Synopses & Reviews
Statistical Concepts, 3/e consists of the last 8 chapters of Richard Lomax’s best selling text, An Introduction to Statistical Concepts, 2/e. Designed for a second course in statistics, Lomax’s comprehensive and flexible coverage allows instructors to pick and choose those topics most appropriate for their course. It includes topics not found in competing texts such as the non-parametric and modern alternative procedures and advanced analysis of variance (ANOVA) and regression models. Its intuitive approach helps students more easily understand the concepts and interpret software results. Throughout the text, the author demonstrates how many statistical concepts relate to one another. Only the most crucial equations are included.
The new edition features: *SPSS sections throughout with input, output, and APA style write-ups using the book’s dataset;
*a CD with every example and problem dataset used in the text in SPSS format;
*more information on confidence intervals, effect size measures, power, and regression models;
*a revised sequence of the regression and ANOVA chapters for enhanced conceptual flow;
*de-emphasized computations to provide more discussion of concepts and software;
*more end of chapter problems with more realistic data and a greater emphasis on interpretation;
*many more references; and
*an Instructor’s Resource CD with all of the solutions to the problems and other teaching aids.
Statistical Concepts, 3/e covers a number of ANOVA and regression models: one-factor; multiple comparison; factorial; ANCOVA; random- and mixed-effect; hierarchical and randomized blocks; and simple and multiple regression. Realistic examples from education and the behavioral sciences illustrate the concepts. Each example includes an examination of the various procedures and necessary assumptions, tips on developing an APA style write-up, and sample SPSS output. Useful tables of assumptions and the effects of their violation are included, along with how to test assumptions in SPSS. Each chapter concludes with conceptual and computational problems, about a third of which are new to this edition. Answers to the odd-numbered problems are provided.
Intended for the second or intermediate course in statistics taught in education and/or behavioral science departments usually found at the master’s or doctoral level and occasionally at the undergraduate level. A prerequisite of descriptive statistics through t-tests is assumed.
Synopsis
Statistical Concepts, Third Edition consists of the last 8 chapters of Richard Lomax s best selling text, An Introduction to Statistical Concepts, Second Edition. Designed for a second course in statistics, Lomax s comprehensive and flexible coverage allows instructors to pick and choose those topics most appropriate for their course. It includes topics not found in competing texts such as the non-parametric and modern alternative procedures and advanced analysis of variance (ANOVA) and regression models. Its intuitive approach helps students more easily understand the concepts and interpret software results. Throughout the text, the author demonstrates how many statistical concepts relate to one another. Only the most crucial equations are included.
The new edition features:
- SPSS sections throughout with input, output, and APA style write-ups using the book s dataset
- a CD with every example and problem dataset used in the text in SPSS format
- more information on confidence intervals, effect size measures, power, and regression models
- a revised sequence of the regression and ANOVA chapters for enhanced conceptual flow
- de-emphasized computations to provide more discussion of concepts and software
- more problems with more realistic data and a greater emphasis on interpretation
- an Instructor s Resource CD with all of the solutions to the problems and other teaching aids.
Statistical Concepts, Third Edition covers a number of ANOVA and regression models: one-factor; multiple comparison; factorial; ANCOVA; random- and mixed-effect; hierarchical and randomized blocks; and simple and multiple regression. Realistic examples from education and the behavioral sciences illustrate the concepts. Each example includes an examination of the various procedures and necessary assumptions, tips on developing an APA style write-up, and sample SPSS output. Useful tables of assumptions and the effects of their violation are included, along with how to test assumptions in SPSS. Each chapter concludes with conceptual and computational problems, about a third of which are new to this edition. Answers to the odd-numbered problems are provided.
Intended for the second or intermediate course in statistics taught in education and/or behavioral science departments usually found at the master s or doctoral level and occasionally at the undergraduate level. A prerequisite of descriptive statistics through t-tests is assumed.
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Synopsis
Designed for a second course in statistics, Lomax's comprehensive and flexible coverage allows instructors to pick and choose those topics most appropriate for their course. It includes topics not found in competing texts such as the non-parametric and modern alternative procedures and advanced analysis of variance (ANOVA) and regression models. New edition features: SPSS sections throughout with input, output, and APA style write-ups using the book's dataset; a CD with every example and problem dataset used in the text in SPSS format; more information on confidence intervals, effect size measures, power, and regression models; a revised sequence of the regression and ANOVA chapters for enhanced conceptual flow; de-emphasized computations to provide more discussion of concepts and software; more end of chapter problems with more realistic data and a greater emphasis on interpretation and an Instructor's Resource CD with all of the solutions to the problems and other teaching aids.
Table of Contents
Contents: Preface. One-Factor Analysis of Variance—Fixed-Effects Model. Multiple Comparison Procedures. Factorial Analysis of Variance—Fixed-Effects Model. Introduction to Analysis of Covariance: The One-Factor Fixed-Effects Model With a Single Covariate. Random- and Mixed-Effects Analysis of Variance Models. Hierarchical and Randomized Block Analysis of Variance Models. Simple Linear Regression. Multiple Regression.