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Ripper Street: Season 1 and 2 (DVD): Matthew Macfadyen, Jerome Flynn, Adam Rothenberg, Myanna Buring, Jonathan Barnwell, David... Ripper Street: Season 1 and 2 (DVD)
Matthew Macfadyen, Jerome Flynn, Adam Rothenberg, Myanna Buring, Jonathan Barnwell, … 1
R94 Discovery Miles 940 Ships in 10 - 25 working days

The first two series of the TV drama starring Matthew Macfadyen as a detective trying to maintain the law on the streets of Whitechapel in the wake of the Jack the Ripper murders. Though the immediate threat posed by the Ripper seems to have receded, H Division's Detective Inspector Edmund Reid (Macfadyen), aided by the hard-boiled Sergeant Bennett Drake (Jerome Flynn) and American forensics expert Captain Homer Jackson (Adam Rothenberg), still has his hands full trying to keep the East End streets safe. Series 1 episodes are: 'I Need Light', 'In My Protection', 'The King Came Calling', 'The Good of This City', 'The Weight of One Man's Heart', 'Tournament of Shadows', 'A Man of My Company' and 'What Use Our Work?'. Series 2 episodes are: 'Pure As the Driven', 'Am I Not Monstrous?', 'Become Man', 'Dynamite and a Woman', 'Threads of Silk and Gold', 'A Stronger Loving World', 'Our Betrayal: Part One' and 'Our Betrayal: Part Two'.

Scientific Inference - Learning from Data (Hardcover, New): Simon Vaughan Scientific Inference - Learning from Data (Hardcover, New)
Simon Vaughan
R2,140 Discovery Miles 21 400 Ships in 18 - 22 working days

Providing the knowledge and practical experience to begin analysing scientific data, this book is ideal for physical sciences students wishing to improve their data handling skills. The book focuses on explaining and developing the practice and understanding of basic statistical analysis, concentrating on a few core ideas, such as the visual display of information, modelling using the likelihood function, and simulating random data. Key concepts are developed through a combination of graphical explanations, worked examples, example computer code and case studies using real data. Students will develop an understanding of the ideas behind statistical methods and gain experience in applying them in practice. Further resources are available at www.cambridge.org/9781107607590, including data files for the case studies so students can practise analysing data, and exercises to test students' understanding.

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