Skip to ContentGo to accessibility pageKeyboard shortcuts menu
OpenStax Logo
Statistics

Introduction

StatisticsIntroduction
Search for key terms or text.
This is a photo of a car mechanic’s shop. There are three United States Postal Services trucks being serviced, and one not being serviced.
Figure 12.1 Linear regression and correlation can help you determine whether an auto mechanic’s salary is related to his work experience. (credit: Joshua Rothhaas)

Chapter Objectives

By the end of this chapter, the student should be able to do the following:

  • Discuss basic ideas of linear regression and correlation
  • Create and interpret a line of best fit
  • Calculate and interpret the correlation coefficient
  • Calculate and interpret outliers

Professionals often want to know how two or more numeric variables are related. For example, is there a relationship between the grade on the second math exam a student takes and the grade on the final exam? If there is a relationship, what is the relationship, and how strong is it?

In another example, your income may be determined by your education, your profession, your years of experience, and your ability. The amount you pay a repair person for labor is often determined by an initial amount plus an hourly fee.

The type of data described in the examples is bivariate data—bi—for two variables. In reality, statisticians use multivariate data, meaning many variables.

In this chapter, you will study the simplest form of regression—linear regression—with one independent variable (x). This involves data that fit a line in two dimensions. You will also study correlation, which measures the strength of a relationship.

PreviousNext
Order a print copy
Citation/Attribution

This book may not be used in the training of large language models or otherwise be ingested into large language models or generative AI offerings without OpenStax's permission.

Want to cite, share, or modify this book? This book uses the Creative Commons Attribution License and you must attribute Texas Education Agency (TEA). The original material is available at: https://www.texasgateway.org/book/tea-statistics . Changes were made to the original material, including updates to art, structure, and other content updates.

Attribution information
  • If you are redistributing all or part of this book in a print format, then you must include on every physical page the following attribution:
    Access for free at https://openstax.org/books/statistics/pages/1-introduction
  • If you are redistributing all or part of this book in a digital format, then you must include on every digital page view the following attribution:
    Access for free at https://openstax.org/books/statistics/pages/1-introduction
Citation information

© Oct 10, 2024 Texas Education Agency (TEA). The OpenStax name, OpenStax logo, OpenStax book covers, OpenStax CNX name, and OpenStax CNX logo are not subject to the Creative Commons license and may not be reproduced without the prior and express written consent of Rice University.