Political Science 30: Political Inquiry

1. Linear Regression II Political Science 30:Political Inquiry

2. Linear Regression II: Making Sense of Regression Results • Interpreting SPSS regression output • Coefficients for independent variables • Fit of the regression: R Square • Statistical significance • How to reject the null hypothesis • Multivariate regressions • College graduation rates • Ethnicity and voting

3. Linear Regression: Review • Want to draw a line that best represents the relationship between the IV (X) and DV (Y). • Y = a + b*X • Allows us to predict DV given value of IV • Regression finds the values for a and b that minimizes the distance between the points and the line • Technically, a and b are population parameters. We only get to calculate sample statistics, a-hat and b-hat.

4. Interpreting SPSS regression output Slope or “coefficient” How tight is the fit? Y-intercept or “constant”

5. Interpreting SPSS regression output • An SPSS regression output includes two key tables for interpreting your results: • A “Coefficients” table that contains the y-intercept (or “constant”) of the regression, a coefficient for every independent variable, and the standard error of that coefficient. • A “Model Summary” table that gives you information on the fit of your regression.

6. Interpreting SPSS regression output: Coefficients In this class, we willONLY LOOK AT UNSTANDARDIZED COEFFICIENTS! • The y-intercept is 4.2% with a standard error of 7.0% • The coefficient for SAT Scores is 0.059%, with a standard error of 0.007%.

7. Interpreting SPSS regression output: Coefficients Est. Graduation Rate = 4.2 + 0.059 * Average SAT Score

8. Interpreting SPSS regression output: Coefficients • The y-intercept or constant is the predicted value of the dependent variable when the independent variable takes on the value of zero. • This basic model predicts that when a college admits a class of students who averaged zero on their SAT, 4.2% of them will graduate. • The constant is not the most helpful statistic.

9. Interpreting SPSS regression output: Coefficients • The coefficient of an independent variable is the predicted change in the dependent variable that results from a one unit increase in the independent variable. • A college with students whose SAT scores are one point higher on average will have a graduation rate that is 0.059% higher. • Increasing SAT scores by 200 points leads to a (200)(0.059%) = 11.8% rise in graduation rates

10. Interpreting SPSS regression output: Fit of the Regression The R Square measures how closely a regression line fits the data in a scatterplot. • It can range from zero (no explanatory power) to one (perfect prediction). • An R Square of 0.345 means that differences in SAT scores can explain 35% of the variation in college graduation rates. Key sentence for your homework!

11. R Square Examples

12. Statistical Significance • What would the null hypothesis look like in a scatterplot? • If the independent variable has no effect on the dependent variable, the scatterplot should look random, the regression line should be flat, and its slope should be zero. • Null hypothesis: The regression coefficient (b) for an independent variable equals zero. • Can we reject null b=0 based on our estimate of b-hat?

13. Statistical Significance • Our formal test of statistical significance asks whether we can be sure that a regression coefficient for the population differs from zero. • Just like in a difference in means/proportions test, the “standard error” is the standard deviation of the sample distribution. • If a coefficient is more than two standard errors away from zero, we can reject the null hypothesis (that it equals zero).

14. Statistical Significance • So, if a coefficient is more than twice the size of its standard error, we reject the null hypothesis with 95% confidence. • This works whether the coefficient is negative or positive. • The coefficient/standard error ratio is called the “test statistic” or “t-stat.” • A t-stat bigger than 2 or less than -2 indicates at statistically significant correlation.

15. Interpreting SPSS regression output: T-Stats

16. Multivariate Regressions • A “multivariate regression” uses more than one independent variable (or confound) to explain variation in a dependent variable. • The coefficient for each independent variable reports its effect on the DV, holding constant all of the other IVs in the regression. • Thought experiment: Comparing two colleges founded in the same year with the same student faculty ratio, what is the effect of SATs?

17. Multivariate Regressions Year of Founding SAT Scores Graduation Tuition Rates Student/Faculty Ratio

18. Multivariate Regressions • Again, want to estimate coefficients: Est. Grad. Rate = a + b1*SAT Score + b2*Year Founded+ b3*Tuition + b4*Faculty Ratio

19. Multivariate Regressions

20. Multivariate Regressions • Holding all other factors constant, a 200 point increase in SAT scores leads to a predicted (200)(0.042) = 8.4% increase in the graduation rate, and this effect is statistically significant. • Controlling for other factors, a college that is 100 years younger should have a graduation rate that is (100)(-0.021) = 2.1% lower, but this effect is not significantly different from zero.