Linear Regression

The text is aimed at final-year undergraduate students or those at the graduate level doing econometrics for the first time. It is an introductory course in the theory and practice of classical and modern econometric methods. A proper study of the material will allow the reader to understand the scope and limitations of classical and modern econometric techniques; read, write and properly interpret articles and reports of an applied econometric nature; and be in a position to build upon the elements of econometric theory and practice introduced in the book.

Although some basic knowledge of matrix algebra and elementary statistical theory will be assumed, much of it is covered in the text. All the main theoretical concepts are illustrated with the use of econometric software, mainly Eveiws.

This book provides comprehensive coverage so that it can be used in a single- or two-course sequence in statistics. It provides greater flexibility because it contains many topics not dealt with in other introductory texts. Its conceptual, intuitive approach allows for concepts to be easily stated and related to real-life examples. Throughout the text the author demonstrates how many statistical concepts can be related to one another. Unlike other texts, this book includes the following topics: * skewness and kurtosis measures; * inferences about two dependent proportions and two independent means with unequal variances; * homogeneity of variance tests; * layout of the data in ANOVA models; * the ANOVA linear model; * a wide variety of multiple comparison procedures; * significance tests in multiple linear regression; and * extensive discussion of assumptions and how to deal with assumption violations. Numerous tables and figures help illustrate concepts and present examples within the text. An extensive bibliography is included. A number of pedagogical devices are included to increase the reader's conceptual understanding of statistics: chapter outlines; list of key concepts for each chapter; chapter objectives; numerous realistic examples; summary tables of statistical assumptions; extensive references; and end of chapter conceptual and computational problems. An instructor's manual is available containing answers to all of the problems, as well as a collection of statistical humor designed to be an instructional aid. This book is intended for introductory statistics courses for students in education and behavioral sciences.

Written as a supplemental text for an introductory or intermediate statistics course, this book is organized along the lines of many popular statistics texts. The chapters provide a good conceptual understanding of basic statistics and include exercises that use S-PLUS simulation programs. Each chapter lists a set of objectives and a summary. The book offers a rich insight into how probability has shaped statistical procedures in the behavioral sciences, as well as a brief history behind the creation of various statistics. Computational skills are kept to a minimum by including S-PLUS programs that run the exercises in the chapters. Students are not required to master the writing of S-PLUS programs, but explanations of how the programs work and program output are included in each chapter. S-PLUS is an advanced statistical package that has an extensive library of functions, which offer flexibility in writing customized routines. The S-PLUS functions provide the capability of programming object and dialog windows, which are commonly used in Windows software applications. The S-PLUS program also contains pull-down menus for the statistical analysis of data.

The authors of this unique text found that while most students can "crunch" the numbers quite easily and accurately with a calculator or computer, many have trouble seeing the "big picture" or seeing how research questions and design influence data analysis. As a result, the authors developed a semantically consistent framework that integrates traditional research approaches (experimental, quasi-experimental, comparative) into three basic kinds of research questions (difference, associational, and descriptive), which, in turn, lead to three kinds or groups of statistics with the same names. This text: *helps students become good consumers of research by demonstrating how to analyze and evaluate research articles; *offers a number of summarizing diagrams and tables that clarify confusing or difficult to learn topics; *points out the value of qualitative research and how it should lead quantitative researchers to be more flexible; *divides all quantitative research questions into five logically consistent categories that help students select appropriate statistics and understand their cause and effect; and *classifies design into three major types: between groups, within subjects, and mixed groups and shows that, although these three types use the same general type of statistics (e.g., ANOVA), the specific statistics in between-groups design are different from those in within-subjects and mixed groups.

This book is for students taking either a first-year graduate statistics course or an advanced undergraduate statistics course in Psychology. Enough introductory statistics is briefly reviewed to bring everyone up to speed. The book is highly user-friendly without sacrificing rigor, not only in anticipating students' questions, but also in paying attention to the introduction of new methods and notation. In addition, many topics given only casual or superficial treatment are elaborated here, such as: the nature of interaction and its interpretation, in terms of theory and response scale transformations; generalized forms of analysis of covariance; extensive coverage of multiple comparison methods; coverage of nonorthogonal designs; and discussion of functional measurement. The text is structured for reading in multiple passes of increasing depth; for the student who desires deeper understanding, there are optional sections; for the student who is or becomes proficient in matrix algebra, there arestill deeper optional sections. The book is also equipped with an excellent set of class-tested exercises and answers.

Built around a problem solving theme, this book extends the intermediate and advanced student's expertise to more challenging situations that involve applying statistical methods to real-world problems. Data relevant to these problems are collected and analyzed to provide useful answers. Building on its central problem-solving theme, a large number of data sets arising from real problems are contained in the text and in the exercises provided at the end of each chapter. Answers, or hints to providing answers, are provided in an appendix. Concentrating largely on the established SPSS and the newer S-Plus statistical packages, the author provides a short, end-of-chapter section entitled Computer Hints that helps the student undertake the analyses reported in the chapter using these statistical packages.

This authoritative guide to the use of quantitative methods is designed to be used as the basic text for graduate courses, and is also suitable for upper-level students. Making History Count is written by two senior economic historians with considerable international teaching experience. The text is clearly illustrated with numerous tables, graphs and diagrams, leading the student through the various key topics. It is supported by five specific historical data-sets, available electronically in downloadable and manipulable form.