1 / 11

Pearson’s Correlation and Bivariate Regression

Pearson’s Correlation and Bivariate Regression. Lab Exercise: Chapter 9. Example Questions:. Do opposites really attract? Is there a negative correlation between the educational levels of spouses? One more year in school typically results in how much more annual income?

jeffreycruz
Download Presentation

Pearson’s Correlation and Bivariate Regression

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. Pearson’s Correlation and Bivariate Regression Lab Exercise: Chapter 9

  2. Example Questions: • Do opposites really attract? Is there a negative correlation between the educational levels of spouses? • One more year in school typically results in how much more annual income? • Schooling accounts for how much of the differences in persons’ incomes? • What annual income would we predict for someone with 16 years of schooling?

  3. Interval/Ratio Measures of Association • Pearson’s r • ranges from −1.00 to 1.00 • symmetric • Analyze | Correlate | Bivariate • pairwise and listwise deletion of missing data

  4. Bivariate Correlation

  5. Scatterplot: Do opposites attract?*Check linearity, strength, direction, and homoscedasticity

  6. Bivariate Linear Regression: Income on Schooling • Equation for a straight line • “Best-fitting” straight line

  7. Bivariate Linear Regression (cont.) • Analyze | Regression | Linear

  8. Regression Output of INCOME86 on EDUC for 1980 GSS Young Adults Answering Questions with Statistics Chapter 9

  9. Bivariate Linear Regression (cont.) • Unstandardized coefficients • Regression equation • Predicted value Ŷ: substitute value for X (16 yrs?) = $21,604.089 • Regression residual: Y - Ŷ

  10. Bivariate Linear Regression (cont.) • Multiple correlation coefficient (R) • indicates strength but not direction • Coefficient of determination (R2) • Coefficient of alienation (residual or unexplained)

  11. Bivariate Linear Regression (cont.) • Some limitations to remember • regression does not prove causality • for interval-ratio level variables • Can be used with caution (requires special interpretation) for grouped interval ratio or ordinal variables with >5 categories • linear means only linear

More Related