Applied Statistics: Principles and Examples

There are many books which set out the more commonly used statistical methods in a form suitable for applications. There are also widely available computer packages for implementing these techniques in a relatively painless way. We have in the present book concentrated not so much on the techniques themselves but rather on the general issues involved in their fruitful application. The book is in two parts, the first dealing with general ideas and principles and the second with a range of examples, all, however, involving fairly small sets of data and fairly standard techniques. Readers who have experience of the application of statistical methods may want to concentrate on the first part, using the second part, and better still their own experience, to illuminate and criticize the general ideas. If the book is used by students with little or no experience of applications, a selection of examples from the second part of the book should be studied first, any general principles being introduced at a later stage when at least some background for their understanding is available.

Table of Contents:
I Principles.- 1. Nature and objectives of statistical analysis.- 1.1 Introduction.- 1.2 Data quality.- 1.3 Data structure and quantity.- 1.4 Phases of analysis.- 1.5 Styles of analysis.- 1.6 Computational and numerical analytical aspects.- 1.7 Response and explanatory variables.- 1.8 Types of investigation.- 1.9 Purposes of investigation.- 2. Some general concepts.- 2.1 Types of observation.- 2.2 Descriptive and probabilistic methods.- 2.3 Some aspects of probability models.- 3. Some strategical aspects.- 3.1 Introduction.- 3.2 Incorporation of related data and external information.- 3.3 Role of special stochastic models.- 3.4 Achievement of economical and consistent description.- 3.5 Attitudes to assumptions.- 3.6 Depth and complexity of analysis appropriate.- 3.7 Analysis in the light of the data.- 4. Some types of statistical procedure.- 4.1 Introduction.- 4.2 Formulation of models: generalities.- 4.3 Formulation of models: systematic component.- 4.4 Formulation of models: random component.- 4.5 Calculation of summarizing quantities.- 4.6 Graphical analysis.- 4.7 Significance tests.- 4.8 Interval estimation.- 4.9 Decision procedures.- 4.10 Examination of the adequacy of models.- 4.11 Parameters and parameterization.- 4.12 Transformations.- 4.13 Interaction.- II Examples.- A Admissions to intensive care unit.- B Intervals between adjacent births.- C Statistical aspects of literary style.- D Temperature distribution in a chemical reactor.- E A 'before and after' study of blood pressure.- F Comparison of industrial processes in the presence of trend.- G Cost of construction of nuclear power plants.- H Effect of process and purity index on fault occurrence.- I Growth of bones from chick embryos.- J Factorial experiment on cycles to failure of worsted yarn.- K Factorial experiment on diets for chickens.- L Binary preference data for detergent use.- M Fertilizer experiment on growth of cauliflowers.- N Subjective preference data on soap pads.- O Atomic weight of iodine.- P Multifactor experiment on a nutritive medium.- Q Strength of cotton yarn.- R Biochemical experiment on the blood of mice.- S Voltage regulator performance.- T Intervals between the failure of air-conditioning equipment in aircraft.- U Survival times of leukemia patients.- V A retrospective study with binary data.- W Housing and associated factors.- X Educational plans of Wisconsin schoolboys.- Summary of examples.- Further sets of data.- References.- Author index.