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Cover of Introductory Statistics v1.0
Published: 
August 2012
Page Count: 
390
ISBN (Digital): 
978-1-4533-4487-3

Introductory Statistics

Version 1.0
By Douglas S. Shafer and Zhiyi Zhang

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  • Concise, streamlined approach.
  • Wide range of exercises for each concept to drive comprehension.
  • Large data sets available online.
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This textbook is suitable for the following courses: Introductory Statistics (one semester).

Introductory Statistics provides a concise, coherent explanation of introductory statistics topics that caters to the needs of general education students in standard one-semester introductory courses. Introductory Statistics ensures student comprehension by focusing on important core concepts and using a variety of exercises and examples to illustrate topics covered. As a result, Introductory Statistics provides a low-cost, high-quality alternative to existing options on the market.

  • About the Authors
  • Acknowledgements
  • Dedication
  • Preface

    • Chapter 1: Introduction

  • 1.1 Basic Definitions and Concepts
  • 1.2 Overview
  • 1.3 Presentation of Data

    • Chapter 2: Descriptive Statistics

  • 2.1 Three Popular Data Displays
  • 2.2 Measures of Central Location
  • 2.3 Measures of Variability
  • 2.4 Relative Position of Data
  • 2.5 The Empirical Rule and Chebyshev’s Theorem

    • Chapter 3: Basic Concepts of Probability

  • 3.1 Sample Spaces, Events, and Their Probabilities
  • 3.2 Complements, Intersections, and Unions
  • 3.3 Conditional Probability and Independent Events

    • Chapter 4: Discrete Random Variables

  • 4.1 Random Variables
  • 4.2 Probability Distributions for Discrete Random Variables
  • 4.3 The Binomial Distribution

    • Chapter 5: Continuous Random Variables

  • 5.1 Continuous Random Variables
  • 5.2 The Standard Normal Distribution
  • 5.3 Probability Computations for General Normal Random Variables
  • 5.4 Areas of Tails of Distributions

    • Chapter 6: Sampling Distributions

  • 6.1 The Mean and Standard Deviation of the Sample Mean
  • 6.2 The Sampling Distribution of the Sample Mean
  • 6.3 The Sample Proportion

    • Chapter 7: Estimation

  • 7.1 Large Sample Estimation of a Population Mean
  • 7.2 Small Sample Estimation of a Population Mean
  • 7.3 Large Sample Estimation of a Population Proportion
  • 7.4 Sample Size Considerations

    • Chapter 8: Testing Hypotheses

  • 8.1 The Elements of Hypothesis Testing
  • 8.2 Large Sample Tests for a Population Mean
  • 8.3 The Observed Significance of a Test
  • 8.4 Small Sample Tests for a Population Mean
  • 8.5 Large Sample Tests for a Population Proportion

    • Chapter 9: Two-Sample Problems

  • 9.1 Comparison of Two Population Means: Large, Independent Samples
  • 9.2 Comparison of Two Population Means: Small, Independent Samples
  • 9.3 Comparison of Two Population Means: Paired Samples
  • 9.4 Comparison of Two Population Proportions
  • 9.5 Sample Size Considerations

    • Chapter 10: Correlation and Regression

  • 10.1 Linear Relationships Between Variables
  • 10.2 The Linear Correlation Coefficient
  • 10.3 Modelling Linear Relationships with Randomness Present
  • 10.4 The Least Squares Regression Line
  • 10.5 Statistical Inferences About β1
  • 10.6 The Coefficient of Determination
  • 10.7 Estimation and Prediction
  • 10.8 A Complete Example
  • 10.9 Formula List

    • Chapter 11: Chi-Square Tests and F-Tests

  • 11.1 Chi-Square Tests for Independence
  • 11.2 Chi-Square One-Sample Goodness-of-Fit Tests
  • 11.3 F-tests for Equality of Two Variances
  • 11.4 F-Tests in One-Way ANOVA

    • Appendix A: Appendix

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    Douglas S. Shafer University of North Carolina - Charlotte

    Douglas Shafer is Professor of Mathematics at the University of North Carolina at Charlotte. In addition to his position in Charlotte, he has held visiting positions at the University of Missouri at Columbia and Montana State University and a Senior Fulbright Fellowship in Belgium. He teaches a range of mathematics courses as well as introductory statistics. In addition to journal articles and this statistics textbook, he has co-authored a graduate-level textbook in his research specialty with V.G. Romanovski (Maribor, Slovenia). He earned a Ph.D. in mathematics at the University of North Carolina at Chapel Hill.

    Zhiyi Zhang University of North Carolina - Charlotte

    Zhiyi Zhang is Professor of Mathematics at the University of North Carolina at Charlotte. In addition to his teaching and research duties at the university, he consults actively to industries and governments on a wide range of statistical issues. His research activities in statistics have been supported by the National Science Foundation, the U.S. Environmental Protection Agency, the Office of Naval Research, and the National Institute of Health. He earned a Ph.D. in statistics at Rutgers University in New Jersey.

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