An imaginative introduction to statistics, reorienting the course towards an understanding of statistical thinking and its meaning and use in daily life and work. Gudmund Iversen and Mary Gergen bring their years of experience and insight into teaching the subject, incorporating such innovations and insights as a sustained emphasis on the process of statistical analysis and what statistics can and cannot do as well as careful exposition of the ideas of developing statistical and graphical literacy. In the spirit of contemporary pedagogy and by using technology, the authors break down the traditional barriers of statistical formulas and lengthy computations encountered by students without strong quantitative skills. Further, formulas are grouped at the end of each chapter along with related problems, and, with only algebra as a prerequisite, the book is ideal for students in the liberal arts and the behavioural and social sciences.

Aimed at readers who may be more familiar with statistics than calculus and mathematics, this carefully written volume gives an overview of the central ideas in calculus. Author Gudmund R. Iversen shows examples of how calculus is used to translate many real-world phenomena into mathematical functions. Beginning with an explanation of the two major parts of calculus, differentiation and integration, Iversen illustrates how calculus is used in statistics to distinguish between the mean and the median, to derive the least squares formulas for regression coefficients, to find values of parameters from theoretical distributions, and to find a statistical p value when we using one of the continuous test variables like the t variable. Social scientists who either never took a calculus course or who want to "brush up" on their understanding of calculus will find this book a necessity.

Contextual analysis, the study of the role of the group context on actions and attitudes of individuals, is a useful technique in the study of education, neighborhoods, census tracts, election districts, and the family. However, the effective use of contextual analysis has involved overcoming a number of issues, such as group boundaries, the mobility of the individuals within a group, overlapping groups, missing individual data, and the choice of statistical models. Contextual Analysis offers researchers a guide for selecting the best model to use. Written in a straightforward style, the book explores such topics as contextual analysis with absolute effects, with relative effects, and the choice between regression coefficients as fixed parameters or as random variables.

The authors have improved on their widely used first edition by providing updated examples, adding material on how to do ANOVA using statistical packages for microcomputers, linking the use of ANOVA to regression analysis, and enchancing their discussion on using ANOVA for experimentally gathered data.

Statisticians now generally acknowledge the theorectical importance of Bayesian inference, if not its practical validity. According to Gudmund R. Iversen, one reason for the lag in applications is that empirical researchers have lacked a grounding in the methodology. His volume provides this introduction and serves as a companion to #4, Tests of Significance.