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An LGBT romantic comedy with a twist from the Comedy Women in Print prize winner Laura Steven, author of The Exact Opposite of Okay. A hilarious love story with bite, for fans of Sex Education, Booksmart, Becky Albertalli's Love, Simon and Jenny Han's To All The Boys I've Loved Before.Physics genius Caro Kerber-Murphy knows she’s smart. With straight As and a college scholarship already in the bag, she’s meeting her two dads’ colossal expectations and then some. But there’s one test she’s never quite been able to ace: love. And when, in a particularly desperate moment, Caro discovers a (definitely questionable) scientific breakthrough that promises to make you irresistible to everyone around you, she wonders if this could be the key.What happens next will change everything Caro thought she knew about chemistry – in the lab and in love. Is hot guy Haruki with her of his own free will? Are her feelings for her best friend some sort of side-effect? Will her dog, Sirius, ever stop humping her leg?Laura is the author of fiercely funny feminist comedy The Exact Opposite of Okay and its sequel, A Girl Called Shameless. The Exact Opposite of Okay was a bestselling young adult debut in 2018 and won the inaugural Comedy Women in Print prize, founded by Helen Lederer, from a shortlist including Gail Honeyman's Eleanor Oliphant is Completely Fine and Why Mummy Swears by Gill Sims.Praise for The Exact Opposite of Okay:'A brilliant social satire … disarmingly charming and relatable … it was hilarious.Laura Steven is an explosive talent on the page!' CWIP judges MarianKeyes, Kathy Lette, Katy Brand, Allison Pearson, Shazia Mirza and Jennifer Young'Laura Steven simultaneously destroyed the patriarchy and made me laugh so hard I choked. I will protect Izzy O'Neill with my life.' Becky Albertalli, author of Love, Simon

Quantitative and Statistical Research Methods This user-friendly textbook teaches students to understand and apply procedural steps in completing quantitative studies. It explains statistics while progressing through the steps of the hypothesis-testing process from hypothesis to results. The research problems used in the book reflect statistical applications related to interesting and important topics. In addition, the book provides a Research Analysis and Interpretation Guide to help students analyze research articles. Designed as a hands-on resource, each chapter covers a single research problem and offers directions for implementing the research method from start to finish. Readers will learn how to: Pinpoint research questions and hypotheses Identify, classify, and operationally define the study variables Choose appropriate research designs Conduct power analysis Select an appropriate statistic for the problem Use a data set Conduct data screening and analyses using SPSS Interpret the statistics Write the results related to the problem Quantitative and Statistical Research Methods allows students to immediately, independently, and successfully apply quantitative methods to their own research projects.

A comprehensive guide to statistical hypothesis testing with examples in SAS and R When analyzing datasets the following questions often arise: Is there a short hand procedure for a statistical test available in SAS or R? If so, how do I use it? If not, how do I program the test myself? This book answers these questions and provides an overview of the most common statistical test problems in a comprehensive way, making it easy to find and perform an appropriate statistical test. A general summary of statistical test theory is presented, along with a basic description for each test, including the necessary prerequisites, assumptions, the formal test problem and the test statistic. Examples in both SAS and R are provided, along with program code to perform the test, resulting output and remarks explaining the necessary program parameters. Key features: • Provides examples in both SAS and R for each test presented. • Looks at the most common statistical tests, displayed in a clear and easy to follow way. • Supported by a supplementary website http://www.d-taeger.de featuring example program code. Academics, practitioners and SAS and R programmers will find this book a valuable resource. Students using SAS and R will also find it an excellent choice for reference and data analysis.

Statistics with JMP: Hypothesis Tests, ANOVA and Regression Peter Goos, University of Leuven and University of Antwerp, Belgium David Meintrup, University of Applied Sciences Ingolstadt, Germany A first course on basic statistical methodology using JMP This book provides a first course on parameter estimation (point estimates and confidence interval estimates), hypothesis testing, ANOVA and simple linear regression. The authors approach combines mathematical depth with numerous examples and demonstrations using the JMP software. Key features: Provides a comprehensive and rigorous presentation of introductory statistics that has been extensively classroom tested. Pays attention to the usual parametric hypothesis tests as well as to non-parametric tests (including the calculation of exact p-values). Discusses the power of various statistical tests, along with examples in JMP to enable in-sight into this difficult topic. Promotes the use of graphs and confidence intervals in addition to p-values. Course materials and tutorials for teaching are available on the book's companion website. Masters and advanced students in applied statistics, industrial engineering, business engineering, civil engineering and bio-science engineering will find this book beneficial. It also provides a useful resource for teachers of statistics particularly in the area of engineering.

A novel presentation of rank and permutation tests, with accessible guidance to applications in R Nonparametric testing problems are frequently encountered in many scientific disciplines, such as engineering, medicine and the social sciences. This book summarizes traditional rank techniques and more recent developments in permutation testing as robust tools for dealing with complex data with low sample size. Key Features: Examines the most widely used methodologies of nonparametric testing. Includes extensive software codes in R featuring worked examples, and uses real case studies from both experimental and observational studies. Presents and discusses solutions to the most important and frequently encountered real problems in different fields. Features a supporting website (www.wiley.com/go/hypothesis_testing) containing all of the data sets examined in the book along with ready to use R software codes. Nonparametric Hypothesis Testing combines an up to date overview with useful practical guidance to applications in R, and will be a valuable resource for practitioners and researchers working in a wide range of scientific fields including engineering, biostatistics, psychology and medicine.