Statistical Concepts—A First Course presents the first 10 chapters from An Introduction to Statistical Concepts, Fourth Edition. Designed for first and lower-level statistics courses, this book communicates a conceptual, intuitive understanding of statistics that does not assume extensive or recent training in mathematics and only requires a rudimentary knowledge of algebra. Covering the most basic statistical concepts, this book is designed to help readers really understand statistical concepts, in what situations they can be applied, and how to apply them to data. Specifically, the text covers basic descriptive statistics, including ways of representing data graphically, statistical measures that describe a set of data, the normal distribution and other types of standard scores, and an introduction to probability and sampling. The remainder of the text covers various inferential tests, including those involving tests of means (e.g., t tests), proportions, variances, and correlations. Providing accessible and comprehensive coverage of topics suitable for an undergraduate or graduate course in statistics, this book is an invaluable resource for students undertaking an introductory course in statistics in any number of social science and behavioral science disciplines.
Statistical concepts : a first course, presents the first ten chapters from An introduction to statistical concepts, 4th edition.
Durbin, I., & Watson, G. S. (1971). Testing for serial correlation in least squares regression, III. Biometrika, 58, 1—19. Elashoff, I. D. (1969). Analysis of covariance: A delicate instrument. American Educational Research Iournal, 6, ...
This comprehensive, flexible text is used in both one- and two-semester courses to review introductory through intermediate statistics.
The Book of R is a comprehensive, beginner-friendly guide to R, the world’s most popular programming language for statistical analysis.
The text begins with coverage of descriptive statistics such as measures of central tendency and variability, then moves on to inferential statistics.
This book offers a modern and accessible introduction to Statistical Inference, the science of inferring key information from data.
This book offers a modern and accessible introduction to Statistical Inference, the science of inferring key information from data.
Key concepts are simply stated and reintroduced and related to one another for reinforcement. Numerous examples demonstrate their relevance. This edition features more explanation to increase understanding of the concepts.
The new edition of An Introduction to Statistical Concepts is designed to help students really understand statistical concepts, the situations in which they can be used, and how to apply them to data.
... (6) part and partial correlations, (7) collinearity diagnostics, (8) Durbin-Watson, and (9) Casewise diagnostics. For this example, we apply an alpha level of .05, thus we will leave the default confidence interval percentage at 95.