1 / 8

Chapter 4 Two-Variables Analysis

Chapter 4 Two-Variables Analysis. 09/19-20/2013. Outline. Issue: How to identify the linear relationship between two variables? Relationship: • Scatter Plot is a collection of observations on an X-Y graph

carlynda-kelly
Download Presentation

Chapter 4 Two-Variables Analysis

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Chapter 4 Two-Variables Analysis 09/19-20/2013

  2. Outline • Issue: How to identify the linear relationship between two variables? • Relationship: • • Scatter Plot is a collection of observations • on an X-Y graph • • Covariance conveys the direction of the potential relationship • Correlation coefficient measures the strength of a linear relationship between two variables • Causality and predictions: • • Least squares line

  3. Scatter Plot

  4. Scatter Plot: Degree of Association

  5. Covariance • A measure of the strength of a linear relationship between two variables • While the magnitude changes with the units, its sign conveys direction only. • Positive covariance  Positive linear relationship • Negative covariance  Negative linear relationship

  6. Correlation coefficient • Unit-free and always between -1 (perfectly negative linear relationship) and +1 (perfectly positive linear relationship) • The greater the absolute value of the correlation coefficient, the stronger the linear relationship.

  7. Least squares line • A Unique line that describes the relationship between two variables, when one causes the other. It has the smallest sum of squared error!

  8. Sum of Squared Error is the observed value and is the predicted value.

More Related