This book is mainly based on lectures given by Professor D. R. Cox and myself at Birkbeck College over a period of eight to nine years. It began as a joint venture, but pressure of other work made it necessary for Professor Cox to withdraw early on. I have throughout received much valuable advice and encouragement from Professor Cox, but of course, I am solely responsible for the text, and any errors remaining in it. The book is intended as a first course on statistical methods, and there is a liberal supply of exercises. Although the mathematical level of the book is low, I have tried to explain carefully the logical reasoning behind the use of the methods discussed. Some of the exercises which require more difficult mathematics are marked with an asterisk, and these may be omitted. In this way, I hope that the book will satisfy the needs for a course on statistical methods at a range of mathematical levels. It is essential for the reader to work through the numerical exercises, for only in this way can he grasp the full meaning and usefulness of the statistical techniques, and gain practice in the interpretation of the results. Chapters 7 and 8 discuss methods appropriate for use on ranked or discrete data, and Chapters 9-12 do not depend on these chapters. Chapters 7 and 8 may therefore be omitted, if desired.

This study manual, originally published by Houghton Mifflin Co. in 1977, provides a large number of exercises to accompany the material contained in Elementary Statistical Methods in Psychology and Education, Second Edition. This manual will be a useful tool for the beginning student of statistics.

Bridging the gap between statistics texts and SAS documentation, Elementary Statistics Using SAS is written for those who want to perform analyses to solve problems. The first section of the book explains the basics of SAS data sets and shows how to use SAS for descriptive statistics and graphs. The second section discusses fundamental statistical concepts, including normality and hypothesis testing. The remaining sections of the book show analyses for comparing two groups, comparing multiple groups, fitting regression equations, and exploring contingency tables. For each analysis, author Sandra Schlotzhauer explains assumptions, statistical approach, and SAS methods and syntax, and makes conclusions from the results. Statistical methods covered include two-sample t-tests, paired-difference t-tests, analysis of variance, multiple comparison techniques, regression, regression diagnostics, and chi-square tests. Elementary Statistics Using SAS is a thoroughly revised and updated edition of Ramon Littell and Sandra Schlotzhauer's SAS System for Elementary Statistical Analysis.

This updated edition shows how to use SAS to perform basic statistical analysis. General topics include creating a data set with SAS; summarizing data with descriptive statistics, frequency tables, and bar charts; comparing groups (t-tests, one-way ANOVA, and nonparametric analogues); performing basic linear regression (lines, curves, and two-variable models); performing simple regression diagnostics (residuals plots, studentized residuals); and creating and analyzing tables of data. Using real-life examples, this beginner's guide bridges the gap between statistics texts and SAS documentation.

Providing a much-needed bridge between elementary statistics courses and advanced research methods courses, Understanding Advanced Statistical Methods helps students grasp the fundamental assumptions and machinery behind sophisticated statistical topics, such as logistic regression, maximum likelihood, bootstrapping, nonparametrics, and Bayesian methods. The book teaches students how to properly model, think critically, and design their own studies to avoid common errors. It leads them to think differently not only about math and statistics but also about general research and the scientific method. With a focus on statistical models as producers of data, the book enables students to more easily understand the machinery of advanced statistics. It also downplays the "population" interpretation of statistical models and presents Bayesian methods before frequentist ones. Requiring no prior calculus experience, the text employs a "just-in-time" approach that introduces mathematical topics, including calculus, where needed. Formulas throughout the text are used to explain why calculus and probability are essential in statistical modeling. The authors also intuitively explain the theory and logic behind real data analysis, incorporating a range of application examples from the social, economic, biological, medical, physical, and engineering sciences. Enabling your students to answer the why behind statistical methods, this text teaches them how to successfully draw conclusions when the premises are flawed. It empowers them to use advanced statistical methods with confidence and develop their own statistical recipes. Ancillary materials are available on the book’s website.