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Statistics for Making Decisions (Paperback)
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Statistics for Making Decisions (Paperback)
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Making decisions is a ubiquitous mental activity in our private and
professional or public lives. It entails choosing one course of
action from an available shortlist of options. Statistics for
Making Decisions places decision making at the centre of
statistical inference, proposing its theory as a new paradigm for
statistical practice. The analysis in this paradigm is earnest
about prior information and the consequences of the various kinds
of errors that may be committed. Its conclusion is a course of
action tailored to the perspective of the specific client or
sponsor of the analysis. The author's intention is a wholesale
replacement of hypothesis testing, indicting it with the argument
that it has no means of incorporating the consequences of errors
which self-evidently matter to the client. The volume appeals to
the analyst who deals with the simplest statistical problems of
comparing two samples (which one has a greater mean or variance),
or deciding whether a parameter is positive or negative. It
combines highlighting the deficiencies of hypothesis testing with
promoting a principled solution based on the idea of a currency for
error, of which we want to spend as little as possible. This is
implemented by selecting the option for which the expected loss is
smallest (the Bayes rule). The price to pay is the need for a more
detailed description of the options, and eliciting and quantifying
the consequences (ramifications) of the errors. This is what our
clients do informally and often inexpertly after receiving outputs
of the analysis in an established format, such as the verdict of a
hypothesis test or an estimate and its standard error. As a
scientific discipline and profession, statistics has a potential to
do this much better and deliver to the client a more complete and
more relevant product. Nicholas T. Longford is a senior
statistician at Imperial College, London, specialising in
statistical methods for neonatal medicine. His interests include
causal analysis of observational studies, decision theory, and the
contest of modelling and design in data analysis. His longer-term
appointments in the past include Educational Testing Service,
Princeton, NJ, USA, de Montfort University, Leicester, England, and
directorship of SNTL, a statistics research and consulting company.
He is the author of over 100 journal articles and six other
monographs on a variety of topics in applied statistics.
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