Data Driven Statistical Methods

✍ Scribed by Peter Sprent (Author)

• Publisher: Routledge
• Year: 1997
• Leaves: 419
• Edition: 1
• Category: Library

✦ Synopsis

Calculations once prohibitively time-consuming can be completed in microseconds by modern computers. This has resulted in dramatic shifts in emphasis in applied statistics. Not only has it freed us from an obsession with the 5% and 1% significance levels imposed by conventional tables but many exact estimation procedures based on randomization tests are now as easy to carry out as approximations based on normal distribution theory. In a wider context it has facilitated the everyday use of tools such as the bootstrap and robust estimation methods as well as diagnostic tests for pinpointing or for adjusting possible aberrations or contamination that may otherwise be virtually undetectable in complex data sets. Data Driven Statistical Methods provides an insight into modern developments in statistical methodology using examples that highlight connections between these techniques as well as their relationship to other established approaches. Illustration by simple numerical examples takes priority over abstract theory. Examples and exercises are selected from many fields ranging from studies of literary style to analysis of survival data from clinical files, from psychological tests to interpretation of evidence in legal cases. Users are encouraged to apply the methods to their own or other data sets relevant to their fields of interest. The book will appeal both to lecturers giving undergraduate mainstream or service courses in statistics and to newly-practising statisticians or others concerned with data interpretation in any discipline who want to make the best use of modern statistical computer software.

✦ Table of Contents

Data-Driven Inference. The Bootstrap. Outliers, Contamination, and Robustness. Location Tests for Two Independent Samples. More One- and Two-Sample Tests. Three or More Independent Samples. Designed Experiments. Correlation and Concordance. Bivariate Regression. Other Regression Models and Diagnostics. Categorical Data Analysis. Further Categorical Data Analysis. Data-Driven or Model-Driven? References. Index.