Statistics is of ever-increasing importance in Science and Technology and this book presents the essentials of the subject in a form suitable either as the basis of a course of lectures or to be read and/or used on its own. It assumes very little in the way of mathematical knowledge-just the ability to substitute numerically in a few simple formulae. However, some mathematical proofs are outlined or given in full to illustrate the derivation of the subject; these can be omitted without loss of understanding. The book does aim at making clear the scope and nature of those essential tests and methods that a scientist or technologist is likely to need; to this end each chapter has been divided into sections with their own subheadings and some effort has been made to make the text unambiguous (if any reader finds a misleading point anywhere I hope he will write to me about it). Also with this aim in view, the equality of probability to proportion of population is stated early, then the normal distribution and the taking of samples is discussed. This occupies the first five chapters. With the principles of these chapters understood, the student can immediately learn the significance tests of Chapter 6 and, if he needs it, the analysis of variance of Chapter 7. For some scientists this will be most of what they need. Howcver, they will be in a position to read and/or use the remaining chapters without undue difficulty.

Springer Book Archives

1 Introduction or 'What is statistics?'.- 2 The presentation of data.- 3 Probability, its meaning, real and theoretical populations.- 4 Basic properties of the normal distribution.- 5 Some properties of sampling distributions.- 6 Applications of normal sampling theory; significance tests.- 7 Normal sampling theory: test for difference between several sample means, analysis of variance, design of experiments.- 8 Normal sampling theory: estimation of 'parameters' by confidence intervals, by maximum likelihood.- 9 The binomial distribution: laws of probability, applications of the binomial distribution, the multinomial distribution.- 10 The Poisson, negative exponential, and rectangular distributions.- 11 The ?2 test for 'goodness of fit': test for 'association'.- 12 Fitting lines and curves to data, least squares method.- 13 Regression curves and lines, correlation coefficient, normal bivariate distribution.- 14 Some distribution-independent (or 'distribution-free' or 'non-parametric') tests.- 15 Note on sampling techniques and quality control.- 16 Some problems of practical origin.- Answers.