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An Introduction to Statistical Concepts

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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.

Table of Contents

Contents: Preface. Introduction. Data Representation. Univariate Population Parameters and Sample Statistics. The Normal Distribution and Standard Scores. Introduction to Probability and Sample Statistics. Introduction to Hypothesis Testing: Inferences About a Single Mean. Inferences About the Difference Between Two Means. Inferences About Proportions. Inferences About Variances. Bivariate Measures of Association. Simple Linear Regression. Multiple Regression. 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.