1. More about Correlations

2. Spearman Rank order correlation Does the same type of analysis as a Pearson r but with data that only represents order. –Ordinal data represents highest to lowest but without any indication of distance between ranks.

3. Spearman correlation cont. –With a Spearmen rank order correlation both variables (x and y) are ranked. –The correlation then determines the relationship between rankings –Easier calculation but less powerful as a statistical test.

4. Correlation: Relationship between two variables. Regression: What would you predict about the dependent variable, given the independent variable(s). Multiple Regression

5. Since you can have several variables: One or more are designated as dependent while all others are independent. –The DV is identified based on prior knowledge or expectations. –The IV’s can be continuous measurements (different than an ANOVA) –This analysis still does not show causation.

6. Relationship is defined by : –Where: a is the intercept Each x is an IV Each B is a regression coefficient for a particular IV

7. Looking at the output Correlation overall is evaluated with F.

8. DV IV1 IV2 IV3

9. R - Multiple correlation coefficient is the measure of correlation between the predicted y and the obtained y. R 2 - the portion of the variation of the DV that is predictable from the regression equation.

10. Output cont. Each IV can be evaluated based on a t test based on the regression coefficients.

11. If: cancer deaths % of smokers and % of the population over 75 are used to predict median health care costs…

14. If: # of hospitals and # of MD’s Are used to predict median health care costs…