Introductory Statistics: Exploring the World Through Data, Canadian Edition, 1st edition

Published by Pearson Canada (January 29, 2016) © 2017

  • Robert N. Gould University of California, Los Angeles
  • Colleen Ryan California Lutheran University
  • Jim Stallard University of Calgary
  • Michelle Boué Trent University

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  • A print edition
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  • Pearson+ eTextbook not included
  • Instant access once purchased
  • Register with a Course ID, a link from your instructor or an LMS link (Blackboard™, Canvas™, Moodle or D2L®)

Features

  • Interactive digital learning experience
  • Help when and where you need it
  • Instant feedback on assignments
  • Apps and study tools

Title overview

Exploring the World through Data

We all have access to rich and valuable data relevant in many important fields: environment, civics, social sciences, economics, health care, and entertainment. This text teaches students to learn from such data and to become cognizant of the role of the data that appear all around them.

Canadian authors Jim Stallard and Michelle Boué emphasize that learning statistics extends beyond the classroom to an essential life skill, and want Canadian students to develop a “data habit of mind.” Regardless of their math backgrounds, students will learn how to think about data and how to reason using data. With a clear, unintimidating writing style and carefully chosen pedagogy, this text makes data analysis accessible to all students.

Table of contents

  1. Introduction to Data
  2. Picturing Variation with Graphs
  3. Numerical Summaries of Centre and Variation
  4. Regression Analysis: Exploring Associations between Variables
  5. Modelling Variation with Probability
  6. Modeling Random Events: The Normal and Binomial Models
  7. Survey Sampling and Inference
  8. Hypothesis Testing for Population Proportions
  9. Inferring Population Means
  10. Associations between Categorical Variables
  11. Multiple Comparisons and Analysis of Variance
  12. Experimental Design: Controlling Variation
  13. Inference without Normality
  14. Inference for Regression

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