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Book Review: Statistical inference: an integrated approach

Book Review: Statistical inference: an integrated approach 2 Boag JW. Maximum likelihood estimates of the proportion of patients cured by cancer therapy. Journal of the Royal Statistical Society, Series B 1949; 11:15­44. McConway KJ, Jones MC, Taylor PC 1999: Statistical modelling using GENSTAT. London: Arnold, £27.99 (PB), ISBN: 0 340 75985 2. The book serves as an introduction to the basic principles of statistical modelling, an area of statistical methodology of growing interest among a variety of disciplines in science. The authors have written a textbook for a self-contained course in statistical modelling, aiming at the student with basic education in statistics at the senior under- graduate level. Their main educational objective is the choice and application of the appropriate statistical model in situations in which a response variable depends on at least one explanatory variable. For example, the authors try to answer questions such as how well patients will respond to treatment, given their age and the severity of their disease; or how different strains of wheat compare when grown in various conditions; or how loss due to abrasion might depend on the hardness and tensile strength of samples of rubber. Emphasis is placed on using real problems and data to stimulate analyses and http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Statistical Methods in Medical Research SAGE

Book Review: Statistical inference: an integrated approach

Abstract

2 Boag JW. Maximum likelihood estimates of the proportion of patients cured by cancer therapy. Journal of the Royal Statistical Society, Series B 1949; 11:15­44. McConway KJ, Jones MC, Taylor PC 1999: Statistical modelling using GENSTAT. London: Arnold, £27.99 (PB), ISBN: 0 340 75985 2. The book serves as an introduction to the basic principles of statistical modelling, an area of statistical methodology of growing interest among a variety of disciplines in science. The authors have written a textbook for a self-contained course in statistical modelling, aiming at the student with basic education in statistics at the senior under- graduate level. Their main educational objective is the choice and application of the appropriate statistical model in situations in which a response variable depends on at least one explanatory variable. For example, the authors try to answer questions such as how well patients will respond to treatment, given their age and the severity of their disease; or how different strains of wheat compare when grown in various conditions; or how loss due to abrasion might depend on the hardness and tensile strength of samples of rubber. Emphasis is placed on using real problems and data to stimulate analyses and
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