A. John Bailer – författare

Graduate students of statistics studying environmental data analysis will find this invaluable as will practicing data analysts and environmental scientists including specialists in atmospheric science, biology and biomedicine, chemistry, ecology, environmental health, geography, and geology.

How do you learn about what’s going on in the world? Did a news headline grab your attention? Did a news story report on recent research? What do you need to know to be a critical consumer of the news you read? If you are looking to start developing your data self-defense and critical news consumption skills, this book is for you! It reflects a long-term collaboration between a statistician and a journalist to shed light on the statistics behind the stories and the stories behind the statistics. The only prerequisite for enjoying this book is an interest in developing the skills and insights for better understanding news stories that incorporate quantitative information.

Chapters in Statistics Behind the Headlines kick off with a news story headline and a summary of the story itself. The meat of each chapter consists of an exploration of the statistical and journalism concepts needed to understand the data analyzed and reported in the story. The chapters are organized around these sections:

Chapters close with connections to the Stats + Stories podcast.

How do you learn about what’s going on in the world? Did a news headline grab your attention? Did a news story report on recent research? What do you need to know to be a critical consumer of the news you read? If you are looking to start developing your data self-defense and critical news consumption skills, this book is for you! It reflects a long-term collaboration between a statistician and a journalist to shed light on the statistics behind the stories and the stories behind the statistics. The only prerequisite for enjoying this book is an interest in developing the skills and insights for better understanding news stories that incorporate quantitative information.

Chapters in Statistics Behind the Headlines kick off with a news story headline and a summary of the story itself. The meat of each chapter consists of an exploration of the statistical and journalism concepts needed to understand the data analyzed and reported in the story. The chapters are organized around these sections:

Chapters close with connections to the Stats + Stories podcast.

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.