Applied Statistics I: Basic Bivariate Techniques has been created from the first half of Rebecca M. Warner's popular Applied Statistics: From Bivariate Through Multivariate Techniques. The author's contemporary approach differs from some of the well-worn texts in the market, and reflects current thinking in the field. It spends less time on statistical significance testing, and moves in the direction of the "new statistics" by focusing more on confidence intervals and effect size. Instructors of upper undergraduate or beginning graduate level courses will find that the greater focus on basic concepts such as partition of variance and effect size is more useful to students, particularly as preparation for more advanced courses. Spending less time on statistical significance testing allows for more time to be devoted to more interesting and useful statistics that students will see in journal articles (such as correlation and regression). This introductory statistics text includes examples in SPSS, together with datasets on an accompanying website. A companion study guide reproducing the exercises and examples in R will also be available.

Rebecca M. Warner's Applied Statistics: From Bivariate Through Multivariate Techniques, Second Edition provides a clear introduction to widely used topics in bivariate and multivariate statistics, including multiple regression, discriminant analysis, MANOVA, factor analysis, and binary logistic regression. The approach is applied and does not require formal mathematics; equations are accompanied by verbal explanations. Students are asked to think about the meaning of equations. Each chapter presents a complete empirical research example to illustrate the application of a specific method. Although SPSS examples are used throughout the book, the conceptual material will be helpful for users of different programs. Each chapter has a glossary and comprehension questions.

Applied Statistics I: Basic Bivariate Techniques spends less time on statistical significance testing, and moves in the direction of the "new statistics" by focusing more on confidence intervals and effect size. Instructors of upper undergraduate or beginning graduate level courses will find that the greater focus on basic concepts such as partition of variance and effect size is more useful to students, particularly as preparation for more advanced courses. Spending less time on statistical significance testing allows for more time to be devoted to more interesting and useful statistics that students will see in journal articles (such as correlation and regression). The author includes examples in SPSS, together with datasets on an accompanying website. A companion study guide reproducing the exercises and examples in R will also be available.

Rebecca M. Warner's bestselling Applied Statistics: From Bivariate Through Multivariate Techniques has been split into two volumes for ease of use over a two-course sequence. This new multivariate statistics text, Applied Statistics II: Multivariable and Multivariate Techniques, Third Edition is based on chapters from the second half of original book, but with much additional material. This text now provides a distinctive bridge between earlier courses and advanced topics through extensive discussion of statistical control (adding a third variable), a new chapter on the "new statistics", a new chapter on outliers and missing values, and a final chapter that provides an introduction to structural equation modeling. This text provides a solid introduction to concepts such as statistical control, mediation, moderation, and path modeling necessary to students taking intermediate and advanced statistics courses across the social sciences. Examples are provided in SPSS with datasets available on an accompanying website. A companion study guide reproducing the exercises and examples in R will also be available.

This book provides a thorough introduction to methods for detecting and describing cyclic patterns in time-series data. It is written both for researchers and students new to the area and for those who have already collected time-series data but wish to learn new ways of understanding and presenting them. Facilitating the interpretation of observations of behavior, physiology, mood, perceptual threshold, social indicator variables, and other responses, the book focuses on practical applications and requires much less mathematical background than most comparable texts. Using real data sets and currently available software (SPSS for Windows), the author employs extensive examples to clarify key concepts. Topics covered include research design issues, preliminary data screening, identification and description of cycles, summary of results across time series, and assessment of relations between time series. Also considered are theoretical questions, problems of interpretation, and potential sources of artifact.

Rebecca M. Warner's bestselling Applied Statistics: From Bivariate Through Multivariate Techniques has been split into two volumes for ease of use over a two-course sequence. This new multivariate statistics text, Applied Statistics II: Multivariable and Multivariate Techniques, Third Edition is based on chapters from the second half of original book, but with additional material. The book provides a distinctive bridge between earlier courses and advanced topics through extensive discussion of statistical control (adding a third variable), a new chapter on the "new statistics", a new chapter on outliers and missing values, and a final chapter that provides an introduction to structural equation modeling. Examples are provided in SPSS with datasets available on an accompanying website. A companion study guide reproducing the exercises and examples in R will also be available.

We offer these texts bundled together at a discount for your students. Warner: Applied Statistics, 2e Applied Statistics: From Bivariate Through Multivariate Techniques, Second Edition, provides a clear introduction to widely used topics in bivariate and multivariate statistics, including multiple regression, discriminant analysis, MANOVA, factor analysis, and binary logistic regression. Author Rebecca M. Warner uses an applied approach that does not require formal mathematics; equations are accompanied by verbal explanations, and students are asked to think about the meaning of the equations. Each chapter presents a complete empirical research example to illustrate the application of a specific method, along with a glossary and comprehension questions to help students master each concept. SPSS examples are used throughout the book; however, the conceptual material will be helpful for users of different programs. IBM SPSS Statistics Student Version 22.0 IBM® SPSS® Student Version is a single-user license that provides affordable access to statistical analysis, modeling and survey research tools that can support your college coursework and enable you to develop the advanced analytical skills employers seek. This program is intended only for academic persons, including students, professors, and other academic professionals for use solely in connection with a course offering. It may only be used in combination with academic textbooks for the sole purpose of teaching and learning analytics. Use of this program is limited to course work as defined by the course syllabus. Any other use of the program is prohibited. This version of the program: is for a 12-month period, beginning on the date of customer installation; is limited to two installations; does not allow for add on SPSS modules; is limited to 1500 cases and 50 variables; does not allow for SPSS Command Syntax to be used; and does not allow customers to merge or aggregate files. Please contact your Sales Representative for more information.