistics-life-sciences-medicine-and-health/biostatistics-toolbox-data-analysis?format=HB
List(s) this item appears in: VR_Healthcare Analytics | VR_Data Analytics, Data Visualization and Big Data
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Table of Contents

Part I. Basics:
1. Statistical distribution
2. Confidence intervals
3. A weighted average
4. Two discrete probability functions
5. Correlation
Part II. Applications:
6. The 2 x 2 table
7. Linear bivariate regression model
8. The 2 x k table
9. The log-linear Poisson regression model
10. Two-way and three-way tables analysis
11. Bootstrap analysis
12. Graphical analysis
13. The variance
14. The log-normal distribution
15. Nonparametric analysis
Part III. Survival:
16. Rates
17. Nonparametric survival analysis
18. The Weibull survival function
Part IV. Epidemiology:
19. Prediction, a natural measure of performance
20. The attributable risk summary
21. Time/space analysis
22. ROC curve and analysis
Part V. Genetics:
23. Selection: a statistical description
24. Mendelian segregation analysis
25. Admixed populations
26. Nonrandom mating
Part VI. Theory:
27. Statistical estimation
Part VII. R-Appendix.

This sophisticated package of statistical methods is for advanced master's (MPH) and PhD students in public health and epidemiology who are involved in the analysis of data. It makes the link from statistical theory to data analysis, focusing on the methods and data types most common in public health and related fields. Like most toolboxes, the statistical tools in this book are organized into sections with similar objectives. Unlike most toolboxes, however, these tools are accompanied by complete instructions, explanations, detailed examples, and advice on relevant issues and potential pitfalls - conveying skills, intuition, and experience. The only prerequisite is a first-year statistics course and familiarity with a computing package such as R, Stata, SPSS, or SAS. Though the book is not tied to a particular computing language, its figures and analyses were all created using R. Relevant R code, data sets, and links to public data sets are available from www.cambridge.org/9781107113084.

Statistical tools are complemented by complete instructions, explanations, detailed examples, and advice on relevant issues and potential pitfalls
Students only require familiarity with a computing package such as R, Stata, SPSS, or SAS, as well as a first-year statistics course
Online resources include relevant R code, data sets, and links to public data sets.


http://admin.cambridge.org/aq/academic/subjects/statistics-probability/statistics-life-sciences-medicine-and-health/biostatistics-toolbox-data-analysis?format=HB

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