This broad text provides a complete overview of most standard statistical methods, including multiple regression, analysis of variance, experimental design, and sampling techniques. Assuming a background of only two years of high school algebra, this book teaches intelligent data analysis and covers the principles of good data collection. * Provides a complete discussion of analysis of data including estimation, diagnostics, and remedial actions * Examples contain graphical illustration for ease of interpretation * Intended for use with almost any statistical software * Examples are worked to a logical conclusion, including interpretation of results * A complete Instructor's Manual is available to adopters

Data on water quality and other environmental issues are being collected at an ever-increasing rate. In the past, however, the techniques used by scientists to interpret this data have not progressed as quickly. This is a book of modern statistical methods for analysis of practical problems in water quality and water resources. The last fifteen years have seen major advances in the fields of exploratory data analysis (EDA) and robust statistical methods. The 'real-life' characteristics of environmental data tend to drive analysis towards the use of these methods. These advances are presented in a practical and relevant format. Alternate methods are compared, highlighting the strengths and weaknesses of each as applied to environmental data. Techniques for trend analysis and dealing with water below the detection limit are topics covered, which are of great interest to consultants in water-quality and hydrology, scientists in state, provincial and federal water resources, and geological survey agencies. The practising water resources scientist will find the worked examples using actual field data from case studies of environmental problems, of real value. Exercises at the end of each chapter enable the mechanics of the methodological process to be fully understood, with data sets included on diskette for easy use. The result is a book that is both up-to-date and immediately relevant to ongoing work in the environmental and water sciences.

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.

Statistical Methods: An Introduction to Basic Statistical Concepts and Analysis, Second Edition is a textbook designed for students with no prior training in statistics. It provides a solid background of the core statistical concepts taught in most introductory statistics textbooks. Mathematical proofs are deemphasized in favor of careful explanations of statistical constructs. The text begins with coverage of descriptive statistics such as measures of central tendency and variability, then moves on to inferential statistics. Transitional chapters on z-scores, probability, and sampling distributions pave the way to understanding the logic of hypothesis testing and the inferential tests that follow. Hypothesis testing is taught through a four-step process. These same four steps are used throughout the text for the other statistical tests presented including t tests, one- and two-way ANOVAs, chi-square, and correlation. A chapter on nonparametric tests is also provided as an alternative when the requirements cannot be met for parametric tests. Because the same logical framework and sequential steps are used throughout the text, a consistency is provided that allows students to gradually master the concepts. Their learning is enhanced further with the inclusion of "thought questions" and practice problems integrated throughout the chapters. New to the second edition: Chapters on factorial analysis of variance and non-parametric techniques for all data Additional and updated chapter exercises for students to test and demonstrate their learning Full instructor resources: test bank questions, Powerpoint slides, and an Instructor Manual

Important text offers lucid explanation of how to regulate variables and maintain control over statistics in order to achieve quality control over manufactured products, crops and data. First inexpensive paperback edition.

Quantitative and Statistical Research Methods This user-friendly textbook teaches students to understand and apply procedural steps in completing quantitative studies. It explains statistics while progressing through the steps of the hypothesis-testing process from hypothesis to results. The research problems used in the book reflect statistical applications related to interesting and important topics. In addition, the book provides a Research Analysis and Interpretation Guide to help students analyze research articles. Designed as a hands-on resource, each chapter covers a single research problem and offers directions for implementing the research method from start to finish. Readers will learn how to: Pinpoint research questions and hypotheses Identify, classify, and operationally define the study variables Choose appropriate research designs Conduct power analysis Select an appropriate statistic for the problem Use a data set Conduct data screening and analyses using SPSS Interpret the statistics Write the results related to the problem Quantitative and Statistical Research Methods allows students to immediately, independently, and successfully apply quantitative methods to their own research projects.

Conventional statistical methods have a very serious flaw. They routinely miss differences among groups or associations among variables that are detected by more modern techniques, even under very small departures from normality. Hundreds of journal articles have described the reasons standard techniques can be unsatisfactory, but simple, intuitive explanations are generally unavailable. Situations arise where even highly nonsignificant results become significant when analyzed with more modern methods. Without assuming the reader has any prior training in statistics, Part I of this book describes basic statistical principles from a point of view that makes their shortcomings intuitive and easy to understand. The emphasis is on verbal and graphical descriptions of concepts. Part II describes modern methods that address the problems covered in Part I. Using data from actual studies, many examples are included to illustrate the practical problems with conventional procedures and how more modern methods can make a substantial difference in the conclusions reached in many areas of statistical research. The second edition of this book includes a number of advances and insights that have occurred since the first edition appeared. Included are new results relevant to medians, regression, measures of association, strategies for comparing dependent groups, methods for dealing with heteroscedasticity, and measures of effect size.