Statistics in Natural Resources:Applications with R

R

环境科学技术基础学科

原   价:
1370.00
售   价:
1096.00
优惠
平台大促 低至8折优惠
发货周期:预计5-7周发货
作      者
出  版 社
出版时间
2022年08月19日
装      帧
ISBN
9781032258782
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页      码
354
开      本
234 x 156 mm (6.14 x 9.21
语      种
英文
版      次
1
综合评分
5 分
我 要 买
- +
库存 30 本
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图书简介
To manage our environment sustainably, professionals must understand the quality and quantity of our natural resources. Statistical analysis provides information that supports management decisions and is universally used across scientific disciplines. Statistics in Natural Resources: Applications with R focuses on the application of statistical analyses in the environmental, agricultural, and natural resources disciplines. This is a book well suited for current or aspiring natural resource professionals who are required to analyze data and perform statistical analyses in their daily work. More seasoned professionals that have previously had a course or two in statistics will also find the content familiar. This text can also serve as a bridge between professionals who understand statistics and want to learn how to perform analyses on natural resources data in R. The primary goal of this book is to learn and apply common statistical methods used in natural resources by using the R programming language. If you dedicate considerable time to this book, you will: Develop analytical and visualization skills for investigating the behavior of agricultural and natural resources data. Become competent in importing, analyzing, and visualizing complex data sets in the R environment. Recode, combine, and restructure data sets for statistical analysis and visualization. Appreciate probability concepts as they apply to environmental problems. Understand common distributions used in statistical applications and inference. Summarize data effectively and efficiently for reporting purposes. Learn the tasks required to perform a variety of statistical hypothesis tests and interpret their results. Understand which modeling frameworks are appropriate for your data and how to interpret predictions. Includes over 130 exercises in R, with solutions available on the book
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