1 / 14

Correlation Coefficient : Just how strong is this relationship?

Correlation Coefficient : Just how strong is this relationship?. AP Statistics Newell. Carbs vs. Fat. Here are data on 9 different types of hamburgers at McDonalds. Carbs vs. Fat. Are we looking for an explanatory-response relationship or just a relationship here?

salene
Télécharger la présentation

Correlation Coefficient : Just how strong is this relationship?

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. Correlation Coefficient: Just how strong is this relationship? AP Statistics Newell

  2. Carbs vs. Fat Here are data on 9 different types of hamburgers at McDonalds.

  3. Carbs vs. Fat • Are we looking for an explanatory-response relationship or just a relationship here? • What relationship do you expect? • Enter carbs into L1 and fat into L2. Create the scatterplot. • Describe the strength, form, and direction of the scatterplot. Are there any outliers?

  4. Carbs vs. Fat

  5. Correlation • A correlation is a LINEAR relationship. • The correlation coefficient is a number that summarizes how strong the LINEARrelationship is as well as the direction of that LINEAR relationship. • Correlation = LINEAR relationship

  6. Fun facts about the correlation coefficient • A measure of the strength and direction of a LINEAR relationship • r or R (as a statistic, as a parameter use r or rho) • -1 ≤ r ≤ 1 • Negative r = negatively sloping line • Positive r = positively sloping line • r = 1 or -1 perfect linear relationship • r = 0  no linear relationship • r has no units… we’ll see why in a minute

  7. Examples of r

  8. Carbs vs. Fat • Is the correlation coefficient positive or negative here? • Is the correlation coefficient closer to 1 or to 0? • Let’s find this on our calculators…

  9. Calculator Instructions • Before we begin, turn diagnostics on 2nd CATALOG  DiagnosticOn  ENTER • Enter data into two lists • Go to: STAT  CALC  8: LinReg(a+bx)  ENTER • Enter your two lists, separated by a comma. The explanatory variable should be first.

  10. Examples… Match R to each picture • r = -.9668 • r = .8621 • r = -.8984 • r = .1990

  11. What happens to r when the data changes? • Add 20 to each explanatory value Put cursor on name L3. 2nd 1  + 20  Enter • Find the correlation between L3 and L2 (L3 is the explanatory variable)

  12. What happens to r when the data changes? • Multiply each explanatory value by 5 Put cursor on name L3. 2nd 1  *5  Enter • Find the correlation between L3 and L2 (L3 is the explanatory variable)

  13. What happens to r when the data changes? • Find the correlation between L1 and L2 where L2 is now the explanatory variable.

  14. What happens to r when the data changes? R does NOT change if • You change one variable’s values by adding or multiplying the same number to each value • You switch the explanatory and response variables

More Related