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{ "item_title" : "A Biostatistics Toolbox for Data Analysis", "item_author" : [" Steve Selvin "], "item_description" : "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.", "item_img_path" : "https://covers1.booksamillion.com/covers/bam/1/10/711/308/1107113083_b.jpg", "price_data" : { "retail_price" : "64.00", "online_price" : "64.00", "our_price" : "64.00", "club_price" : "64.00", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
A Biostatistics Toolbox for Data Analysis|Steve Selvin

A Biostatistics Toolbox for Data Analysis

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Overview

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.

This item is Non-Returnable

Details

  • ISBN-13: 9781107113084
  • ISBN-10: 1107113083
  • Publisher: Cambridge University Press
  • Publish Date: October 2015
  • Dimensions: 10.33 x 7.15 x 1.36 inches
  • Shipping Weight: 2.66 pounds
  • Page Count: 578

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