1 / 13

Bivariate Relationships

Bivariate Relationships. Plotting a Line. Review: Covariance. When it tends to be the case that x is greater than the mean when y is greater than the mean AND x is lower than the mean when y is lower than the mean, then there is a positive covariation. Plot showing positive covariance.

jenette-mclaughlin
Download Presentation

Bivariate Relationships

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. Bivariate Relationships Plotting a Line

  2. Review: Covariance • When it tends to be the case that x is greater than the mean when y is greater than the mean AND x is lower than the mean when y is lower than the mean, then there is a positive covariation

  3. Plot showing positive covariance Mean urban % Mean female literacy

  4. Expected value • But we may want to know more specific knowledge than that – we may want to know the expected value of y for each increased value of x • I may know the mean of everyone’s height in class • But if I know gender, then I can generate two expected values • If you remember, we are always trying to do better than the mean

  5. Substantive effect • For every 10K dollars given in humanitarian aid, there is an increase in 3K spent on weapons • For every 10K dollars given in humanitarian aid, there is a .5K increase spent on weapons • For every 10K dollars given in humanitarian aid, there is a 8K increase spent on weapons • Unit of analysis?

  6. Regression equation • y = a + bx + e • ŷ = a + bx • ŷ is also known as yhat • y is the dependent variable value • yhat is the predicted value • a is the intercept

  7. X and Y • Y X • 2 1 • 2 • 4 3 • 3 4 • 6 5 • 5 6

  8. X and Y • Y X • 2 1 • 2 • 4 3 • 3 4 • 6 5 • 5 6

  9. ŷ= a + bx ŷ = .6 + .83x

  10. ŷ= a + bx • b is slope – rise over run • a is the y intercept; constant • Standard error is the average error from the actual points to the slope • T is the ratio of the slope divided by the standard error • Beta = Pearson r in bivariate analysis

  11. Another example

  12. Life Happiness and Occupational Prestige

  13. 3.5 3 2.5 2 Are you happy? (mean = 1.9) 1.5 1 0.5 0 0 10 20 30 40 50 60 Occupational Prestige: (mean = 37) Life happiness = 3.33 - .038 Occupational Prestige

More Related