1 / 20

Regression Lines

Regression Lines. Today’s Aim: To learn the method for calculating the most accurate Line of Best Fit for a set of data. Make a Scatterplot of the following data:. Lets guess where the Line of Best Fit should go.

ludlow
Télécharger la présentation

Regression Lines

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. Regression Lines

  2. Today’s Aim:To learn the method for calculating the most accurate Line of Best Fit for a set of data

  3. Make a Scatterplot of the following data:

  4. Lets guess where the Line of Best Fit should go

  5. Now we want to measure the distance between the actual Y values for each point and the predicted Y value on our possible Line of Best Fit

  6. Now, lets try with a different line…

  7. We can also measure with numbers the vertical distances between the Scatterplot points and the Line of Best Fit

  8. Actual y values: Predicted y values: Difference in y values: 90 1.8 88.2 81 80 90 79.1 .9 3.6 1.8 1.8 83.6 6.3 88.2 3.6 83.6 .9 3.6 81 .9 80 79.1

  9. For the first possible Line of Best Fit, the sum of the vertical distances (errors) was 6.3

  10. 90 4.8 4.8 85.2 8.2 3 83 .4 3 81 79.6 .4 80

  11. The sum of the vertical distances (errors) on the second possible line was 8.2.

  12. The correct Line of Best Fit is called a Regression Line.

  13. A Regression Line is the line that makes the sum of the squares of the vertical distances (errors) of the data points from the line as small as possible.

  14. To Calculate the Error: Error = actualy value - predictedy value Note: If the predicted value is larger than the actual value, the error will be a negative number. This is why we square the errors - to turn them into positive numbers.

  15. For example… SUM: 6.35 SUM: .78

More Related