Skip to content Skip to navigation

Connexions

You are here: Home » Content » Linear Regression and Correlation: Prediction

Navigation

Content Actions
  • Download module PDF
  • Add to ...
    Add the module to:
    • My Favorites
    • A lens
    • An external social bookmarking service
    • My Favorites (What is 'My Favorites'?)
      'My Favorites' is a special kind of lens which you can use to bookmark modules and collections directly in Connexions. 'My Favorites' can only be seen by you, and collections saved in 'My Favorites' can remember the last module you were on. You need a Connexions account to use 'My Favorites'.
    • A lens (What is a lens?)

      Definition of a lens

      Lenses

      A lens is a custom view of Connexions content. You can think of it as a fancy kind of list that will let you see Connexions through the eyes of organizations and people you trust.

      What is in a lens?

      Lens makers point to Connexions materials (modules and collections), creating a guide that includes their own comments and descriptive tags about the content.

      Who can create a lens?

      Any individual Connexions member, a community, or a respected organization.

    • External bookmarks
  • E-mail the authors

Lenses

What is a lens?

Definition of a lens

Lenses

A lens is a custom view of Connexions content. You can think of it as a fancy kind of list that will let you see Connexions through the eyes of organizations and people you trust.

What is in a lens?

Lens makers point to Connexions materials (modules and collections), creating a guide that includes their own comments and descriptive tags about the content.

Who can create a lens?

Any individual Connexions member, a community, or a respected organization.

This content is ...

In these lenses

  • Bio 502 at CSUDH

    This module is included inLens: Bio 502
    By: Terrence McGlynnAs a part of collection:"Collaborative Statistics"

    Comments:

    "This is the course textbook for Biology 502 at CSU Dominguez Hills"

    Click the "Bio 502 at CSUDH" link to see all content selected in this lens.

Recently Viewed

This feature requires Javascript to be enabled.

Tags

(What is a tag?)

These tags come from the endorsement, affiliation, and other lenses that include this content.

Linear Regression and Correlation: Prediction

Module by: Dr. Barbara Illowsky, Susan Dean

Summary: This module provides an overview of Linear Regression and Correlation: Prediction Up one level as a part of Collaborative Statistics collection (col10522) by Barbara Illowsky and Susan Dean.

The exam scores (xx-values) range from 65 to 75. Suppose you want to know the final exam score of statistics students who received 73 on the third exam. Since 73 is between the xx-values 65 and 75, substitute x=73x=73 into the equation. Then:

y ^ = -173.51 + 4.83 ( 73 ) = 179.08 y ^ =-173.51+4.83(73)=179.08 (1)

We predict that a statistics student who receives a 73 on the third exam will receive 179.08 on the final exam. Remember, do not use the regression equation to predict values outside the domain of xx.

Example 1

Recall the third exam/final exam example.

Problem 1

What would you predict the final exam score to be for a student who scored a 66 on the third exam?

Solution 1

145.27

Problem 2

What would you predict the final exam score to be for a student who scored a 78 on the third exam?

Solution 2

78 is outside of the domain of x values (independent variables), so you cannot reliably predict the final exam score for this student.

Comments, questions, feedback, criticisms?

Send feedback