Provides an introduction to statistical thinking that will help the public consume results reported in the popular media.

Statistics for Environmental Biology and Toxicology presents and illustrates statistical methods appropriate for the analysis of environmental data obtained in biological or toxicological experiments. Beginning with basic probability and statistical inferences, this text progresses through non-linear and generalized linear models, trend testing, time-to-event data and analysis of cross-classified tabular and categorical data. For the more complex analyses, extensive examples including SAS and S-PLUS programming code are provided to assist the reader when implementing the methods in practice.

Statistical Programming in SAS Second Edition provides a foundation for programming to implement statistical solutions using SAS, a system that has been used to solve data analytic problems for more than 40 years. The author includes motivating examples to inspire readers to generate programming solutions. Upper-level undergraduates, beginning graduate students, and professionals involved in generating programming solutions for data-analytic problems will benefit from this book. The ideal background for a reader is some background in regression modeling and introductory experience with computer programming. The coverage of statistical programming in the second edition includes Getting data into the SAS system, engineering new features, and formatting variables Writing readable and well-documented code Structuring, implementing, and debugging programs that are well documented Creating solutions to novel problems Combining data sources, extracting parts of data sets, and reshaping data sets as needed for other analyses Generating general solutions using macros Customizing output Producing insight-inspiring data visualizations Parsing, processing, and analyzing text Programming solutions using matrices and connecting to R Processing text Programming with matrices Connecting SAS with R Covering topics that are part of both base and certification exams.

Statistical Programming in SAS Second Edition provides a foundation for programming to implement statistical solutions using SAS, a system that has been used to solve data analytic problems for more than 40 years. The author includes motivating examples to inspire readers to generate programming solutions. Upper-level undergraduates, beginning graduate students, and professionals involved in generating programming solutions for data-analytic problems will benefit from this book. The ideal background for a reader is some background in regression modeling and introductory experience with computer programming. The coverage of statistical programming in the second edition includes Getting data into the SAS system, engineering new features, and formatting variables Writing readable and well-documented code Structuring, implementing, and debugging programs that are well documented Creating solutions to novel problems Combining data sources, extracting parts of data sets, and reshaping data sets as needed for other analyses Generating general solutions using macros Customizing output Producing insight-inspiring data visualizations Parsing, processing, and analyzing text Programming solutions using matrices and connecting to R Processing text Programming with matrices Connecting SAS with R Covering topics that are part of both base and certification exams.

Conservation agriculture is a sustainable production model that not only optimizes crop yields, but also reaps economic and environmental benefits as well. The adoption of successful conservation agriculture methods has resulted in energy savings, higher organic matter content and biotic activity in soil, increased crop-water availability and thus resilience to drought, improved recharge of aquifers, less erosion, and reduced impacts from the weather associated with climate change in general. Agricultural Impacts of Climate Change examines several important aspects of crop production, such as climate change, soil management, farm machinery, and different methods for sustainable conservation agriculture. It presents spatial distribution of a daily, monthly and annual precipitation concentration indices, Diffuse Reflectance Fourier Transform Infrared Spectroscopy for analyzing the organic matter in soil, and adaptation strategies for climate-related plant disease scenarios. It also discusses solar energy-based greenhouse modeling, precision farming using remote sensing and GIS, and various types of machinery used for conservation agriculture. Features: Examines the effects of climate change on agriculture and the related strategies for mitigation through practical, real-world examples Explores innovative on-farm technology options to increase system efficiency resulting in improved water usage Presents examples of precision farming using climate-resilient technologies

Provides an introduction to statistical thinking that will help the public consume results reported in the popular media.